Quantum Machine Learning

Abstract

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement concrete quantum software that offers such advantages. Recent work has made clear that the hardware and software challenges are still considerable but has also opened paths towards solutions.

24 pages, 2 figures

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Cited by in corpus (1309)

• Quantum Computing in the NISQ era and beyond
• Variational Quantum Algorithms
• Supervised learning with quantum enhanced feature spaces
• Machine learning and the physical sciences
• Barren plateaus in quantum neural network training landscapes
• A Quantum Engineer's Guide to Superconducting Qubits
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum Convolutional Neural Networks
• Integrated Photonic Quantum Technologies
• Suppressing quantum errors by scaling a surface code logical qubit
• Circuit-centric quantum classifiers
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Efficient Learning for Deep Quantum Neural Networks
• Challenges and Opportunities in Quantum Machine Learning
• AI for Next Generation Computing: Emerging Trends and Future Directions
• Quantum computing for finance: overview and prospects
• Data re-uploading for a universal quantum classifier
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Quantum Error Mitigation
• Quantum generative adversarial learning
• Quantum Simulators: Architectures and Opportunities
• Quantum advantage in learning from experiments
• A rigorous and robust quantum speed-up in supervised machine learning
• Quantum machine learning: a classical perspective
• Quantum generative adversarial networks
• Quantum computing with neutral atoms
• Generalization in quantum machine learning from few training data
• Transfer learning in hybrid classical-quantum neural networks
• Quantum Entanglement in Neural Network States
• Continuous-variable quantum neural networks
• Superconducting Quantum Computing: A Review
• Wave Physics as an Analog Recurrent Neural Network
• Quantum Computing for Finance: State of the Art and Future Prospects
• Absence of Barren Plateaus in Quantum Convolutional Neural Networks
• Quantum simulation and computing with Rydberg-interacting qubits
• Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems
• The Expressive Power of Parameterized Quantum Circuits
• Hybrid Quantum-Classical Convolutional Neural Networks
• Quantum computing for energy systems optimization: Challenges and opportunities
• Quantum Risk Analysis
• Differentiable Learning of Quantum Circuit Born Machine
• Warm-starting quantum optimization
• Experimental Quantum Generative Adversarial Networks for Image Generation
• Simulating quantum many-body dynamics on a current digital quantum computer
• An Artificial Neuron Implemented on an Actual Quantum Processor
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Information-theoretic bounds on quantum advantage in machine learning
• Simulating Large Quantum Circuits on a Small Quantum Computer
• Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers
• Quantum Computer Systems for Scientific Discovery
• Variational quantum simulation of general processes
• Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
• Option Pricing using Quantum Computers
• Barren Plateaus in Variational Quantum Computing
• Single-photon quantum hardware: towards scalable photonic quantum technology with a quantum advantage
• Is quantum advantage the right goal for quantum machine learning?
• Neural-Network Quantum States, String-Bond States, and Chiral Topological States
• Quantum machine learning beyond kernel methods
• The prospects of quantum computing in computational molecular biology
• Quantum Communication Using Semiconductor Quantum Dots
• Solving nonlinear differential equations with differentiable quantum circuits
• Sequential minimal optimization for quantum-classical hybrid algorithms
• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
• Quantum-Inspired Support Vector Machine
• Quantum Hopfield neural network
• Variational algorithms for linear algebra
• Standard Model Physics and the Digital Quantum Revolution: Thoughts about the Interface
• Trainability of Dissipative Perceptron-Based Quantum Neural Networks
• A divide-and-conquer algorithm for quantum state preparation
• Semiconductor Quantum Computation
• Supervised quantum machine learning models are kernel methods
• Quantum State Preparation with Optimal Circuit Depth: Implementations and Applications
• Quantum reservoir processing
• Hardware-efficient quantum random access memory with hybrid quantum acoustic systems
• Theory of overparametrization in quantum neural networks
• Quantum machine learning for image classification
• Quantum Variational Autoencoder
• Quantum generative adversarial learning in a superconducting quantum circuit
• Quantum generative adversarial network for generating discrete distribution
• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Non-Gaussian quantum states of a multimode light field
• Group-Invariant Quantum Machine Learning
• Generalization in Quantum Machine Learning: a Quantum Information Perspective
• Experimental Machine Learning of Quantum States
• Quantum noise protects quantum classifiers against adversaries
• Quantum Machine Learning for Chemistry and Physics
• Learning Quantum Systems
• CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations
• Machine learning meets quantum physics
• Training Quantum Embedding Kernels on Near-Term Quantum Computers
• Equivalence of quantum barren plateaus to cost concentration and narrow gorges
• Circuit-Based Quantum Random Access Memory for Classical Data
• A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
• Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory
• Quantum machine learning for quantum anomaly detection
• Quantum Machine Learning for Electronic Structure Calculations
• Machine Learning for Quantum Matter
• Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions
• Quantum Adversarial Machine Learning
• The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors
• Quantum computers as universal quantum simulators: state-of-art and perspectives
• Barren plateaus preclude learning scramblers
• Overview and Comparison of Gate Level Quantum Software Platforms
• Quantum-inspired algorithms in practice
• Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey
• TensorCircuit: a Quantum Software Framework for the NISQ Era
• Complex Networks from Classical to Quantum
• Machine learning method for state preparation and gate synthesis on photonic quantum computers
• Noisy intermediate-scale quantum computers
• Drug design on quantum computers
• 8x8 Reconfigurable quantum photonic processor based on silicon nitride waveguides
• Crossing a topological phase transition with a quantum computer
• Efficient measure for the expressivity of variational quantum algorithms
• Fisher Information in Noisy Intermediate-Scale Quantum Applications
• Shortcuts to Adiabaticity in Digitized Adiabatic Quantum Computing
• Deep Reinforcement Learning for Quantum Gate Control
• Quantum computing models for artificial neural networks
• Realizing Quantum Convolutional Neural Networks on a Superconducting Quantum Processor to Recognize Quantum Phases
• Quantum Software Engineering: Landscapes and Horizons
• An efficient quantum algorithm for the time evolution of parameterized circuits
• Quantum federated learning through blind quantum computing
• Hybrid quantum neural network for drug response prediction
• Experimental quantum adversarial learning with programmable superconducting qubits
• Neutral Atom Quantum Computing Hardware: Performance and End-User Perspective
• Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
• Quantum neuromorphic computing
• Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
• Optimal Layout Synthesis for Quantum Computing
• Temporal Information Processing on Noisy Quantum Computers
• Experimental engineering of arbitrary qudit states with discrete-time quantum walks
• Advances of Machine Learning in Materials Science: Ideas and Techniques
• A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads
• Quantum Machine Learning in High Energy Physics
• Universal Variational Quantum Computation
• Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
• A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
• Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicator
• Quantum Neural Network States: A Brief Review of Methods and Applications
• Topological quantum phase transitions retrieved through unsupervised machine learning
• Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning
• Quantum Machine Learning: from physics to software engineering
• Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics
• Theory for Equivariant Quantum Neural Networks
• Topological Quantum Compiling with Reinforcement Learning
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• Towards understanding the power of quantum kernels in the NISQ era
• Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices
• Entanglement-Based Quantum Information Technology
• Time Series Quantum Reservoir Computing with Weak and Projective Measurements
• Unitary quantum perceptron as efficient universal approximator
• Quantum error correction for the toric code using deep reinforcement learning
• Neural Predictor based Quantum Architecture Search
• Learning and Inference on Generative Adversarial Quantum Circuits
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• Quantum Vision Transformers
• Quantum Computing for Molecular Biology
• Big Data: the End of the Scientific Method?
• Decentralized Base-Graph Routing for the Quantum Internet
• Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
• Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
• Scalable High-Performance Fluxonium Quantum Processor
• Biology and medicine in the landscape of quantum advantages
• The Quantum Wasserstein Distance of Order 1
• Quantum algorithm for non-homogeneous linear partial differential equations
• Ultra-High-Energy Cosmic Rays: The Intersection of the Cosmic and Energy Frontiers
• Basic protocols in quantum reinforcement learning with superconducting circuits
• Experimental Phase Estimation Enhanced By Machine Learning
• Pulse-efficient circuit transpilation for quantum applications on cross-resonance-based hardware
• Universal discriminative quantum neural networks
• Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
• Annealing by simulating the coherent Ising machine
• Quenches near Ising quantum criticality as a challenge for artificial neural networks
• Global optimization of quantum dynamics with AlphaZero deep exploration
• Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces
• Quantum Artificial Life in an IBM Quantum Computer
• Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
• A Quantum Algorithm for Solving Linear Differential Equations: Theory and Experiment
• Advances in silicon quantum photonics
• On the Quantum versus Classical Learnability of Discrete Distributions
• Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
• Natural quantum reservoir computing for temporal information processing
• Enhancing Generative Models via Quantum Correlations
• Quantum data compression by principal component analysis
• Classical variational simulation of the Quantum Approximate Optimization Algorithm
• Towards provably efficient quantum algorithms for large-scale machine-learning models
• An end-to-end trainable hybrid classical-quantum classifier
• Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning
• Vulnerability of quantum classification to adversarial perturbations
• Exponential Speedup of Quantum Annealing by Inhomogeneous Driving of the Transverse Field
• Clustering of conditional mutual information for quantum Gibbs states above a threshold temperature
• Efficient step-merged quantum imaginary time evolution algorithm for quantum chemistry
• Towards quantum enhanced adversarial robustness in machine learning
• Quantum K-nearest neighbor classification algorithm based on Hamming distance
• Does provable absence of barren plateaus imply classical simulability?
• Representation Learning via Quantum Neural Tangent Kernels
• Supervised learning with a quantum classifier using a multi-level system
• Understanding quantum machine learning also requires rethinking generalization
• A cross-disciplinary introduction to quantum annealing-based algorithms
• Entanglement-Gradient Routing for Quantum Networks
• Quantum Neural Network for Quantum Neural Computing
• A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers
• Pattern Learning Electronic Density of States
• Prospects of Quantum Computing for Molecular Sciences
• Quantum implementation of an artificial feed-forward neural network
• Out-of-distribution generalization for learning quantum dynamics
• Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
• NISQ Computers: A Path to Quantum Supremacy
• On the practical usefulness of the Hardware Efficient Ansatz
• Automatic design of quantum feature maps
• Machine Learning Bell Nonlocality in Quantum Many-body Systems
• Time-Sliced Quantum Circuit Partitioning for Modular Architectures
• Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
• Variational Quantum Singular Value Decomposition
• Machine Learning Phase Transitions with a Quantum Processor
• Hamiltonian variational ansatz without barren plateaus
• Hybrid quantum-classical reservoir computing of thermal convection flow
• Classification with Quantum Machine Learning: A Survey
• Subtleties in the trainability of quantum machine learning models
• Reconstruction of a Photonic Qubit State with Reinforcement Learning
• Shadows of quantum machine learning
• Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
• Learning temporal data with variational quantum recurrent neural network
• Variational Quantum Eigensolver for Frustrated Quantum Systems
• Resilience of quantum random access memory to generic noise
• Entanglement Classification via Neural Network Quantum States
• Measurement Error Mitigation in Quantum Computers Through Classical Bit-Flip Correction
• Information Scrambling in Quantum Neural Networks
• Efficient Representation of Topologically Ordered States with Restricted Boltzmann Machines
• Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models
• Energy dynamics, heat production and heat-work conversion with qubits: towards the development of quantum machines
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage
• Genome assembly using quantum and quantum-inspired annealing
• Benchmarking Adversarially Robust Quantum Machine Learning at Scale
• Improved thermal area law and quasi-linear time algorithm for quantum Gibbs states
• Reconstructing quantum states with quantum reservoir networks
• Quantum algorithms applied to satellite mission planning for Earth observation
• Machine Learning for Anomaly Detection in Particle Physics
• Geometry of learning neural quantum states
• Detecting quantum entanglement with unsupervised learning
• Opportunistic Entanglement Distribution for the Quantum Internet
• Machine Learning for Practical Quantum Error Mitigation
• Quantum Fast Poisson Solver: the algorithm and modular circuit design
• Bayesian Deep Learning on a Quantum Computer
• Measurement-based adaptation protocol with quantum reinforcement learning
• Variational Quantum Circuits for Quantum State Tomography
• Machine Learning meets Quantum Foundations: A Brief Survey
• The complexity of quantum support vector machines
• Quantum autoencoders via quantum adders with genetic algorithms
• On the statistical complexity of quantum circuits
• Hybrid quantum computation gate with trapped ion system
• Quantum Phase Estimation with Time-Frequency Qudits in a Single Photon
• A Deductive Verification Framework for Circuit-building Quantum Programs
• Quantum Circuit Cutting with Maximum Likelihood Tomography
• Quantum algorithm for visual tracking
• Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
• Quantum machine learning of large datasets using randomized measurements
• The theory of the quantum kernel-based binary classifier
• Analytic theory for the dynamics of wide quantum neural networks
• Generative model benchmarks for superconducting qubits
• Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting
• Quantum Cloud Computing: Trends and Challenges
• Quantum Memristors in Quantum Photonics
• Quantum Machine Learning for Digital Health? A Systematic Review
• Dimensional Expressivity Analysis of Parametric Quantum Circuits
• Quantum algorithm and quantum circuit for A-Optimal Projection: dimensionality reduction
• Data compression for quantum machine learning
• Realization of a quantum autoencoder for lossless compression of quantum data
• Generative quantum machine learning via denoising diffusion probabilistic models
• Demonstration of three- and four-body interactions between trapped-ion spins
• Quantum-enhanced data classification with a variational entangled sensor network
• Experimental quantum kernel machine learning with nuclear spins in a solid
• Experimental Demonstration of Non-local Controlled-Unitary Quantum Gates Using a Five-qubit Quantum Computer
• Training Optimization for Gate-Model Quantum Neural Networks
• A Variational Quantum Algorithm for Preparing Quantum Gibbs States
• Noise-Assisted Quantum Autoencoder
• Quantum enhancements for deep reinforcement learning in large spaces
• Qubit Mapping Based on Subgraph Isomorphism and Filtered Depth-Limited Search
• Benchmarking of Different Optimizers in the Variational Quantum Algorithms for Applications in Quantum Chemistry
• The Presence and Absence of Barren Plateaus in Tensor-network Based Machine Learning
• Hybrid quantum-classical machine learning for generative chemistry and drug design
• Generalized quantum circuit differentiation rules
• Quantum Neural Network Classifiers: A Tutorial
• Classical surrogates for quantum learning models
• Markovian Quantum Neuroevolution for Machine Learning
• Building spatial symmetries into parameterized quantum circuits for faster training
• Krotov Method for Optimal Control in Closed Quantum Systems
• Quantum Language Model with Entanglement Embedding for Question Answering
• Quantum phase detection generalisation from marginal quantum neural network models
• Quantum machine learning and quantum biomimetics: A perspective
• Realizing a quantum generative adversarial network using a programmable superconducting processor
• Benchmarking neural networks for quantum computation
• Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze
• Integrating Quantum Computing Resources into Scientific HPC Ecosystems
• Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification
• An improved quantum-inspired algorithm for linear regression
• Deep quantum neural networks equipped with backpropagation on a superconducting processor
• Matrix Product Operators for Sequence to Sequence Learning
• Quantum Computing: An Overview Across the System Stack
• Abrupt Transitions in Variational Quantum Circuit Training
• Circuit-based quantum random access memory for classical data with continuous amplitudes
• Generative Tensor Network Classification Model for Supervised Machine Learning
• Quantum Kernel Methods for Solving Differential Equations
• Pattern recognition techniques for Boson Sampling validation
• Non-Abelian symmetry can increase entanglement entropy
• Supervised Quantum Learning without Measurements
• Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification
• Equivalence Checking of Quantum Circuits with the ZX-Calculus
• Towards quantum advantage via topological data analysis
• Robust in Practice: Adversarial Attacks on Quantum Machine Learning
• Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
• Resonant Quantum Principal Component Analysis
• Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
• Style-based quantum generative adversarial networks for Monte Carlo events
• Quantum Clustering with k-Means: a Hybrid Approach
• Experimental quantum advantage with quantum coupon collector
• QASMBench: A Low-level QASM Benchmark Suite for NISQ Evaluation and Simulation
• Rapid thermalization of spin chain commuting Hamiltonians
• Quantum Anomaly Detection for Collider Physics
• Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
• Implementation of efficient quantum search algorithms on NISQ computers
• Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials
• Variational learning for quantum artificial neural networks
• Demonstration of Entanglement-Enhanced Covert Sensing
• Toward Trainability of Quantum Neural Networks
• Quantum processor-inspired machine learning in the biomedical sciences
• Divergence of predictive model output as indication of phase transitions
• Entanglement Availability Differentiation Service for the Quantum Internet
• Complex Quantum Networks: a Topical Review
• Machine learning \& artificial intelligence in the quantum domain
• A Survey of Quantum Learning Theory
• Quantum Kernel Methods under Scrutiny: A Benchmarking Study
• Variational Quantum Algorithms for Dimensionality Reduction and Classification
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Quantum speedup in the identification of cause-effect relations
• Predicting quantum advantage by quantum walk with convolutional neural networks
• Configurable sublinear circuits for quantum state preparation
• Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network
• Expressivity of Quantum Neural Networks
• Entanglement Access Control for the Quantum Internet
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• Practical distributed quantum information processing with LOCCNet
• A machine learning approach to dynamical properties of quantum many-body systems
• A Grover-search Based Quantum Learning Scheme for Classification
• Large-scale quantum approximate optimization on non-planar graphs with machine learning noise mitigation
• Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing
• Trainability barriers and opportunities in quantum generative modeling
• ResQNets: A Residual Approach for Mitigating Barren Plateaus in Quantum Neural Networks
• Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices
• Analyzing Prospects for Quantum Advantage in Topological Data Analysis
• Hybrid quantum-classical neural network for calculating ground state energies of molecules
• Symmetry enhanced variational quantum spin eigensolver
• Dissipation as a resource for Quantum Reservoir Computing
• Neural-network-designed pulse sequences for robust control of singlet-triplet qubits
• Robustness Verification of Quantum Classifiers
• Learning Nonlinear Input-Output Maps with Dissipative Quantum Systems
• Quantum pixel representations and compression for $N$-dimensional images
• Quantum algorithms for approximate function loading
• Provably Trainable Rotationally Equivariant Quantum Machine Learning
• Two-Unitary Decomposition Algorithm and Open Quantum System Simulation
• Setting up experimental Bell test with reinforcement learning
• Full-Stack, Real-System Quantum Computer Studies: Architectural Comparisons and Design Insights
• Quantum Error Correction with Quantum Autoencoders
• MQT QMAP: Efficient Quantum Circuit Mapping
• Variational Quantum Anomaly Detection: Unsupervised mapping of phase diagrams on a physical quantum computer
• Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community
• Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review
• A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation
• Quantum algorithms for anomaly detection using amplitude estimation
• Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis
• Quantum computed moments correction to variational estimates
• Feedback-driven quantum reservoir computing for time-series analysis
• Double sparse quantum state preparation
• Scrambling Ability of Quantum Neural Networks Architectures
• Scalable and High-Fidelity Quantum Random Access Memory in Spin-Photon Networks
• Overfitting in quantum machine learning and entangling dropout
• Optimal (controlled) quantum state preparation and improved unitary synthesis by quantum circuits with any number of ancillary qubits
• Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization
• Quantum Transfer Learning for Real-World, Small, and High-Dimensional Datasets
• Quantum-Noise-Driven Generative Diffusion Models
• Non-IID Quantum Federated Learning with One-shot Communication Complexity
• On the Interpretability of Quantum Neural Networks
• Quantum semi-supervised generative adversarial network for enhanced data classification
• Convex optimization of programmable quantum computers
• Predicting toxicity by quantum machine learning
• Optimization and learning of quantum programs
• Universal Adversarial Examples and Perturbations for Quantum Classifiers
• Programmable Quantum Annealers as Noisy Gibbs Samplers
• Quantum Machine Learning for $b$-jet charge identification
• Lower- and higher-order nonclassical properties of photon added and subtracted displaced Fock states
• Quantum Reinforcement Learning: the Maze problem
• Multilayer Optimization for the Quantum Internet
• New Trends in Quantum Machine Learning
• Quantum many-body systems in thermal equilibrium
• Dynamical simulation via quantum machine learning with provable generalization
• Multiqubit and multilevel quantum reinforcement learning with quantum technologies
• Tree tensor network classifiers for machine learning: from quantum-inspired to quantum-assisted
• Speeding-up the decision making of a learning agent using an ion trap quantum processor
• Unsupervised learning using topological data augmentation
• Interpretable Quantum Advantage in Neural Sequence Learning
• Variational Quantum Approximate Support Vector Machine with Inference Transfer
• A semi-agnostic ansatz with variable structure for quantum machine learning
• Quantum topology identification with deep neural networks and quantum walks
• Quantum Differentially Private Sparse Regression Learning
• Quantum Monge-Kantorovich problem and transport distance between density matrices
• Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers
• Generative training of quantum Boltzmann machines with hidden units
• Revealing quantum chaos with machine learning
• AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images
• Estimating the degree of non-Markovianity using machine learning
• Entanglement Accessibility Measures for the Quantum Internet
• Generative machine learning with tensor networks: benchmarks on near-term quantum computers
• Fock State-enhanced Expressivity of Quantum Machine Learning Models
• Quantum neural networks to simulate many-body quantum systems
• Dense Quantum Measurement Theory
• Efficient Machine Learning Representations of Surface Code with Boundaries, Defects, Domain Walls and Twists
• Minimum quantum run-time characterization and calibration via restless measurements with dynamic repetition rates
• Learning quantum dynamics with latent neural ODEs
• End-to-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics
• QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
• Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits
• Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs
• Adaptive Routing for Quantum Memory Failures in the Quantum Internet
• Multidimensional Fourier series with quantum circuits
• A brief introduction to quantum algorithms
• A single $T$-gate makes distribution learning hard
• Quantum Reinforcement Learning via Policy Iteration
• Probabilistic Modeling with Matrix Product States
• Quantum state preparation protocol for encoding classical data into the amplitudes of a quantum information processing register's wave function
• Extract the Degradation Information in Squeezed States with Machine Learning
• Entanglement entropy production in Quantum Neural Networks
• Conditional $\pi$-Phase Shift of Single-Photon-Level Pulses at Room Temperature
• Variational autoencoder reconstruction of complex many-body physics
• Quantum Phase Processing and its Applications in Estimating Phase and Entropies
• Low-rank quantum state preparation
• Quantum Gaussian Process Regression for Bayesian Optimization
• Tackling the challenge of a huge materials science search space with quantum-inspired annealing
• Entangled Datasets for Quantum Machine Learning
• Quantum Capsule Networks
• Practical application improvement to Quantum SVM: theory to practice
• Variational quantum algorithm for experimental photonic multiparameter estimation
• Combinatorial Optimization on Gate Model Quantum Computers: A Survey
• Quantum reservoir computing in finite dimensions
• Single-exposure absorption imaging of ultracold atoms using deep learning
• Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks
• Fault-tolerant quantum computation using large spin cat-codes
• Spatial Mode Correction of Single Photons using Machine Learning
• Single-qubit universal classifier implemented on an ion-trap quantum device
• Quantum computing model of an artificial neuron with continuously valued input data
• Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning
• A Simple Quantum Neural Net with a Periodic Activation Function
• Fourier expansion in variational quantum algorithms
• Learning correlated noise in a 39-qubit quantum processor
• Quantum Circuit Compiler for a Shuttling-Based Trapped-Ion Quantum Computer
• Quantum Enhanced Inference in Markov Logic Networks
• Nonlinear dynamics and quantum chaos of a family of kicked $p$-spin models
• Variational Quantum Linear Solver with Dynamic Ansatz
• Towards Quantum Simulation of Non-Markovian Open Quantum Dynamics: A Universal and Compact Theory
• Qubernetes: Towards a Unified Cloud-Native Execution Platform for Hybrid Classic-Quantum Computing
• Quantitative wave-particle duality relations from the density matrix properties
• Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning
• Sublinear quantum algorithms for training linear and kernel-based classifiers
• Continuous-variable quantum Gaussian process regression and quantum singular value decomposition of non-sparse low rank matrices
• Machine Learning and Quantum Devices
• Data centers with quantum random access memory and quantum networks
• Quantum Circuit Design for Objective Function Maximization in Gate-Model Quantum Computers
• Entanglement-guided architectures of machine learning by quantum tensor network
• Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines
• Microwave signal processing using an analog quantum reservoir computer
• Quantum kernels to learn the phases of quantum matter
• Analyzing the impact of time-correlated noise on zero-noise extrapolation
• Scalable effective temperature reduction for quantum annealers via nested quantum annealing correction
• Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating Advanced Parameter Initialization Strategies
• Leveraging Adiabatic Quantum Computation for Election Forecasting
• QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction
• Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning
• Retrieving information from a black hole using quantum machine learning
• Designing calibration and expressivity-efficient instruction sets for quantum computing
• Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions
• Noisy Quantum Kernel Machines
• Optimal training of variational quantum algorithms without barren plateaus
• Quantum and classical bounds for two-state overlaps
• Quantum Markovianity as a supervised learning task
• Variational Gibbs State Preparation on NISQ devices
• Artificial Neural Network Based Computation for Out-of-Time-Ordered Correlators
• Quantum random access memory via quantum walk
• Reinforcement learning for semi-autonomous approximate quantum eigensolver
• Fragmented imaginary-time evolution for early-stage quantum signal processors
• HAMMER: boosting fidelity of noisy Quantum circuits by exploiting Hamming behavior of erroneous outcomes
• Microwave-multiplexed qubit controller using adiabatic superconductor logic
• Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems
• Efficient Deterministic Preparation of Quantum States Using Decision Diagrams
• Reachability Deficits in Quantum Approximate Optimization of Graph Problems
• Trotter errors from dynamical structural instabilities of Floquet maps in quantum simulation
• Topology Adaption for the Quantum Internet
• Effects of noise on the overparametrization of quantum neural networks
• Quantum neural networks force fields generation
• A universal scheme for robust self-testing in the prepare-and-measure scenario
• Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode
• Experimental measurement of Hilbert-Schmidt distance between two-qubit states as means for speeding-up machine learning
• Probing nonclassicality in an optically-driven cavity with two atomic ensembles
• Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture
• Representation of binary classification trees with binary features by quantum circuits
• QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
• Quantum Memristors in Frequency-Entangled Optical Fields
• Tensor networks for interpretable and efficient quantum-inspired machine learning
• Quantum adiabatic machine learning with zooming
• Predicting excited states from ground state wavefunction by supervised quantum machine learning
• Resource Saving via Ensemble Techniques for Quantum Neural Networks
• Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metric
• Collective optimization for variational quantum eigensolvers
• Quantum Mathematics in Artificial Intelligence
• Information processing at the speed of light
• A duplication-free quantum neural network for universal approximation
• Quantum Algorithms to Matrix Multiplication
• Social laser model for the Bandwagon effect: generation of coherent information waves
• Numerical evidence against advantage with quantum fidelity kernels on classical data
• Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing
• Experimental realization of a quantum image classifier via tensor-network-based machine learning
• Single-preparation unsupervised quantum machine learning: concepts and applications
• Cyber-Physical Defense in the Quantum Era
• Quantum Convolutional Neural Networks are Effectively Classically Simulable
• Supervised learning for robust quantum control in composite-pulse systems
• Wave Matrix Lindbladization I: Quantum Programs for Simulating Markovian Dynamics
• Optimal universal learning machines for quantum state discrimination
• Quantum approximate Bayesian computation for NMR model inference
• Quantum AI simulator using a hybrid CPU-FPGA approach
• Demonstration of low-overhead quantum error correction codes
• Quantum Circuit Design Methodology for Multiple Linear Regression
• Quantum Computing: Vision and Challenges
• A General Approach to Dropout in Quantum Neural Networks
• Implementing quantum algorithms on temporal photonic cluster states
• Lie-algebraic classical simulations for quantum computing
• Compact quantum kernel-based binary classifier
• Quantum Transfer Learning with Adversarial Robustness for Classification of High-Resolution Image Datasets
• Dequantizing quantum machine learning models using tensor networks
• Solving the time-complexity problem and tuning the performance of quantum reservoir computing by artificial memory restriction
• Searching for anomalous quartic gauge couplings at muon colliders using principle component analysis
• Enhanced Quantum Synchronization via Quantum Machine Learning
• Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
• Pulse-efficient quantum machine learning
• Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices
• Harnessing Quantum Extreme Learning Machines for image classification
• Optimal quantum dataset for learning a unitary transformation
• Rapid training of quantum recurrent neural networks
• Entanglement-based quantum deep learning
• Quantum Gaussian process regression
• Permutation Invariant Encodings for Quantum Machine Learning with Point Cloud Data
• Quantum search on noisy intermediate-scale quantum devices
• Quantum forgery attacks on COPA,AES-COPA and marble authenticated encryption algorithms
• Approximate complex amplitude encoding algorithm and its application to data classification problems
• Speed-up Quantum Perceptron via Shortcuts to Adiabaticity
• Qurzon: A Prototype for a Divide and Conquer Based Quantum Compiler
• Transition Role of Entangled Data in Quantum Machine Learning
• Practical Verification of Quantum Properties in Quantum Approximate Optimization Runs
• Latency considerations for stochastic optimizers in variational quantum algorithms
• Recompilation-enhanced simulation of electron-phonon dynamics on IBM Quantum computers
• Circuit complexity of quantum access models for encoding classical data
• Data re-uploading with a single qudit
• Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series
• Topological-Graph Dependencies and Scaling Properties of a Heuristic Qubit-Assignment Algorithm
• Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
• Supervised Learning with Quantum Measurements
• Variational Simulation of Schwinger's Hamiltonian with Polarisation Qubits
• A super-polynomial quantum-classical separation for density modelling
• Protocol for implementing quantum nonparametric learning with trapped ions
• Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
• An exponentially-growing family of universal quantum circuits
• Quantum advantage for differential equation analysis
• Machine learning effective models for quantum systems
• Estimating Quantum Mutual Information Through a Quantum Neural Network
• Quantum associative memory with a single driven-dissipative nonlinear oscillator
• Quantum circuit synthesis using Householder transformations
• Universal quantum gates between nitrogen-vacancy centers in a levitated nanodiamond
• Networked Quantum Services
• Phase diagram of quantum generalized Potts-Hopfield neural networks
• Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features
• Quantum neural networks form Gaussian processes
• Predominant Aspects on Security for Quantum Machine Learning: Literature Review
• On the expressivity of embedding quantum kernels
• Quantum reinforcement learning in continuous action space
• Optimal Usage of Quantum Random Access Memory in Quantum Machine Learning
• Realizing quantum linear regression with auxiliary qumodes
• An open-source modular framework for quantum computing
• Certified variational quantum algorithms for eigenstate preparation
• Quantum Kernel Alignment with Stochastic Gradient Descent
• Atomic Quantum Technologies for Quantum Matter and Fundamental Physics Applications
• Quantum Neural Estimation of Entropies
• Spacetime-Efficient Low-Depth Quantum State Preparation with Applications
• ScQ cloud quantum computation for generating Greenberger-Horne-Zeilinger states of up to 10 qubits
• Quantum version of the k-NN classifier based on a quantum sorting algorithm
• Effects of quantum resources on the statistical complexity of quantum circuits
• Quantum algorithms for the generalized eigenvalue problem
• Quantum Optimal Transport
• Optimizing Counterdiabaticity by Variational Quantum Circuits
• Monotonicity of the quantum 2-Wasserstein distance
• Scalable Quantum Simulations of Scattering in Scalar Field Theory on 120 Qubits
• Quantum Bandits
• Tangent-Space Gradient Optimization of Tensor Network for Machine Learning
• Quantivine: A Visualization Approach for Large-scale Quantum Circuit Representation and Analysis
• Permutation-Invariant Quantum Codes for Deletion Errors
• Efficient algorithms for quantum information bottleneck
• The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs
• A hybrid classical-quantum approach to speed-up Q-learning
• Benefits of Open Quantum Systems for Quantum Machine Learning
• High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
• Engineering unsteerable quantum states with active feedback
• Quantum Jump Metrology
• Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers
• Barren plateaus from learning scramblers with local cost functions
• On the convergence of projective-simulation-based reinforcement learning in Markov decision processes
• Optimal joint cutting of two-qubit rotation gates
• QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity
• Variational quantum Gibbs state preparation with a truncated Taylor series
• Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations
• Conditional Born machine for Monte Carlo event generation
• Quantum spectral clustering algorithm for unsupervised learning
• Reinforcement Learning with Quantum Variational Circuits
• Qibolab: an open-source hybrid quantum operating system
• Quantum gradient descent algorithms for nonequilibrium steady states and linear algebraic systems
• Theory of Noise-Scaled Stability Bounds and Entanglement Rate Maximization in the Quantum Internet
• Quantum mean centering for block-encoding-based quantum algorithm
• Deep reinforcement learning for robust quantum optimization
• Statistical Complexity of Quantum Learning
• Large-scale quantum hybrid solution for linear systems of equations
• Multiobjective variational quantum optimization for constrained problems: an application to Cash Management
• Quantum Machine Learning Implementations: Proposals and Experiments
• Self-testing of binary Pauli measurements requiring neither entanglement nor any dimensional restriction
• Calibrating the Classical Hardness of the Quantum Approximate Optimization Algorithm
• Experimental implementation of quantum algorithm for association rules mining
• On the universality of $S_n$-equivariant $k$-body gates
• Optimization of the Memory Reset Rate of a Quantum Echo-State Network for Time Sequential Tasks
• Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification
• Quantum Gradient Algorithm for General Polynomials
• Playing Atari with Hybrid Quantum-Classical Reinforcement Learning
• Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
• Quantum Vision Transformers for Quark-Gluon Classification
• Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis
• Loop-Free Tensor Networks for High-Energy Physics
• Fast, high precision dynamics in quantum optimal control theory
• Variational determination of multi-qubit geometrical entanglement in NISQ computers
• Complexity-Theoretic Limitations on Quantum Algorithms for Topological Data Analysis
• Mitigating errors by quantum verification and post-selection
• Quantum Topological Data Analysis with Linear Depth and Exponential Speedup
• The role of entanglement for enhancing the efficiency of quantum kernels towards classification
• Classical Artificial Neural Network Training Using Quantum Walks as a Search Procedure
• Ground state search by local and sequential updates of neural network quantum states
• Variational Approach to Quantum State Tomography based on Maximal Entropy Formalism
• What can we learn from quantum convolutional neural networks?
• Quantifying scrambling in quantum neural networks
• Quantum Continual Learning Overcoming Catastrophic Forgetting
• Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
• Probabilistic design of optimal sequential decision-making algorithms in learning and control
• Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor
• Quantum kernels with squeezed-state encoding for machine learning
• Quantum Generative Models for Small Molecule Drug Discovery
• Quantum diffusion map for nonlinear dimensionality reduction
• Validating quantum-supremacy experiments with exact and fast tensor network contraction
• The Uncertainty Principle of Quantum Processes
• Control of the $ZZ$ coupling between Kerr-cat qubits via transmon couplers
• Simulation of a Variational Quantum Perceptron using Grover's Algorithm
• On the effects of biased quantum random numbers on the initialization of artificial neural networks
• How to enhance quantum generative adversarial learning of noisy information
• AI and Quantum Computing in Binary Photocatalytic Hydrogen Production
• Advancements and Challenges in Quantum Machine Learning for Medical Image Classification: A Comprehensive Review
• Neural quantum kernels: training quantum kernels with quantum neural networks
• Data-driven methods for diffusivity prediction in nuclear fuels
• Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
• Noise-Resistant Quantum State Compression Readout
• Quantum Algorithm for Unsupervised Anomaly Detection
• Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging
• Quantum Expectation-Maximization Algorithm
• Tensor-network study of quantum phase transition on Sierpiński fractal
• Quantum Gaussian process model of potential energy surface for a polyatomic molecule
• Neural networks for quantum state tomography with constrained measurements
• Mitigated barren plateaus in the time-nonlocal optimization of analog quantum-algorithm protocols
• Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons
• Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
• Quantum Expectation-Maximization for Gaussian Mixture Models
• Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
• Quantum computing through the lens of control: A tutorial introduction
• Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials
• Experimental demonstration of quantum learning speed-up with classical input data
• Variational Quanvolutional Neural Networks with enhanced image encoding
• Quantum process tomography with unknown single-preparation input states
• An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks
• A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks
• Supervised learning of few dirty bosons with variable particle number
• Inferring physical properties of symmetric states from the fewest copies
• Automated design of quantum optical experiments for device-independent quantum key distribution
• Automatic and effective discovery of quantum kernels
• Machine Learning-Assisted Manipulation and Readout of Molecular Spin Qubits
• Quantum-machine-learning channel discrimination
• Sampling and scrambling on a chain of superconducting qubits
• A quantum active learning algorithm for sampling against adversarial attacks
• Automatic quantum circuit encoding of a given arbitrary quantum state
• Quantum Dynamical Hamiltonian Monte Carlo
• Discriminating Quantum States with Quantum Machine Learning
• High-dimensional quantum encoding via photon-subtracted squeezed states
• Communication-efficient Quantum Algorithm for Distributed Machine Learning
• Demonstration of a bosonic quantum classifier with data re-uploading
• Estimating the randomness of quantum circuit ensembles up to 50 qubits
• Dissipative variational quantum algorithms for Gibbs state preparation
• QDNN: DNN with Quantum Neural Network Layers
• Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance
• Encoding classical data into a quantum computer
• State Stabilization for Gate-Model Quantum Computers
• Client-friendly continuous-variable blind and verifiable quantum computing
• Optimizing Gate Decomposition for High-Level Quantum Programming
• LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks
• The role of data embedding in equivariant quantum convolutional neural networks
• Exploring super-additivity of coherent information of noisy quantum channels through Genetic algorithms
• Qoncord: A Multi-Device Job Scheduling Framework for Variational Quantum Algorithms
• When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
• Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
• Building a Quantum Engineering Undergraduate Program
• The battle of clean and dirty qubits in the era of partial error correction
• Experimental Quantum Learning of a Spectral Decomposition
• Simulating a ring-like Hubbard system with a quantum computer
• Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner
• Machine learning applied to quantum synchronization-assisted probing
• Quantum Gram-Schmidt Processes and Their Application to Efficient State Read-out for Quantum Algorithms
• Opportunities and challenges of quantum computing for climate modelling
• Near-Term Quantum-Classical Associative Adversarial Networks
• Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
• Entanglement-induced provable and robust quantum learning advantages
• Kernel-based quantum regressor models learn non-Markovianity
• A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network
• Variational Quantum Algorithms for Trace Distance and Fidelity Estimation
• Optimal storage capacity of quantum Hopfield neural networks
• Quantum algorithms for SVD-based data representation and analysis
• A Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor
• Quantum algorithms for matrix operations and linear systems of equations
• A Continuous Variable Born Machine
• A quantum collisional classifier driven by information reservoir
• Quantum error reduction with deep neural network applied at the post-processing stage
• Can shallow quantum circuits scramble local noise into global white noise?
• Quantum-accessible reinforcement learning beyond strictly epochal environments
• Quantum Fourier Iterative Amplitude Estimation
• A tensor network discriminator architecture for classification of quantum data on quantum computers
• A Physical Quantum Agent
• Entanglement assisted training algorithm for supervised quantum classifiers
• Universal quantum computing with thermal state bosonic systems
• Direct implementation of a perceptron in superconducting circuit quantum hardware
• Adaptive projected variational quantum dynamics
• Small quantum computers and large classical data sets
• Quantum autoencoders for image classification
• Performance analysis of a hybrid agent for quantum-accessible reinforcement learning
• QEML (Quantum Enhanced Machine Learning): Using Quantum Computing to Enhance ML Classifiers and Feature Spaces
• Quantum accelerated approach to the thermal state of classical spin systems with applications to pattern-retrieval in the Hopfield neural network
• Asymptotically Optimal Circuit Depth for Quantum State Preparation and General Unitary Synthesis
• Quantum support vector data description for anomaly detection
• A Quantum-inspired Algorithm for General Minimum Conical Hull Problems
• Quantum Amplitude Estimation in the Presence of Noise
• Exploring Quantum Control Landscape and Solution Space Complexity through Dimensionality Reduction & Optimization Algorithms
• Jet Discrimination with Quantum Complete Graph Neural Network
• Parametric Probabilistic Quantum Memory
• Continuous-time dynamics and error scaling of noisy highly-entangling quantum circuits
• Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
• The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation
• Theoretical analysis of a nearly optimal analog quantum search
• Qudit Machine Learning
• Quantum-enhanced learning of rotations about an unknown direction
• Analog quantum variational embedding classifier
• Programmable adiabatic demagnetization for systems with trivial and topological excitations
• QGo: Scalable Quantum Circuit Optimization Using Automated Synthesis
• Quantum Semi-Supervised Kernel Learning
• Physical Realization of Measurement Based Quantum Computation
• Quantum DeepONet: Neural operators accelerated by quantum computing
• Resource-efficient simulation of noisy quantum circuits and application to network-enabled QRAM optimization
• Magnetic Phases of Spatially-Modulated Spin-1 Chains in Rydberg Excitons: Classical and Quantum Simulations
• Softening of Majorana edge states by long-range couplings
• Near-term Efficient Quantum Algorithms for Entanglement Analysis
• Measuring distances in Hilbert space by many-particle interference
• Quantum Walk to Train a Classical Artificial Neural Network
• Certificates of quantum many-body properties assisted by machine learning
• Energy-dependent barren plateau in bosonic variational quantum circuits
• Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
• Quantum Brain Networks: a Perspective
• Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
• Unified multivariate trace estimation and quantum error mitigation
• Satellite image classification with neural quantum kernels
• Resource frugal optimizer for quantum machine learning
• Faster variational quantum algorithms with quantum kernel-based surrogate models
• Laziness, Barren Plateau, and Noise in Machine Learning
• Classical and quantum machine learning applications in spintronics
• Two new results about quantum exact learning
• Guided Quantum Compression for High Dimensional Data Classification
• Scalable simulation of non-equilibrium quantum dynamics via classically optimised unitary circuits
• Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits
• Image Classification with Rotation-Invariant Variational Quantum Circuits
• Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Prediction
• Variational-quantum-eigensolver-inspired optimization for spin-chain work extraction
• Multi-variable integration with a variational quantum circuit
• Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases
• Empirical Sample Complexity of Neural Network Mixed State Reconstruction
• Phase-space negativity as a computational resource for quantum kernel methods
• Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
• Barren plateaus are swamped with traps
• Nonequilibrium fluctuations of a driven quantum heat engine via machine learning
• SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
• Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations
• Near-optimal quantum circuit construction via Cartan decomposition
• A didactic approach to quantum machine learning with a single qubit
• Evaluating the Resilience of Variational Quantum Algorithms to Leakage Noise
• Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers
• Quantum Reservoir Computing for Speckle-Disorder Potentials
• Representation of the Fermionic Boundary Operator
• Black-Box Quantum State Preparation with Inverse Coefficients
• Mitigating Quantum Errors via Truncated Neumann Series
• Tight Bound for Estimating Expectation Values from a System of Linear Equations
• Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery
• Robust quantum classifier with minimal overhead
• Graph kernels encoding features of all subgraphs by quantum superposition
• Deterministic Algorithms for Compiling Quantum Circuits with Recurrent Patterns
• Variational Quantum Support Vector Machine based on $\Gamma$ matrix expansion and Variational Universal-Quantum-State Generator
• Synergies Between Operations Research and Quantum Information Science
• MAQA: A Quantum Framework for Supervised Learning
• Universal quantum gates, artificial neurons and pattern recognition simulated by \textit{LC} resonators
• Neuromorphic computing with a single qudit
• Quantum Machine Learning using Gaussian Processes with Performant Quantum Kernels
• Statistical Limits of Supervised Quantum Learning
• Ion native variational ansatz for quantum approximate optimization
• Quantum continual learning on a programmable superconducting processor
• A Holistic Approach to Quantum Ethics Education
• Machine learning method to determine concentrations of structural defects in irradiated materials
• Variational quantum computing for quantum simulation: principles, implementations, and challenges
• Characterization of quantum states based on creation complexity
• Adiabatic entangling gate of Bose-Einstein condensates based on the minimum function
• Neuromorphic Wireless Split Computing with Multi-Level Spikes
• Photovoltaic power forecasting using quantum machine learning
• Two-level Quantum Walkers on Directed Graphs II: An Application to qRAM
• A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation
• Asymmetric quantum multicast network coding: asymmetric optimal cloning over quantum networks
• Designing ground states of Hopfield networks for quantum state preparation
• Fault-tolerant quantum speedup from constant depth quantum circuits
• Continuous-variable quantum kernel method on a programmable photonic quantum processor
• A quantum computing concept for 1-D elastic wave simulation with exponential speedup
• Implementing perceptron models with qubits
• Variational Quantum Algorithms for Differential Equations on a Noisy Quantum Computer
• Formal Methods for Quantum Programs: A Survey
• Searching for gluon quartic gauge couplings at muon colliders using the auto-encoder
• Adversarial Robustness Guarantees for Quantum Classifiers
• QRAM: A Survey and Critique
• Accelerating Quantum Algorithms with Precomputation
• Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans
• Quantum-inspired canonical correlation analysis for exponentially large dimensional data
• Expressive Quantum Supervised Machine Learning using Kerr-nonlinear Parametric Oscillators
• Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
• Digital-analog quantum learning on Rydberg atom arrays
• EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates
• SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture
• Quantum control landscape for generation of $H$ and $T$ gates in an open qubit with both coherent and environmental drive
• Distributed quantum machine learning via classical communication
• An Iterative Method to Improve the Precision of Quantum Phase Estimation Algorithm
• Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting
• Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays
• Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility
• Quantum reinforcement learning in the presence of thermal dissipation
• Quantum Supervised Learning
• Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact
• Quantum reservoir computing on random regular graphs
• Quantum autoencoders using mixed reference states
• EQC : Ensembled Quantum Computing for Variational Quantum Algorithms
• Entanglement Scaling in Quantum Advantage Benchmarks
• Swap Test with Quantum-Dot Charge Qubits
• Enhancing distributed sensing with imperfect error correction
• Dynamical uncertainty propagation with noisy quantum parameters
• Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
• Hardware-efficient entangled measurements for variational quantum algorithms
• Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
• A Universal Quantum Circuit Design for Periodical Functions
• Analyzing the quantum approximate optimization algorithm: ansätze, symmetries, and Lie algebras
• Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation
• Simulator Demonstration of Large Scale Variational Quantum Algorithm on HPC Cluster
• Prog-QAOA: Framework for resource-efficient quantum optimization through classical programs
• QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems
• Quantum Power Flows: From Theory to Practice
• Censorship of Quantum Resources in Quantum Networks
• Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance
• Quantum Machine Learning and its Supremacy in High Energy Physics
• Experimental Simulation of Hybrid Quantum Systems and Entanglement on a Quantum Computer
• Rademacher complexity of noisy quantum circuits
• Hardware-Efficient Quantum Random Access Memory Design with a Native Gate Set on Superconducting Platforms
• Quantum Information reveals that orbital-wise correlation is essentially classical in Natural Orbitals
• Quadratic unconstrained binary optimization formulation for rectified-linear-unit-type functions
• Generating Universal Adversarial Perturbations for Quantum Classifiers
• Learning Algebraic Models of Quantum Entanglement
• Nondestructive classification of quantum states using an algorithmic quantum computer
• Machine learning and behavioral economics for personalized choice architecture
• Single-photon-added coherent state based measurement transition and its advantages in precision measurement
• Quantum algorithm for logistic regression
• Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks
• Variational quantum simulation of long-range interacting systems
• Best-approximation error for parametric quantum circuits
• A Poisson Model for Entanglement Optimization in the Quantum Internet
• Quantum computing for genomics: conceptual challenges and practical perspectives
• Robust and efficient algorithms for high-dimensional black-box quantum optimization
• Quantum Communication Networks Enhanced by Distributed Quantum Memories
• Quantum density peak clustering
• Simulating thermodynamic properties of dinuclear metal complexes using Variational Quantum Algorithms
• Optimisation-free Classification and Density Estimation with Quantum Circuits
• The Roles of Kerr nonlinearity in a Bosonic Quantum Neural Network
• Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
• Geometry and symmetry in quantum Boltzmann machine
• Implementation and Empirical Evaluation of a Quantum Machine Learning Pipeline for Local Classification
• Generation of True Quantum Random Numbers with On-Demand Probability Distributions via Single-Photon Quantum Walks
• A Classically Efficient Quantum Scalable Fermi-Hubbard Benchmark
• A Study on Quantum Graph Neural Networks Applied to Molecular Physics
• Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows
• Maximising Quantum-Computing Expressive Power through Randomised Circuits
• QUBO.jl: A Julia Ecosystem for Quadratic Unconstrained Binary Optimization
• Routing in Quantum Networks with End-to-End Knowledge
• Coherence Fraction in Grover Search Algorithm
• Efficient and quantum-adaptive machine learning with fermion neural networks
• Assisted quantum simulation of open quantum systems
• Dimension reduction and redundancy removal through successive Schmidt decompositions
• Quantum neural compressive sensing for ghost imaging
• Analyzing and Unifying Robustness Measures for Excitation Transfer Control in Spin Networks
• Quantum random access memory with transmon-controlled phonon routing
• Lattice Hamiltonians and Stray Interactions Within Quantum Processors
• Teleporting two-qubit entanglement across 19 qubits on a superconducting quantum computer
• Quantum Kernel Evaluation via Hong-Ou-Mandel Interference
• Using the Parameterized Quantum Circuit combined with Variational-Quantum-Eigensolver (VQE) to create an Intelligent social workers' schedule problem solver
• Scalable Parameter Design for Superconducting Quantum Circuits with Graph Neural Networks
• A model of interacting quantum neurons with a dynamic synapse
• Framework for Learning and Control in the Classical and Quantum Domains
• Image Classification Based on Quantum KNN Algorithm
• Application of ZX-calculus to Quantum Architecture Search
• Large-scale Quantum Approximate Optimization via Divide-and-Conquer
• Non-Classical Kernels in Continuous Variable Systems
• Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks
• Quantum secure learning with classical samples
• Quantum annealing of a frustrated magnet
• Machine-Learning Insights into the Entanglement-trainability Correlation of Parameterized Quantum Circuits
• Can biological quantum networks solve NP-hard problems?
• Liouvillian skin effect in quantum neural networks
• Impact of Amplitude and Phase Damping Noise on Quantum Reinforcement Learning: Challenges and Opportunities
• Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks
• Material matters in superconducting qubits
• Alz-QNet: A Quantum Regression Network for Studying Alzheimer's Gene Interactions
• Universal adversarial perturbations for multiple classification tasks with quantum classifiers
• Quantum Kerr Learning
• Phase-Matching Quantum Key Distribution without Intensity Modulation
• Optimized Noise Suppression for Quantum Circuits
• Quantum Autoencoder for Multivariate Time Series Anomaly Detection
• Resource-efficient utilization of quantum computers
• Control landscapes for high-fidelity generation of C-NOT and C-PHASE gates with coherent and environmental driving
• Quantum delegated and federated learning via quantum homomorphic encryption
• Improved resource-tunable near-term quantum algorithms for transition probabilities, with applications in physics and variational quantum linear algebra
• Probing arbitrary polarized photon pairs undergoing double Compton scatterings by a dedicated MC simulator validated with experimental data
• Bifurcations, time-series analysis of observables, and network properties in a tripartite quantum system
• Quantum field theories, Markov random fields and machine learning
• Entanglement estimation of Werner states with a quantum extreme learning machine
• Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits
• Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
• Hybrid Quantum Neural Networks with Variational Quantum Regressor for Enhancing QSPR Modeling of CO2-Capturing Amine
• Learning Hidden Quantum Markov Models
• Symmetry-Based Quantum Circuit Mapping
• Quantum enhanced cross-validation for near-optimal neural networks architecture selection
• Spin-qubit noise spectroscopy from randomized benchmarking by supervised learning
• Approximating Hamiltonian dynamics with the Nyström method
• Quasidistribution of phases in Raman process with weak and strong pumps
• Quantum-enhanced long short-term memory with attention for spatial permeability prediction in oilfield reservoirs
• Quantum Semi-Supervised Learning with Quantum Supremacy
• Harnessing Quantum Support Vector Machines for Cross-Domain Classification of Quantum States
• Classification and reconstruction for single-pixel imaging with classical and quantum neural networks
• Universal noise-precision relations in variational quantum algorithms
• Mitigating Quantum Gate Errors for Variational Eigensolvers Using Hardware-Inspired Zero-Noise Extrapolation
• Robust Quantum Reservoir Computing for Molecular Property Prediction
• Experimentally detecting a quantum change point via Bayesian inference
• Improved Quantum Boosting
• High-dimensional counterdiabatic quantum computing
• Optically Controlled Entangling Gates in Randomly Doped Silicon
• AEQUAM: Accelerating Quantum Algorithm Validation through FPGA-Based Emulation
• Encoding-dependent generalization bounds for parametrized quantum circuits
• Entanglement Detection with Quantum-inspired Kernels and SVMs
• Quantum machine learning for multiclass classification beyond kernel methods
• Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
• Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing
• A quantum random access memory (QRAM) using a polynomial encoding of binary strings
• Contextual Quantum Neural Networks for Stock Price Prediction
• The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
• Quantum algorithms for calculating determinant and inverse of matrix and solving linear algebraic systems
• State Classification via a Random-Walk-Based Quantum Neural Network
• Restricting to the chip architecture maintains the quantum neural network accuracy
• Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity
• Information scrambling and entanglement in quantum approximate optimization algorithm circuits
• Trainable Discrete Feature Embeddings for Variational Quantum Classifier
• QFCNN: Quantum Fourier Convolutional Neural Network
• Experimental benchmarking of quantum state overlap estimation strategies with photonic systems
• Finding Quantum Critical Points with Neural-Network Quantum States
• Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer
• Multi-Valued Quantum Neurons
• Quantum-enhanced algorithms for classical target detection in complex environments
• Group theory on quantum Boltzmann machine
• Inferring Hidden Symmetries of Exotic Magnets from Detecting Explicit Order Parameters
• Supervised Learning Guarantee for Quantum AdaBoost
• Information compression via hidden subgroup quantum autoencoders
• Learning Simon's quantum algorithm
• On the complexity of finding the maximum entropy compatible quantum state
• Quantum Spectral Clustering through a Biased Phase Estimation Algorithm
• Permutation-equivariant quantum convolutional neural networks
• Maximal Information Leakage from Quantum Encoding of Classical Data
• Selective Noise Resistant Gate
• Computing scalar products via a two-terminal quantum transmission line
• Single entanglement connection architecture between multi-layer bipartite Hardware Efficient Ansatz
• Bayesian Quantum Circuit
• Machine learning classification of two-dimensional vortex configurations
• Parallel-in-time quantum simulation via Page and Wootters quantum time
• Ground state-based quantum feature maps
• Fault-tolerant quantum computing with the parity code and noise-biased qubits
• Quantum-Assisted Support Vector Regression
• On the similarity of bandwidth-tuned quantum kernels and classical kernels
• Quantum Inception Score
• Quadratic Quantum Speedup for Perceptron Training
• Quantum neural networks with multi-qubit potentials
• The State Preparation of Multivariate Normal Distributions using Tree Tensor Network
• Quantum Divide-and-Conquer Anchoring for Separable Non-negative Matrix Factorization
• Synergic quantum generative machine learning
• Quantum Walk Inspired Neural Networks for Graph-Structured Data
• Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction
• Approximate Equivalence Checking of Noisy Quantum Circuits
• Fourier Analysis of Variational Quantum Circuits for Supervised Learning
• Multidimensional Quantum Generative Modeling by Quantum Hartley Transform
• Quantum coherence of a long-lifetime exciton-polariton condensate
• Ultra-Fast All-Electrical Universal Nano-Qubits
• Lean classical-quantum hybrid neural network model for image classification
• ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks
• Efficient explicit circuit for quantum state preparation of piecewise continuous functions
• Family of Quantum Mutual Information in Multiparty Quantum Systems
• Real-time adaptive sensing of nuclear spins by a single-spin quantum sensor
• QHSL: a quantum hue, saturation, and lightness color model
• Born Rule and Logical Inference in Quantum Mechanics
• A Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack
• Quantum Computing Approach for Energy Optimization in a Prosumer Community
• Quantum Machine Learning Algorithm for Knowledge Graphs
• Quantum Iterative Methods for Solving Differential Equations with Application to Computational Fluid Dynamics
• Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images
• Adaptive Random Quantum Eigensolver
• Quantum and Classical Algorithms for Approximate Submodular Function Minimization
• Measurement-based interleaved randomised benchmarking using IBM processors
• Learning Fourier series with parametrized quantum circuits
• Quantum noise modeling through Reinforcement Learning
• Non-linear classification capability of quantum neural networks due to emergent quantum metastability
• Spin networks on adiabatic quantum computer
• Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing
• Machine-learning based high-bandwidth magnetic sensing
• Quantum Technology for Military Applications
• Einstein-Podolsky-Rosen steering paradox "2=1'' for $N$ qubits
• Dynamic quantum circuit compilation
• Adiabatic Quantum Computing for Binary Clustering
• Data-Dependent Generalization Bounds for Parameterized Quantum Models Under Noise
• Quantum memristor with vacuum--one-photon qubits
• Cross-platform hardware benchmark of style-based quantum GANs for data augmentation on superconducting and trapped-ion processors
• Learning of error statistics for the detection of quantum phases
• Experimental semi-autonomous eigensolver using reinforcement learning
• TILT: Achieving Higher Fidelity on a Trapped-Ion Linear-Tape Quantum Computing Architecture
• Unitary Evolutions Sourced By Interacting Quantum Memories: Closed Quantum Systems Directing Themselves Using Their State Histories
• Alternatives to a nonhomogeneous partial differential equation quantum algorithm
• Measurement-based quantum machine learning
• Introduction to Quantum Error Correction with Stabilizer Codes
• Estimating many properties of a quantum state via quantum reservoir processing
• Quantum Graph Convolutional Neural Networks
• VQE-generated quantum circuit dataset for machine learning
• Learning to Detect Entanglement
• Heisenberg-limited adaptive gradient estimation for multiple observables
• Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing
• Quantum-Enhanced Simulation-Based Optimization for Newsvendor Problems
• Quantum Feasibility Labeling for NP-complete Vertex Coloring Problem
• Distance-four quantum codes with combined postselection and error correction
• QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
• Almost fault-tolerant quantum machine learning with drastic overhead reduction
• Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data
• Bidirectional information flow quantum state tomography
• An Amplitude-Based Implementation of the Unit Step Function on a Quantum Computer
• Multiobjective Optimization in a Quantum Adiabatic Computer
• A Survey on Large-scale Machine Learning
• Experimentally undoing an unknown single-qubit unitary
• Quantum-Inspired Classical Algorithm for Slow Feature Analysis
• Quantum Annealing Formulation for Binary Neural Networks
• Implications of Deep Circuits in Improving Quality of Quantum Question Answering
• Tight Quantum Depth Lower Bound for Solving Systems of Linear Equations
• Control of Overfitting with Physics
• Quantum circuit complexity and unsupervised machine learning of topological order
• High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins
• Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI
• Regularizing quantum loss landscapes by noise injection
• Predicting fermionic densities using a Projected Quantum Kernel method
• The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization
• Typical Machine Learning Datasets as Low-Depth Quantum Circuits
• Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models
• Experimentally validated quantum-secure federated learning over a multi-user quantum network
• Evolved Quantum Boltzmann Machines
• QSteed: A Resource-Virtualized and Hardware-Aware Quantum Compilation Framework for Real Quantum Computing Processors
• Quantitative convergence of trained quantum neural networks to a Gaussian process
• MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption
• A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
• Nearly query-optimal classical shadow estimation of unitary channels
• Quantum-secure multiparty deep learning
• Quantum Curriculum Learning
• Variational quantum cloning machine on an integrated photonic interferometer
• Q-gen: A Parameterized Quantum Circuit Generator
• Optimal Qubit Mapping Search for Encoding Classical Data into Matrix Product State Representation with Minimal Loss
• Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor
• Quantum Active Learning
• Quantum Software Ecosystem Design
• Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
• Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
• Demonstration of quantum projective simulation on a single-photon-based quantum computer
• Quadratic speed-ups in quantum kernelized binary classification
• Quantum State Preparation for Probability Distributions with Reflection Symmetry Using Matrix Product States
• Chemically Motivated Simulation Problems are Efficiently Solvable by a Quantum Computer
• QRCC: Evaluating Large Quantum Circuits on Small Quantum Computers through Integrated Qubit Reuse and Circuit Cutting
• Adaptive measurement strategy for quantum subspace methods
• Mutual information maximizing quantum generative adversarial networks
• Active Learning in Physics: From 101, to Progress, and Perspective
• Determining probability density functions with adiabatic quantum computing
• Entanglement Universality of TGX States in Qubit-Qutrit Systems
• Boson sampling with ultracold atoms in a programmable optical lattice
• Quantum-enhanced cluster detection in physical images
• An Efficient Gradient Sensitive Alternate Framework for VQE with Variable Ansatz
• Quantum Anomaly Detection with a Spin Processor in Diamond
• Quantum Alphatron: quantum advantage for learning with kernels and noise
• Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning
• A Unified Framework for Quantum Supervised Learning
• Quantum Discriminator for Binary Classification
• Quantum Annealing for Semi-Supervised Learning
• Optimal representation of quantum channels
• Derivation of QUBO formulations for sparse estimation
• Recoverability from direct quantum correlations
• Deep Learning Super-Diffusion in Multiplex Networks
• Batched quantum state exponentiation and quantum Hebbian learning
• A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems
• Computability and Complexity of Unconventional Computing Devices
• Quantum Sparse Coding and Decoding Based on Quantum Network
• Quantum Boosting using Domain-Partitioning Hypotheses
• Graph Neural Network-Based Predictor for Optimal Quantum Hardware Selection
• On the Hardness of Measuring Magic
• Estimating the degree of non-Markovianity using variational quantum circuits
• A coherent approach to quantum-classical optimization
• A quantum machine learning classifier to search for new physics
• Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation
• Range dependent Hamiltonian Algorithm for numerical QUBO formulation
• When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square
• The quantum condition space
• Quantum neural network autoencoder and classifier applied to an industrial case study
• Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection
• Suppressing decoherence in quantum state transfer with unitary operations
• A Practical Quantum Hoare Logic with Classical Variables, I
• Quantum Noise Sensing by generating Fake Noise
• Efficiency of neural-network state representations of one-dimensional quantum spin systems
• Decoding Quantum Search Advantage: The Critical Role of State Properties in Random Walks
• Error mitigation of entangled states using brainbox quantum autoencoders
• Ensuring superior learning outcomes and data security for authorized learner
• Quantum Circuit Pruning: Improving Fidelity via Compilation-Aware Circuit Approximation
• Double descent in quantum kernel methods
• Variational Quantum Algorithms for Gibbs State Preparation
• Quantum Arnoldi and conjugate gradient iteration algorithm
• Quantum Machine Learning For Classical Data
• A hybrid machine-learning algorithm for designing quantum experiments
• Quantum natural gradient with thermal-state initialization
• Asymptotic bounds on quantum partial search algorithm and its applications to parallel search
• Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
• A Four-Qubits Code that is a Quantum Deletion Error-Correcting Code with the Optimal Length
• A Unitary Operator Construction Solution Based on Pauli Group for Maximal Dense Coding
• Estimation of mutual information via quantum kernel method
• Refined Criteria for QRAM Error Suppression via Efficient Large-Scale QRAM Simulator
• Harnessing Quantum Dynamics for Robust and Scalable Quantum Extreme Learning Machines
• Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach
• Gibbs state sampling via cluster expansions
• Learning Reduced Representations for Quantum Classifiers
• Single-Qudit Quantum Neural Networks for Multiclass Classification
• Two-level control over quantum state creation via entangled equal-probability state
• QWalkVec: Node Embedding by Quantum Walk
• Machine Learning Kernel Method from a Quantum Generative Model
• Sample-size-reduction of quantum states for the noisy linear problem
• Quantum Circuit Design for Decoded Quantum Interferometry
• Dynamical transition in controllable quantum neural networks with large depth
• Revisiting dequantization and quantum advantage in learning tasks
• Quantum approximated cloning-assisted density matrix exponentiation
• Quantum Circuit Unoptimization
• Ensembles of Quantum Classifiers
• HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
• Quantum sequel of neural network training
• Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Error Mitigation in Quantum Computers through Instruction Scheduling
• Data science and Machine learning in the Clouds: A Perspective for the Future
• Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks
• Variational quantum algorithms with invariant probabilistic error cancellation on noisy quantum processors
• Laplacian Eigenmaps with variational circuits: a quantum embedding of graph data
• Utilizing Quantum Processor for the Analysis of Strongly Correlated Materials
• Quantum Patch-Based Autoencoder for Anomaly Segmentation
• Bosonic Random Walk Networks for Graph Learning
• Error Crafting in Mixed Quantum Gate Synthesis
• Variational simulation of higher-spin systems on qubit-based quantum simulators
• More Practical and Adaptive Algorithms for Online Quantum State Learning
• High-Entanglement Capabilities for Variational Quantum Algorithms: The Poisson Equation Case
• On Sampling and Inference using Quantum Algorithms
• Scaling of symmetry-restricted quantum circuits
• Experimental kernel-based quantum machine learning in finite feature space
• Subsampling Factorization Machine Annealing
• Unified Architecture for Quantum Lookup Tables
• Multi-party quantum fingerprinting with weak coherent pulses: circuit design and protocol analysis
• Intelligent Trajectory Planning in UAV-mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective
• Practical Quantum Reservoir Computing in Rydberg Atom Arrays
• Quantum Phase Recognition via Quantum Attention Mechanism
• Auto Quantum Machine Learning for Multisource Classification
• Validation of an analyzability model for quantum software: a family of experiments
• Bosonic and fermionic mutual information of N-partite systems in dilaton black hole background
• Towards a quantum decision tree in a laser pumped four-level system
• Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool
• Diagonal-Budgeted Trotterization for Efficient Quantum Hamiltonian Simulation
• Machine Learning of Quantum Entanglement from Noisy Measurements
• Quantum Minimal Learning Machine: A Fidelity-Based Approach to Error Mitigation
• The Rise of Quantum Computing -- Take a BITE for Built Environment and Urban Microclimate Research
• POPQC: Parallel Optimization for Quantum Circuits (Extended Version)
• Quantum multi-row iteration algorithm for linear systems with non-square coefficient matrices
• Quantum relaxed row and column iteration methods based on block-encoding
• Quantum information transfer using weak measurements and any non-product resource state
• Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
• Towards a NISQ Algorithm to Simulate Hermitian Matrix Exponentiation
• EVA: a quantum Exponential Value Approximation algorithm
• Quantum Radon Transform and Its Application
• Fast suppression of classification error in variational quantum circuits
• An unsupervised feature learning for quantum-classical convolutional network with applications to fault detection
• A Quantum Convolutional Neural Network for Image Classification
• Fundamental Machine Learning Routines as Quantum Algorithms on a Superconducting Quantum Computer
• Feasible Architecture for Quantum Fully Convolutional Networks
• An investigation on the nonclassical and quantum phase properties of a family of engineered quantum states
• Quantum Spike Neural Network
• Quantum Algorithms in Cybernetics
• Eigen component analysis: A quantum theory incorporated machine learning technique to find linearly maximum separable components
• Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage
• A Data Driven Approach to Learning The Hamiltonian Matrix in Quantum Mechanics
• A Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse ansatz via Machine Learning and Cheminformatics
• Fast tests for probing the causal structure of quantum processes
• Quantum algorithmic differentiation
• Analog quantum simulation of non-Condon effects in molecular spectroscopy
• Quantum Algorithms for Prediction Based on Ridge Regression
• Quantum Machine Learning with SQUID
• Grover's Algorithm for Question Answering
• Quantum Perceptron Revisited: Computational-Statistical Tradeoffs
• Quantum-Inspired Computing: Can it be a Microscopic Computing Model of the Brain?
• Clustering and enhanced classification using a hybrid quantum autoencoder
• Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
• Neural Network Based Qubit Environment Characterization
• Quantum amplitude damping for solving homogeneous linear differential equations: A noninterferometric algorithm
• Quantum Model-Discovery
• Dimensional Expressivity Analysis, best-approximation errors, and automated design of parametric quantum circuits
• Information-Theoretic Limits of Quantum Learning via Data Compression
• Circuit connectivity boosts by quantum-classical-quantum interfaces
• Multi-Armed Bandits and Quantum Channel Oracles
• Enhanced local addressability of a spin array with local exchange pulses and global microwave driving
• Does Quantum Mechanics Breed Larger, More Intricate Quantum Theories? The Case for Experience-Centric Quantum Theory and the Interactome of Quantum Theories
• Optimal Quantum Subtracting Machine
• Quantum Advantage via Efficient Post-processing on Qudit Classical Shadow tomography
• Verifiable cloud-based variational quantum algorithms
• Unitary-transformed projective squeezing: applications for circuit-knitting and state-preparation of non-Gaussian states
• Information geometry of bosonic Gaussian thermal states
• EQNN: Enhanced Quantum Neural Network
• Quantum-Inspired Weight-Constrained Neural Network: Reducing Variable Numbers by 100x Compared to Standard Neural Networks
• Driven tabu search: a quantum inherent optimisation
• Nonlinear path-following via the asymptotic numerical method on a quantum processor
• Quantum community detection via deterministic elimination
• Mean-field limit from general mixtures of experts to quantum neural networks
• Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms
• Comment on arXiv:2307.08384 "Efficient Quantum State Preparation with Walsh Series"
• Optical Quantum Computing
• Logical operations with a dynamical qubit in Floquet-Bacon-Shor code
• Quantum Chebyshev Probabilistic Models for Fragmentation Functions
• Quantum entanglement response to pulsed gate modulation
• On Quantum Perceptron Learning via Quantum Search
• Measuring Incompatible Observables with Quantum Neural Networks
• Quantum phase classification via partial tomography-based quantum hypothesis testing
• Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines
• Quantum Algorithms for Causal Estimands
• Comprehensive Study on Heisenberg-limited Quantum Algorithms for Multiple Observables Estimation
• Physical-Layer Machine Learning with Multimode Interferometric Photon Counting
• High-expressibility Quantum Neural Networks using only classical resources
• Comparison of spin-qubit architectures for quantum error-correcting codes
• Scalable Simulation of Quantum Many-Body Dynamics with Or-Represented Quantum Algebra
• Quantum-assisted tracer dispersion in turbulent shear flow
• Multivariate unbounded quantum regression via log-ratio probabilities mitigating barren plateaus
• Vortex Detection from Quantum Data
• Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes
• Extended parameter shift rules with minimal derivative variance for parameterized quantum circuits
• Multiclass Portfolio Optimization via Variational Quantum Eigensolver with Dicke State Ansatz
• Efficient classical computation of the neural tangent kernel of quantum neural networks
• Superior resilience to poisoning and amenability to unlearning in quantum machine learning
• Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning
• Optimizing Compilation for Distributed Quantum Computing via Clustering and Annealing
• Power and limitations of distributed quantum state purification
• Modeling and benchmarking quantum optical neurons for efficient neural computation
• Quantum spatial best-arm identification via quantum walks
• Counterfactual quantum measurements
• Quantum feature-map learning with reduced resource overhead
• IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
• Pseudo quantum advantages in perceptron storage capacity
• Performance Guarantees for Quantum Neural Estimation of Entropies
• Exploring fixed points and eigenstates of quantum systems with reinforcement learning
• Information Processing in Quantum Thermodynamic Systems: an Autonomous Hamiltonian Approach
• Implementation of a Hamming-Distance-Like Genomic Quantum Classifier Using Inner Products on IBMQX4 and IBMQX16
• One-step preparation of 3D Bell and 3D GHZ states with Rydberg atoms
• Applying Grover-mixer quantum alternating operator ansatz algorithm to higher-order unconstrained binary optimization problems
• Fast collisional $\sqrt{\mathrm{SWAP}}$ gate for fermionic atoms in an optical superlattice
• First-Principles Optical Descriptors and Hybrid Classical-Quantum Classification of Er-Doped CaF${}_2$