Barren plateaus in quantum neural network training landscapes

Abstract

Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum-classical algorithms are popular for applications in quantum simulation, optimization, and machine learning. Due to its simplicity and hardware efficiency, random circuits are often proposed as initial guesses for exploring the space of quantum states. We show that the exponential dimension of Hilbert space and the gradient estimation complexity make this choice unsuitable for hybrid quantum-classical algorithms run on more than a few qubits. Specifically, we show that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits. We argue that this is related to the 2-design characteristic of random circuits, and that solutions to this problem must be studied.

References in corpus (36)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• A variational eigenvalue solver on a quantum processor
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• The theory of variational hybrid quantum-classical algorithms
• A Quantum Approximate Optimization Algorithm
• Characterizing Quantum Supremacy in Near-Term Devices
• Symmetric Informationally Complete Quantum Measurements
• The foundations of statistical mechanics from entanglement: Individual states vs. averages
• Scalable Quantum Simulation of Molecular Energies
• Towards Practical Quantum Variational Algorithms
• Exact and Approximate Unitary 2-Designs: Constructions and Applications
• Quantum optimization using variational algorithms on near-term quantum devices
• Hybrid Quantum-Classical Hierarchy for Mitigation of Decoherence and Determination of Excited States
• Quantum autoencoders for efficient compression of quantum data
• Chaos and complexity by design
• Robust determination of molecular spectra on a quantum processor
• Quantum Simulation of Electronic Structure with Linear Depth and Connectivity
• Cloud Quantum Computing of an Atomic Nucleus
• Quantum Approximate Optimization Algorithm for MaxCut: A Fermionic View
• Random Quantum Circuits are Approximate 2-designs
• Quantum Implementation of Unitary Coupled Cluster for Simulating Molecular Electronic Structure
• Most quantum states are too entangled to be useful as computational resources
• From transistor to trapped-ion computers for quantum chemistry
• Unsupervised Machine Learning on a Hybrid Quantum Computer
• Witnessing eigenstates for quantum simulation of Hamiltonian spectra
• Optimal Quantum Measurements of Expectation Values of Observables
• Application of fermionic marginal constraints to hybrid quantum algorithms
• Exploiting locality in quantum computation for quantum chemistry
• Are random pure states useful for quantum computation?
• Quantum Neuron: an elementary building block for machine learning on quantum computers
• Practical optimization for hybrid quantum-classical algorithms
• Quantum Algorithms for Fixed Qubit Architectures
• Training A Quantum Optimizer
• Products of Independent Gaussian Random Matrices

Cited by in corpus (1045)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Variational Quantum Algorithms
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum computational chemistry
• Quantum Chemistry in the Age of Quantum Computing
• Parameterized quantum circuits as machine learning models
• Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
• The Variational Quantum Eigensolver: a review of methods and best practices
• The power of quantum neural networks
• Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
• Programmable Quantum Simulations of Spin Systems with Trapped Ions
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Quantum algorithms for quantum chemistry and quantum materials science
• From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Power of data in quantum machine learning
• Efficient Learning for Deep Quantum Neural Networks
• Challenges and Opportunities in Quantum Machine Learning
• Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
• Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Hybrid quantum-classical algorithms and quantum error mitigation
• Generalization in quantum machine learning from few training data
• Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
• Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
• Quantum Natural Gradient
• Generalized Unitary Coupled Cluster Wavefunctions for Quantum Computation
• Training variational quantum algorithms is NP-hard
• qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor
• A Review on Quantum Approximate Optimization Algorithm and its Variants
• An initialization strategy for addressing barren plateaus in parametrized quantum circuits
• Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
• The randomized measurement toolbox
• Quantum-assisted quantum compiling
• Absence of Barren Plateaus in Quantum Convolutional Neural Networks
• Layerwise learning for quantum neural networks
• Robust data encodings for quantum classifiers
• Quantum Computation of Electronic Transitions using a Variational Quantum Eigensolver
• Information Scrambling in Computationally Complex Quantum Circuits
• Obstacles to State Preparation and Variational Optimization from Symmetry Protection
• Quantum computing for finance
• Error-mitigated digital quantum simulation
• The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size
• Efficient Symmetry-Preserving State Preparation Circuits for the Variational Quantum Eigensolver Algorithm
• Exploring entanglement and optimization within the Hamiltonian Variational Ansatz
• Effect of barren plateaus on gradient-free optimization
• Beyond Barren Plateaus: Quantum Variational Algorithms Are Swamped With Traps
• Diagnosing Barren Plateaus with Tools from Quantum Optimal Control
• Barren Plateaus in Variational Quantum Computing
• Training of Quantum Circuits on a Hybrid Quantum Computer
• Is quantum advantage the right goal for quantum machine learning?
• Quantum machine learning beyond kernel methods
• The prospects of quantum computing in computational molecular biology
• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• Structure optimization for parameterized quantum circuits
• Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
• Variational Quantum State Diagonalization
• Variational Quantum Linear Solver
• Solving nonlinear differential equations with differentiable quantum circuits
• Quantum circuit architecture search for variational quantum algorithms
• Exploiting symmetry in variational quantum machine learning
• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
• Emerging quantum computing algorithms for quantum chemistry
• Variational algorithms for linear algebra
• Trainability of Dissipative Perceptron-Based Quantum Neural Networks
• Supervised quantum machine learning models are kernel methods
• Theory of overparametrization in quantum neural networks
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Differentiable Quantum Architecture Search
• Group-Invariant Quantum Machine Learning
• Quantum Machine Learning for Chemistry and Physics
• Quantum annealing initialization of the quantum approximate optimization algorithm
• The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
• Estimating the gradient and higher-order derivatives on quantum hardware
• Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
• Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
• Reachability Deficits in Quantum Approximate Optimization
• Variational Quantum Eigensolver with Fewer Qubits
• Large gradients via correlation in random parameterized quantum circuits
• Training Quantum Embedding Kernels on Near-Term Quantum Computers
• Equivalence of quantum barren plateaus to cost concentration and narrow gorges
• A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
• Variational Quantum State Eigensolver
• Entanglement Devised Barren Plateau Mitigation
• A Non-Orthogonal Variational Quantum Eigensolver
• Towards simulating 2D effects in lattice gauge theories on a quantum computer
• Barren plateaus preclude learning scramblers
• Introduction to Haar Measure Tools in Quantum Information: A Beginner's Tutorial
• Qubit-excitation-based adaptive variational quantum eigensolver
• Capacity and quantum geometry of parametrized quantum circuits
• TensorCircuit: a Quantum Software Framework for the NISQ Era
• Higher Order Derivatives of Quantum Neural Networks with Barren Plateaus
• Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design
• Avoiding barren plateaus using classical shadows
• Error Mitigation by Symmetry Verification on a Variational Quantum Eigensolver
• Approximate unitary $t$-designs by short random quantum circuits using nearest-neighbor and long-range gates
• Decoding quantum errors with subspace expansions
• Efficient measure for the expressivity of variational quantum algorithms
• Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms
• Adaptive Variational Quantum Dynamics Simulations
• Quantum computing models for artificial neural networks
• An efficient quantum algorithm for the time evolution of parameterized circuits
• On barren plateaus and cost function locality in variational quantum algorithms
• Digitized-counterdiabatic quantum approximate optimization algorithm
• Hybrid quantum neural network for drug response prediction
• Neutral Atom Quantum Computing Hardware: Performance and End-User Perspective
• Scalable measures of magic resource for quantum computers
• Quantum Krylov subspace algorithms for ground and excited state energy estimation
• Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
• Expressibility of the alternating layered ansatz for quantum computation
• From pulses to circuits and back again: A quantum optimal control perspective on variational quantum algorithms
• Hybrid quantum-classical algorithms for approximate graph coloring
• Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms
• Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicator
• Stable Quantum-Correlated Many Body States through Engineered Dissipation
• Theory for Equivariant Quantum Neural Networks
• Quantum Machine Learning: from physics to software engineering
• A quantum-classical cloud platform optimized for variational hybrid algorithms
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule
• Recent advances for quantum classifiers
• Entanglement-Based Quantum Information Technology
• Variational Quantum Unsampling on a Quantum Photonic Processor
• Hardware Efficient Quantum Algorithms for Vibrational Structure Calculations
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• Quantum Computing for Molecular Biology
• Quantum Methods for Neural Networks and Application to Medical Image Classification
• Neural Predictor based Quantum Architecture Search
• Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
• Biology and medicine in the landscape of quantum advantages
• Adaptive Variational Quantum Imaginary Time Evolution Approach for Ground State Preparation
• The Variational Power of Quantum Circuit Tensor Networks
• ADAPT-VQE is insensitive to rough parameter landscapes and barren plateaus
• Real time evolution for ultracompact Hamiltonian eigenstates on quantum hardware
• Computer-inspired Quantum Experiments
• Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
• Universal discriminative quantum neural networks
• A case study of variational quantum algorithms for a job shop scheduling problem
• Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
• Variational Quantum Algorithm for Estimating the Quantum Fisher Information
• On the Universality of the Quantum Approximate Optimization Algorithm
• Classically optimized Hamiltonian simulation
• Quantum-optimal-control-inspired ansatz for variational quantum algorithms
• Enhancing Generative Models via Quantum Correlations
• Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
• Implementation of quantum imaginary-time evolution method on NISQ devices: Nonlocal approximation
• Quantum Assisted Simulator
• Minimizing State Preparations in Variational Quantum Eigensolver by Partitioning into Commuting Families
• Adversarial quantum circuit learning for pure state approximation
• Barren plateaus in quantum tensor network optimization
• Does provable absence of barren plateaus imply classical simulability?
• Towards quantum enhanced adversarial robustness in machine learning
• The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation
• Representation Learning via Quantum Neural Tangent Kernels
• Quantum computing of the ${}^6$Li nucleus via ordered unitary coupled clusters
• Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus
• Understanding quantum machine learning also requires rethinking generalization
• Exact and efficient Lanczos method on a quantum computer
• Correlation-Informed Permutation of Qubits for Reducing Ansatz Depth in VQE
• A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers
• Quantum Neural Network for Quantum Neural Computing
• Prospects of Quantum Computing for Molecular Sciences
• Quantum implementation of an artificial feed-forward neural network
• On the practical usefulness of the Hardware Efficient Ansatz
• Automatic design of quantum feature maps
• Reinforcement Learning assisted Quantum Optimization
• Digitized-Counterdiabatic Quantum Algorithm for Protein Folding
• Hamiltonian variational ansatz without barren plateaus
• Variational Quantum Singular Value Decomposition
• Towards adiabatic quantum computing using compressed quantum circuits
• Learning quantum data with the quantum Earth Mover's distance
• Subtleties in the trainability of quantum machine learning models
• Efficient evaluation of quantum observables using entangled measurements
• Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
• Variational Quantum Eigensolver with Reduced Circuit Complexity
• Avoiding barren plateaus via transferability of smooth solutions in Hamiltonian Variational Ansatz
• Variational Quantum Eigensolver for Frustrated Quantum Systems
• Scaling of variational quantum circuit depth for condensed matter systems
• The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ansätze
• Quantum Singular Value Decomposer
• Information Scrambling in Quantum Neural Networks
• Beyond the swap test: optimal estimation of quantum state overlap
• QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage
• Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer
• Classical-to-quantum convolutional neural network transfer learning
• Anomaly detection with variational quantum generative adversarial networks
• Quantum algorithm for ground state energy estimation using circuit depth with exponentially improved dependence on precision
• Demonstration of Adiabatic Variational Quantum Computing with a Superconducting Quantum Coprocessor
• Quantum Graph Neural Networks
• Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms
• Reduced Density Matrix Sampling: Self-consistent Embedding and Multiscale Electronic Structure on Current Generation Quantum Computers
• Characterizing the loss landscape of variational quantum circuits
• Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
• Quantum machine learning of large datasets using randomized measurements
• Measurement cost of metric-aware variational quantum algorithms
• Adaptive pruning-based optimization of parameterized quantum circuits
• Multilevel Combinatorial Optimization Across Quantum Architectures
• Generalization despite overfitting in quantum machine learning models
• Quantum inverse iteration algorithm for programmable quantum simulators
• Iterative Quantum Assisted Eigensolver
• Generative quantum machine learning via denoising diffusion probabilistic models
• Quantum classification of the MNIST dataset with Slow Feature Analysis
• Analytic gradients in variational quantum algorithms: Algebraic extensions of the parameter-shift rule to general unitary transformations
• Variational Hamiltonian Diagonalization for Dynamical Quantum Simulation
• Correlating AGP on a quantum computer
• Overlapped grouping measurement: A unified framework for measuring quantum states
• Variational quantum eigensolver for the Heisenberg antiferromagnet on the kagome lattice
• Simulating Hadronic Physics on NISQ devices using Basis Light-Front Quantization
• Markovian Quantum Neuroevolution for Machine Learning
• Quantum variational optimization: The role of entanglement and problem hardness
• The Presence and Absence of Barren Plateaus in Tensor-network Based Machine Learning
• Building spatial symmetries into parameterized quantum circuits for faster training
• Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze
• Quantum Language Model with Entanglement Embedding for Question Answering
• Variational Quantum Simulation for Periodic Materials
• Realizing a quantum generative adversarial network using a programmable superconducting processor
• Graph neural network initialisation of quantum approximate optimisation
• Noise-Resilient Variational Hybrid Quantum-Classical Optimization
• Tight bounds on the convergence of noisy random circuits to the uniform distribution
• Universal Effectiveness of High-Depth Circuits in Variational Eigenproblems
• Variational inference with a quantum computer
• Reflection Equivariant Quantum Neural Networks for Enhanced Image Classification
• Robust resource-efficient quantum variational ansatz through evolutionary algorithm
• TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit ansätze
• Abrupt Transitions in Variational Quantum Circuit Training
• Chemistry on quantum computers with virtual quantum subspace expansion
• A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
• Optimized Low-Depth Quantum Circuits for Molecular Electronic Structure using a Separable Pair Approximation
• Robust in Practice: Adversarial Attacks on Quantum Machine Learning
• Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
• Hybrid Classical-Quantum Autoencoder for Anomaly Detection
• Avoiding Barren Plateaus with Classical Deep Neural Networks
• Quantum optimization via four-body Rydberg gates
• Qubit-efficient encoding scheme for quantum simulations of electronic structure
• Finding the ground state of the Hubbard model by variational methods on a quantum computer with gate errors
• Quantum Embedding Search for Quantum Machine Learning
• Low depth mechanisms for quantum optimization
• Generative Quantum Learning of Joint Probability Distribution Functions
• Hybrid quantum ResNet for car classification and its hyperparameter optimization
• Progress toward favorable landscapes in quantum combinatorial optimization
• Solving hadron structures using the basis light-front quantization approach on quantum computers
• Variational quantum compiling with double Q-learning
• Natural Evolutionary Strategies for Variational Quantum Computation
• Detecting and quantifying entanglement on near-term quantum devices
• Variational learning for quantum artificial neural networks
• Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials
• Quantum approximate optimization via learning-based adaptive optimization
• Toward Trainability of Quantum Neural Networks
• Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
• Probabilistic Nonunitary Gate in Imaginary Time Evolution
• Qubit-efficient encoding schemes for binary optimisation problems
• Towards large-scale quantum optimization solvers with few qubits
• Expressivity of Quantum Neural Networks
• A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding
• Expressibility and trainability of parameterized analog quantum systems for machine learning applications
• Realization of quantum signal processing on a noisy quantum computer
• Gradient Ascent Pulse Engineering with Feedback
• Mitigating Barren Plateaus with Transfer-learning-inspired Parameter Initializations
• Predicting Gibbs-State Expectation Values with Pure Thermal Shadows
• Trainability barriers and opportunities in quantum generative modeling
• ResQNets: A Residual Approach for Mitigating Barren Plateaus in Quantum Neural Networks
• Quantum autoencoders with enhanced data encoding
• Quantum computing for chemistry and physics applications from a Monte Carlo perspective
• Gutzwiller Hybrid Quantum-Classical Computing Approach for Correlated Materials
• Symmetry enhanced variational quantum spin eigensolver
• Tensor networks for quantum machine learning
• Transitions in Entanglement Complexity in Random Circuits
• Artificial Intelligence for Quantum Computing
• Quantum Phase Recognition via Quantum Kernel Methods
• Quantum Architecture Search with Meta-learning
• Quantum algorithms for approximate function loading
• Provably Trainable Rotationally Equivariant Quantum Machine Learning
• Unifying and benchmarking state-of-the-art quantum error mitigation techniques
• Variational quantum process tomography
• Quantum Error Correction with Quantum Autoencoders
• Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community
• Variational Quantum Anomaly Detection: Unsupervised mapping of phase diagrams on a physical quantum computer
• Algebraic Bethe Circuits
• Sequential measurements for quantum-enhanced magnetometry in spin chain probes
• Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis
• Lyapunov control-inspired strategies for quantum combinatorial optimization
• High Dimensional Quantum Machine Learning With Small Quantum Computers
• Feedback-driven quantum reservoir computing for time-series analysis
• Optimal compression of quantum many-body time evolution operators into brickwall circuits
• Scrambling Ability of Quantum Neural Networks Architectures
• Long-time simulations with high fidelity on quantum hardware
• Quantum Transfer Learning for Real-World, Small, and High-Dimensional Datasets
• Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization
• Quantum computed moments correction to variational estimates
• Grover search revisited; application to image pattern matching
• Non-IID Quantum Federated Learning with One-shot Communication Complexity
• Variational Quantum Classifiers Through the Lens of the Hessian
• Variational quantum eigensolver techniques for simulating carbon monoxide oxidation
• Dynamical simulation via quantum machine learning with provable generalization
• Variational Quantum Evolution Equation Solver
• Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
• A variational quantum algorithm for the Feynman-Kac formula
• An Expressive Ansatz for Low-Depth Quantum Approximate Optimisation
• Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
• Optimal quantum control with poor statistics
• A semi-agnostic ansatz with variable structure for quantum machine learning
• Non-trivial symmetries in quantum landscapes and their resilience to quantum noise
• Interpretable Quantum Advantage in Neural Sequence Learning
• On Physics-Informed Neural Networks for Quantum Computers
• Unitary Selective Coupled-Cluster Method
• A Neural-Network Variational Quantum Algorithm for Many-Body Dynamics
• Accelerated quantum Monte Carlo with mitigated error on noisy quantum computer
• Variational Quantum Approximate Support Vector Machine with Inference Transfer
• Quantum algorithms with local particle number conservation: noise effects and error correction
• Preserving Symmetries for Variational Quantum Eigensolvers in the Presence of Noise
• Tree tensor network classifiers for machine learning: from quantum-inspired to quantum-assisted
• Exponential data encoding for quantum supervised learning
• Quantum Deep Hedging
• Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits
• Quantum reservoir computation utilising scale-free networks
• AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images
• Quantum Monge-Kantorovich problem and transport distance between density matrices
• Quantum Deep Reinforcement Learning for Robot Navigation Tasks
• Generative training of quantum Boltzmann machines with hidden units
• Policy Gradient based Quantum Approximate Optimization Algorithm
• Noisy intermediate-scale quantum algorithm for semidefinite programming
• Generalized Quantum Assisted Simulator
• Variational quantum algorithm with information sharing
• Fock State-enhanced Expressivity of Quantum Machine Learning Models
• Efficient classical algorithms for simulating symmetric quantum systems
• Predicting quantum dynamical cost landscapes with deep learning
• Variational quantum simulation of U(1) lattice gauge theories with qudit systems
• Quantum Computational Phase Transition in Combinatorial Problems
• Operator Sampling for Shot-frugal Optimization in Variational Algorithms
• Variational thermal quantum simulation of the lattice Schwinger model
• Hybrid quantum cycle generative adversarial network for small molecule generation
• Quantum Mixed State Compiling
• Nonlinear dynamics as a ground-state solution on quantum computers
• Pulse based Variational Quantum Optimal Control for hybrid quantum computing
• Quantum imaginary time evolution steered by reinforcement learning
• Quantum machine learning with adaptive linear optics
• Quantum Capsule Networks
• Entanglement entropy production in Quantum Neural Networks
• Noisy intermediate-scale quantum simulation of the one-dimensional wave equation
• On fundamental aspects of quantum extreme learning machines
• Uncovering Instabilities in Variational-Quantum Deep Q-Networks
• Variational waveguide QED simulators
• Entangled Datasets for Quantum Machine Learning
• Variational Quantum Time Evolution without the Quantum Geometric Tensor
• Quantum Gaussian Process Regression for Bayesian Optimization
• Challenges in the use of quantum computing hardware-efficient Ansatze in electronic structure theory
• Quantum algorithms from fluctuation theorems: Thermal-state preparation
• Hamiltonian operator approximation for energy measurement and ground state preparation
• Variational Quantum Linear Solver with Dynamic Ansatz
• Parallel Hybrid Networks: an interplay between quantum and classical neural networks
• Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model
• Fourier expansion in variational quantum algorithms
• Classification of dynamical Lie algebras for translation-invariant 2-local spin systems in one dimension
• Contextual Subspace Variational Quantum Eigensolver
• Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data
• Variational quantum one-class classifier
• Variational quantum simulation: a case study for understanding warm starts
• Crystalline Quantum Circuits
• Microwave signal processing using an analog quantum reservoir computer
• On the Sample Complexity of Quantum Boltzmann Machine Learning
• Training variational quantum circuits with CoVaR: covariance root finding with classical shadows
• Hierarchical quantum circuit representations for neural architecture search
• Analytical Framework for Quantum Alternating Operator Ansätze
• An application benchmark for fermionic quantum simulations
• Quantum State Discrimination Using Noisy Quantum Neural Networks
• Preparing Valence-Bond-Solid states on noisy intermediate-scale quantum computers
• Engineered dissipation to mitigate barren plateaus
• Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating Advanced Parameter Initialization Strategies
• 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
• Improving the variational quantum eigensolver using variational adiabatic quantum computing
• Approximating the quantum approximate optimization algorithm with digital-analog interactions
• Optimal training of variational quantum algorithms without barren plateaus
• Quantum-Assisted Solution Paths for the Capacitated Vehicle Routing Problem
• Effects of noise on the overparametrization of quantum neural networks
• Quantum-Selected Configuration Interaction: classical diagonalization of Hamiltonians in subspaces selected by quantum computers
• Efficient variational synthesis of quantum circuits with coherent multi-start optimization
• Variational quantum algorithms to estimate rank, quantum entropies, fidelity and Fisher information via purity minimization
• One-particle Green's functions from the quantum equation of motion algorithm
• A review of Quantum Neural Networks: Methods, Models, Dilemma
• Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits
• An evolving objective function for improved variational quantum optimisation
• Quantum Natural Language Processing
• Automatically Differentiable Quantum Circuit for Many-qubit State Preparation
• Reduction of finite sampling noise in quantum neural networks
• Quantum variational learning for quantum error-correcting codes
• Enhancing Detection of Topological Order by Local Error Correction
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
• Training variational quantum algorithms with random gate activation
• Evaluation of Parameterized Quantum Circuits with Cross-Resonance Pulse-Driven Entanglers
• Quantum Fourier analysis for multivariate functions and applications to a class of Schrödinger-type partial differential equations
• Efficient estimation of trainability for variational quantum circuits
• Preparation of ordered states in ultra-cold gases using Bayesian optimization
• Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection
• Neural network encoded variational quantum algorithms
• Tensor networks for interpretable and efficient quantum-inspired machine learning
• Entanglement Production and Convergence Properties of the Variational Quantum Eigensolver
• Resource Saving via Ensemble Techniques for Quantum Neural Networks
• A duplication-free quantum neural network for universal approximation
• A diagrammatic approach to variational quantum ansatz construction
• Molecular Quantum Circuit Design: A Graph-Based Approach
• Quantum simulations of Fermionic Hamiltonians with efficient encoding and ansatz schemes
• Sparse Quantum State Preparation for Strongly Correlated Systems
• AGP-based unitary coupled cluster theory for quantum computers
• Hamiltonian simulation-based quantum-selected configuration interaction for large-scale electronic structure calculations with a quantum computer
• Calibrating the role of entanglement in variational quantum circuits
• Finite-size criticality in fully connected spin models on superconducting quantum hardware
• Quantum Convolutional Neural Networks are Effectively Classically Simulable
• Schrödinger-Heisenberg Variational Quantum Algorithms
• Numerical evidence against advantage with quantum fidelity kernels on classical data
• A systematic variational approach to band theory in a quantum computer
• Lie-algebraic classical simulations for quantum computing
• Stochastic noise can be helpful for variational quantum algorithms
• A General Approach to Dropout in Quantum Neural Networks
• An introduction to variational quantum algorithms for combinatorial optimization problems
• Quantum approximate Bayesian computation for NMR model inference
• Variational Quantum Simulation of Partial Differential Equations: Applications in Colloidal Transport
• Classical Splitting of Parametrized Quantum Circuits
• Quantum computing quantum Monte Carlo with hybrid tensor network for electronic structure calculations
• Using Differential Evolution to avoid local minima in Variational Quantum Algorithms
• State Preparation Boosters for Early Fault-Tolerant Quantum Computation
• Quantum Dropout: On and Over the Hardness of Quantum Approximate Optimization Algorithm
• TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era
• Here comes the SU(N): multivariate quantum gates and gradients
• Efficient quantum imaginary time evolution by drifting real time evolution: an approach with low gate and measurement complexity
• Simulating strongly interacting Hubbard chains with the Variational Hamiltonian Ansatz on a quantum computer
• Approximate complex amplitude encoding algorithm and its application to data classification problems
• Chemically Aware Unitary Coupled Cluster with ab initio Calculations on System Model H1: A Refrigerant Chemicals Application
• Unsupervised Quantum Circuit Learning in High Energy Physics
• Approximate encoding of quantum states using shallow circuits
• Extending relax-and-round combinatorial optimization solvers with quantum correlations
• Greedy parameter optimization for diabatic quantum annealing
• Isometric tensor network optimization for extensive Hamiltonians is free of barren plateaus
• Calculating nonadiabatic couplings and Berry's phase by variational quantum eigensolvers
• Variational quantum algorithm for ergotropy estimation in quantum many-body batteries
• Networked Quantum Services
• Short Quantum Circuits in Reinforcement Learning Policies for the Vehicle Routing Problem
• Recommending Solution Paths for Solving Optimization Problems with Quantum Computing
• Spatial, spin, and charge symmetry projections for a Fermi-Hubbard model on a quantum computer
• Computing exact moments of local random quantum circuits via tensor networks
• Quantum Generative Adversarial Networks For Anomaly Detection In High Energy Physics
• Mitigating algorithmic errors in quantum optimization through energy extrapolation
• Extending the Variational Quantum Eigensolver to Finite Temperatures
• Quantum neural networks form Gaussian processes
• Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
• Post-Hartree-Fock method in Quantum Chemistry for Quantum Computer
• Estimating Quantum Mutual Information Through a Quantum Neural Network
• On the expressivity of embedding quantum kernels
• Fault-tolerant quantum algorithms for quantum molecular systems: A survey
• Variational Simulation of Schwinger's Hamiltonian with Polarisation Qubits
• An exponentially-growing family of universal quantum circuits
• The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs
• Local minima in quantum systems
• Quantum algorithms for scientific computing
• Symmetry breaking in geometric quantum machine learning in the presence of noise
• Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy
• The quantum cost function concentration dependency on the parametrization expressivity
• Quantum algorithms for the generalized eigenvalue problem
• GSQAS: Graph Self-supervised Quantum Architecture Search
• Solution of SAT Problems with the Adaptive-Bias Quantum Approximate Optimization Algorithm
• Variational dynamics as a ground-state problem on a quantum computer
• Atomic Quantum Technologies for Quantum Matter and Fundamental Physics Applications
• Connecting geometry and performance of two-qubit parameterized quantum circuits
• Quantum Optimization for Training Quantum Neural Networks
• Quantum Neural Estimation of Entropies
• Quantum Circuit AutoEncoder
• FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits
• Improved variational quantum eigensolver via quasi-dynamical evolution
• Discretized quantum adiabatic process for free fermions and comparison with the imaginary-time evolution
• A variational quantum algorithm for Hamiltonian diagonalization
• Variational Preparation of the Sachdev-Ye-Kitaev Thermofield Double
• Variational quantum solver employing the PDS energy functional
• Simulation of adiabatic quantum computing for molecular ground states
• Gutzwiller wave function on a digital quantum computer
• Avoiding local minima in Variational Quantum Algorithms with Neural Networks
• Quantum Energy Landscape and VQA Optimization
• Problem specific classical optimization of Hamiltonian simulation
• Synergetic quantum error mitigation by randomized compiling and zero-noise extrapolation for the variational quantum eigensolver
• Q${}^2$Chemistry: A quantum computation platform for quantum chemistry
• Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning
• Variational Quantum-Neural Hybrid Error Mitigation
• Barren plateaus from learning scramblers with local cost functions
• Conditional Born machine for Monte Carlo event generation
• Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis
• VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
• A Hyperparameter Study for Quantum Kernel Methods
• Replacing neural networks by optimal analytical predictors for the detection of phase transitions
• Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
• Quantum Gaussian filter for exploring ground-state properties
• Assessment of the variational quantum eigensolver: application to the Heisenberg model
• Multiobjective variational quantum optimization for constrained problems: an application to Cash Management
• Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems
• Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping
• Incremental Data-Uploading for Full-Quantum Classification
• Playing Atari with Hybrid Quantum-Classical Reinforcement Learning
• A stabilizer framework for Contextual Subspace VQE and the noncontextual projection ansatz
• Dynamical mean-field theory for the Hubbard-Holstein model on a quantum device
• Quantum Circuit Evolution on NISQ Devices
• Mixer-Phaser Ansätze for Quantum Optimization with Hard Constraints
• Diabatic Quantum Annealing for the Frustrated Ring Model
• Quantifying scrambling in quantum neural networks
• What can we learn from quantum convolutional neural networks?
• Natural parameterized quantum circuit
• Entanglement Diagnostics for Efficient Quantum Computation
• A blueprint for a Digital-Analog Variational Quantum Eigensolver using Rydberg atom arrays
• Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor
• Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
• How to enhance quantum generative adversarial learning of noisy information
• Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel
• Introducing Non-Linear Activations into Quantum Generative Models
• Dynamic-ADAPT-QAOA: An algorithm with shallow and noise-resilient circuits
• Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansatze for Hybrid Variational Quantum Computing
• Reducing the Resources Required by ADAPT-VQE Using Coupled Exchange Operators and Improved Subroutines
• Beyond unital noise in variational quantum algorithms: noise-induced barren plateaus and limit sets
• On exploring the potential of quantum auto-encoder for learning quantum systems
• EHA: Entanglement-variational Hardware-efficient Ansatz for Eigensolvers
• Drawing together control landscape and tomography principles
• Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval
• Neural quantum kernels: training quantum kernels with quantum neural networks
• Improving the speed of variational quantum algorithms for quantum error correction
• Randomized adaptive quantum state preparation
• Optimizing Variational Quantum Algorithms with qBang: Efficiently Interweaving Metric and Momentum to Navigate Flat Energy Landscapes
• A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
• Algorithmic Shadow Spectroscopy
• Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
• A Parameter Setting Heuristic for the Quantum Alternating Operator Ansatz
• Quantum-selected configuration interaction with time-evolved state
• VQE Method: A Short Survey and Recent Developments
• Analyzing variational quantum landscapes with information content
• Automatic quantum circuit encoding of a given arbitrary quantum state
• A general framework for active space embedding methods: applications in quantum computing
• Simulating scalar field theories on quantum computers with limited resources
• Quantum computing through the lens of control: A tutorial introduction
• A Benchmarking Study of Quantum Algorithms for Combinatorial Optimization
• Mitigated barren plateaus in the time-nonlocal optimization of analog quantum-algorithm protocols
• Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
• Sampling Error Analysis in Quantum Krylov Subspace Diagonalization
• Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
• Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
• Multi-Objective Optimization and Network Routing with Near-Term Quantum Computers
• A Quantum Computing Implementation of Nuclear-Electronic Orbital (NEO) Theory: Towards an Exact pre-Born-Oppenheimer Formulation of Molecular Quantum Systems
• Beyond MP2 initialization for unitary coupled cluster quantum circuits
• The role of data embedding in equivariant quantum convolutional neural networks
• Impact of the form of weighted networks on the quantum extreme reservoir computation
• Highly optimized quantum circuits synthesized via data-flow engines
• Grover-QAOA for 3-SAT: Quadratic Speedup, Fair-Sampling, and Parameter Clustering
• Constrained and Vanishing Expressivity of Quantum Fourier Models
• Improved approximation algorithms for bounded-degree local Hamiltonians
• Experimental Quantum Learning of a Spectral Decomposition
• Dissipative variational quantum algorithms for Gibbs state preparation
• Using (1 + 1)D Quantum Cellular Automata for Exploring Collective Effects in Large Scale Quantum Neural Networks
• Qoncord: A Multi-Device Job Scheduling Framework for Variational Quantum Algorithms
• Expressivity of Variational Quantum Machine Learning on the Boolean Cube
• Application of deep quantum neural networks to finance
• When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
• The battle of clean and dirty qubits in the era of partial error correction
• QCOR: A Language Extension Specification for the Heterogeneous Quantum-Classical Model of Computation
• Estimating the randomness of quantum circuit ensembles up to 50 qubits
• Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator
• Trainability Barriers in Low-Depth QAOA Landscapes
• From Ansätze to Z-gates: a NASA View of Quantum Computing
• Quantum Lazy Training
• Can shallow quantum circuits scramble local noise into global white noise?
• Noise-induced shallow circuits and absence of barren plateaus
• Quantum support vector data description for anomaly detection
• Quantum Sensor Network Algorithms for Transmitter Localization
• Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints
• Fermionic Simulators for Enhanced Scalability of Variational Quantum Simulation
• Recycling qubits in near-term quantum computers
• Measurement-induced entanglement phase transitions in variational quantum circuits
• Avoiding Barren Plateaus with Entanglement
• Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
• Solving an Industrially Relevant Quantum Chemistry Problem on Quantum Hardware
• Quantum variational learning for entanglement witnessing
• Self-Adaptive Physics-Informed Quantum Machine Learning for Solving Differential Equations
• Measurement-efficient quantum Krylov subspace diagonalisation
• A tensor network discriminator architecture for classification of quantum data on quantum computers
• Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery
• Coarse grained intermolecular interactions on quantum processors
• Variational Quantum Algorithms for Trace Distance and Fidelity Estimation
• Large-scale simulations of Floquet physics on near-term quantum computers
• Protocols for classically training quantum generative models on probability distributions
• Towards chemical accuracy with shallow quantum circuits: A Clifford-based Hamiltonian engineering approach
• Near-term Efficient Quantum Algorithms for Entanglement Analysis
• Convex Optimization for Nonequilibrium Steady States on a Hybrid Quantum Processor
• Fast-forwarding quantum simulation with real-time quantum Krylov subspace algorithms
• Efficient and Robust Parameter Optimization of the Unitary Coupled-Cluster Ansatz
• Resource frugal optimizer for quantum machine learning
• Quantum supremacy and quantum phase transitions
• Characterization of variational quantum algorithms using free fermions
• Layering and subpool exploration for adaptive Variational Quantum Eigensolvers: Reducing circuit depth, runtime, and susceptibility to noise
• Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
• Satellite image classification with neural quantum kernels
• Designs from Local Random Quantum Circuits with SU(d) Symmetry
• Laziness, Barren Plateau, and Noise in Machine Learning
• Bayesian Learning of Parameterised Quantum Circuits
• Faster variational quantum algorithms with quantum kernel-based surrogate models
• Symmetry-invariant quantum machine learning force fields
• Investigating the Effect of Noise on the Training Performance of Hybrid Quantum Neural Networks
• Enabling High Performance Debugging for Variational Quantum Algorithms using Compressed Sensing
• Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
• Hybrid Tree Tensor Networks for quantum simulation
• Quantum Eigenvector Continuation for Chemistry Applications
• Quantum subspace expansion in the presence of hardware noise
• Stochastic Reservoir Computers
• Emergence of noise-induced barren plateaus in arbitrary layered noise models
• A hybrid quantum algorithm to detect conical intersections
• QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
• Sequential optimal selection of a single-qubit gate and its relation to barren plateau in parameterized quantum circuits
• Energy-dependent barren plateau in bosonic variational quantum circuits
• F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
• Forecasting steam mass flow in power plants using the parallel hybrid network
• Quantum Data Compression and Quantum Cross Entropy
• Subsystem Information Capacity in Random Circuits and Hamiltonian Dynamics
• Quantum computing for classical problems: Variational Quantum Eigensolver for activated processes
• Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits
• Improved iterative quantum algorithm for ground-state preparation
• Image Classification with Rotation-Invariant Variational Quantum Circuits
• Generative model for learning quantum ensemble via optimal transport loss
• Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Prediction
• Barren plateaus are swamped with traps
• Quantum Machine Learning using Gaussian Processes with Performant Quantum Kernels
• Problem-Size Independent Angles for a Grover-Driven Quantum Approximate Optimization Algorithm
• Variational-quantum-eigensolver-inspired optimization for spin-chain work extraction
• Unitary Designs of Symmetric Local Random Circuits
• Exploring Quantum Average-Case Distances: proofs, properties, and examples
• Toward hybrid quantum simulations with qubits and qumodes on trapped-ion platforms
• Operational Quantum Average-Case Distances
• Improving the performance of quantum approximate optimization for preparing non-trivial quantum states without translational symmetry
• Variational Quantum Eigensolver Ansatz for the $J_1$-$J_2$-model
• Limitations of Hartree-Fock with quantum resources
• Training Quantum Boltzmann Machines with the $\beta$-Variational Quantum Eigensolver
• SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
• Workflow for practical quantum chemical calculations with quantum phase estimation algorithm: electronic ground and π-π* excited states of benzene and its derivatives†
• Distributed Quantum Neural Networks via Partitioned Features Encoding
• Equivalence of cost concentration and gradient vanishing for quantum circuits: An elementary proof in the Riemannian formulation
• Quantum Supervised Learning
• Ansatz-Independent Variational Quantum Classifier
• Quantum-enhanced neural networks in the neural tangent kernel framework
• Photovoltaic power forecasting using quantum machine learning
• Performance of Particle Tracking Using a Quantum Graph Neural Network
• Absence of barren plateaus and scaling of gradients in the energy optimization of isometric tensor network states
• Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments
• Stabilizer ground states for simulating quantum many-body physics: theory, algorithms, and applications
• Qudit-inspired optimization for graph coloring
• Quantum continual learning on a programmable superconducting processor
• Subspace Preserving Quantum Convolutional Neural Network Architectures
• Enhanced feature encoding and classification on distributed quantum hardware
• A quantum computing concept for 1-D elastic wave simulation with exponential speedup
• Quantum reservoir computing on random regular graphs
• Applicability of Measurement-based Quantum Computation towards Physically-driven Variational Quantum Eigensolver
• Efficient Strategies for Reducing Sampling Error in Quantum Krylov Subspace Diagonalization
• Beyond Quantum Annealing: Optimal control solutions to MaxCut problems
• An RNN-policy gradient approach for quantum architecture search
• Adversarial Robustness Guarantees for Quantum Classifiers
• Quantum Chaos and Circuit Parameter Optimization
• The dilemma of quantum neural networks
• Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
• Benchmarking Quantum Optimization for the Maximum-Cut Problem on a Superconducting Quantum Computer
• Variational quantum computing for quantum simulation: principles, implementations, and challenges
• Estimating Gibbs partition function with quantumClifford sampling
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits
• Adaptive shot allocation for fast convergence in variational quantum algorithms
• Convergence and Quantum Advantage of Trotterized MERA for Strongly-Correlated Systems
• Practicality of quantum adiabatic algorithm for chemistry applications
• Reduce&chop: Shallow circuits for deeper problems
• State preparation of lattice field theories using quantum optimal control
• Quantum kernel learning Model constructed with small data
• Double-bracket quantum algorithms for quantum imaginary-time evolution
• Exponential Qubit Reduction in Optimization for Financial Transaction Settlement
• Quantum algorithms for cooling: a simple case study
• On Optimizing Hyperparameters for Quantum Neural Networks
• Precision ground-state energy calculation for the water molecule on a superconducting quantum processor
• Local, Expressive, Quantum-Number-Preserving VQE Ansatze for Fermionic Systems
• Gradients and frequency profiles of quantum re-uploading models
• Quantum Kernel Machine Learning With Continuous Variables
• Analyzing the quantum approximate optimization algorithm: ansätze, symmetries, and Lie algebras
• Information flow in parameterized quantum circuits
• Near Term Algorithms for Linear Systems of Equations
• Parameter Setting Heuristics Make the Quantum Approximate Optimization Algorithm Suitable for the Early Fault-Tolerant Era
• Noise reduction using past causal cones in variational quantum algorithms
• The Inductive Bias of Quantum Kernels
• Benchmarking hybrid digitized-counterdiabatic quantum optimization
• Quantum computing for genomics: conceptual challenges and practical perspectives
• Quantum Dynamics Simulation of the Advection-Diffusion Equation
• Quantum neural compressive sensing for ghost imaging
• Quantum Natural Gradient optimizer on noisy platforms: QAOA as a case study
• Quantum Simulation of non-Abelian Lattice Gauge Theories: a variational approach to ${\mathbb{D}}_8$
• Simulating quantum circuits with arbitrary local noise using Pauli Propagation
• A Study on Quantum Graph Neural Networks Applied to Molecular Physics
• Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements
• Scalable circuit depth reduction in feedback-based quantum optimization with a quadratic approximation
• SHARC-VQE: Simplified Hamiltonian Approach with Refinement and Correction enabled Variational Quantum Eigensolver for Molecular Simulation
• Application of ZX-calculus to Quantum Architecture Search
• Characterizing randomness in parameterized quantum circuits through expressibility and average entanglement
• Bounds on the ground state energy of quantum $p$-spin Hamiltonians
• Maximising Quantum-Computing Expressive Power through Randomised Circuits
• Variational measurement-based quantum computation for generative modeling
• Speedy Contraction of ZX Diagrams with Triangles via Stabiliser Decompositions
• Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks
• Classical Ensembles of Single-Qubit Quantum Variational Circuits for Classification
• Sketching phase diagrams using low-depth variational quantum algorithms
• Quantum gradient evaluation through quantum non-demolition measurements
• Efficient and quantum-adaptive machine learning with fermion neural networks
• Time-Optimal Quantum Driving by Variational Circuit Learning
• Super-resolution of Green's functions on noisy quantum computers
• The Roles of Kerr nonlinearity in a Bosonic Quantum Neural Network
• Diagrammatic Analysis for Parameterized Quantum Circuits
• Information loss and run time from practical application of quantum data compression
• Optimisation-free Classification and Density Estimation with Quantum Circuits
• Toward Reliability in the NISQ Era: Robust Interval Guarantee for Quantum Measurements on Approximate States
• Optimization of the Variational Quantum Eigensolver for Quantum Chemistry Applications
• Fully-programmable universal quantum simulator with a one-dimensional quantum processor
• Roadmap for quantum simulation of the fractional quantum Hall effect
• Multidimensional quantum dynamics with explicitly correlated Gaussian wave packets using Rothe's method
• Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
• Machine-Learning Insights into the Entanglement-trainability Correlation of Parameterized Quantum Circuits
• Exploring Ground States of Fermi-Hubbard Model on Honeycomb Lattices with Counterdiabaticity
• Matrix product state ansatz for the variational quantum solution of the Heisenberg model on Kagome geometries
• Robust Quantum Reservoir Computing for Molecular Property Prediction
• Differentiable matrix product states for simulating variational quantum computational chemistry
• Accurate harmonic vibrational frequencies for diatomic molecules via quantum computing
• Using gradient-based algorithms to determine ground state energies on a quantum computer
• Scalability Challenges in Variational Quantum Optimization under Stochastic Noise
• Efficient quantum-enhanced classical simulation for patches of quantum landscapes
• Kernel-Function Based Quantum Algorithms for Finite Temperature Quantum Simulation
• Quantum techniques for eigenvalue problems
• Critical Limitations in Quantum-Selected Configuration Interaction Methods
• Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs
• Schrödinger as a Quantum Programmer: Estimating Entanglement via Steering
• Order $p$ quantum Wasserstein distances from couplings
• QSlack: A slack-variable approach for variational quantum semi-definite programming
• Classification and reconstruction for single-pixel imaging with classical and quantum neural networks
• Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
• Variational quantum eigensolver with embedded entanglement using a tensor-network ansatz
• All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
• Hermitian-preserving ansatz and variational open quantum eigensolver
• Predicting Expressibility of Parameterized Quantum Circuits using Graph Neural Network
• Molecular Properties from Quantum Krylov Subspace Diagonalization
• Quantum Kerr Learning
• Determining the N-representability of a reduced density matrix via unitary evolution and stochastic sampling
• Limitations of variational quantum algorithms: a quantum optimal transport approach
• Quantum computing quantum Monte Carlo algorithm
• Energy Landscape Plummeting in Variational Quantum Eigensolver: Subspace Optimization, Non-iterative Corrections and Generator-informed Initialization for Improved Quantum Efficiency
• Quantum Computing for Data Centric Engineering and Science
• Near-term quantum algorithm for computing molecular and materials properties based on recursive variational series methods
• Solving lattice gauge theories using the quantum Krylov algorithm and qubitization
• Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks
• Variational Optimization for Quantum Problems using Deep Generative Networks
• Implementing transferable annealing protocols for combinatorial optimisation on neutral atom quantum processors: a case study on smart-charging of electric vehicles
• Photonic variational quantum eigensolver using entanglement measurements
• Calculation of the moscovium ground-state energy by quantum algorithms
• Quantum Inception Score
• Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning
• Enhancing Variational Quantum Circuit Training: An Improved Neural Network Approach for Barren Plateau Mitigation
• Channel Attention for Quantum Convolutional Neural Networks
• Hamiltonian-based graph-state ansatz for variational quantum algorithms
• Multireference error mitigation for quantum computation of chemistry
• Emergent unitary designs for encoded qubits from coherent errors and syndrome measurements
• Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity
• Imaginary Hamiltonian variational ansatz for combinatorial optimization problems
• Image Classification by Throwing Quantum Kitchen Sinks at Tensor Networks
• Supervised Learning Guarantee for Quantum AdaBoost
• Quantum Extremal Learning
• High-fidelity dimer excitations using quantum hardware
• Subspace-Based Local Compilation of Variational Quantum Circuits for Large-Scale Quantum Many-Body Simulation
• Efficient Quantum Cooling Algorithm for Fermionic Systems
• Quantum subspace expansion approach for simulating dynamical response functions of Kitaev spin liquids
• Modeling Linear Inequality Constraints in Quadratic Binary Optimization for Variational Quantum Eigensolver
• Quantum supremacy with analog quantum processors for material science and machine learning
• On the similarity of bandwidth-tuned quantum kernels and classical kernels
• Learning from physics experiments, with quantum computers: Applications in muon spectroscopy
• Single-shot quantum machine learning
• Sketching the Best Approximate Quantum Compiling Problem
• Quantum Neural Networks: Concepts, Applications, and Challenges
• Exploiting subspace constraints and ab initio variational methods for quantum chemistry
• Deep variational quantum eigensolver for excited states and its application to quantum chemistry calculation of periodic materials
• Variational Quantum Simulation of Valence-Bond Solids
• Information compression via hidden subgroup quantum autoencoders
• Backpropagation scaling in parameterised quantum circuits
• Adaptive measurement strategy for noisy quantum amplitude estimation with variational quantum circuits
• Single entanglement connection architecture between multi-layer bipartite Hardware Efficient Ansatz
• Deep Circuit Compression for Quantum Dynamics via Tensor Networks
• QFCNN: Quantum Fourier Convolutional Neural Network
• Noise-aware variational eigensolvers: a dissipative route for lattice gauge theories
• Parallel-in-time quantum simulation via Page and Wootters quantum time
• Quantum Edge Detection
• Permutation-equivariant quantum convolutional neural networks
• Classical shadows meet quantum optimal mass transport
• From Heisenberg to Hubbard: An initial state for the shallow quantum simulation of correlated electrons
• Tensor-Programmable Quantum Circuits for Solving Differential Equations
• Estimating many properties of a quantum state via quantum reservoir processing
• Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images
• qlbm -- A Quantum Lattice Boltzmann Software Framework
• Lean classical-quantum hybrid neural network model for image classification
• Learning Equivariant Maps with Variational Quantum Circuits
• Double-bracket algorithm for quantum signal processing without post-selection
• Quantum computation of a quasiparticle band structure with the quantum-selected configuration interaction
• Quantum Machine Learning of Molecular Energies with Hybrid Quantum-Neural Wavefunction
• Exploring nontrivial topology at quantum criticality in a superconducting processor
• Certified Random Number Generation using Quantum Computers
• Estimating Eigenenergies from Quantum Dynamics: A Unified Noise-Resilient Measurement-Driven Approach
• Random Projection using Random Quantum Circuits
• Parent Hamiltonian as a benchmark problem for variational quantum eigensolvers
• Learning to learn with an evolutionary strategy applied to variational quantum algorithms
• Software-Hardware Co-Optimization for Computational Chemistry on Superconducting Quantum Processors
• Hacking Cryptographic Protocols with Advanced Variational Quantum Attacks
• Leveraging commuting groups for an efficient variational Hamiltonian ansatz
• Dual-VQE: A quantum algorithm to lower bound the ground-state energy
• Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms
• Trained quantum neural networks are Gaussian processes
• Learning Fourier series with parametrized quantum circuits
• Perturbative gadgets for gate-based quantum computing: Non-recursive constructions without subspace restrictions
• Data-Dependent Generalization Bounds for Parameterized Quantum Models Under Noise
• Towards solving the BCS Hamiltonian gap in Near-Term Quantum Computers
• Deep-Circuit QAOA
• Pitfalls when tackling the exponential concentration of parameterized quantum models
• Boson sampling enhanced quantum chemistry
• Universal compiling and (No-)Free-Lunch theorems for continuous variable quantum learning
• Architectures and random properties of symplectic quantum circuits
• Measurement-based quantum machine learning
• A Quantum Information Theoretic View On A Deep Quantum Neural Network
• Equilibrium Propagation: the Quantum and the Thermal Cases
• Optimized numerical gradient and Hessian estimation for variational quantum algorithms
• Probing many-body Bell correlation depth with superconducting qubits
• Variational Microcanonical Estimator
• Almost fault-tolerant quantum machine learning with drastic overhead reduction
• A Variational Quantum Algorithm For Approximating Convex Roofs
• A Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack
• Exact spectral gaps of random one-dimensional quantum circuits
• Survey on Computational Applications of Tensor Network Simulations
• Non-linear classification capability of quantum neural networks due to emergent quantum metastability
• Variational LOCC-assisted quantum circuits for long-range entangled states
• VQE-generated quantum circuit dataset for machine learning
• Quantum circuits for partial differential equations in Fourier space
• Stabilizer configuration interaction: Finding molecular subspaces with error detection properties
• Normalized Gradient Descent for Variational Quantum Algorithms
• Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
• The impacts of optimization algorithm and basis size on the accuracy and efficiency of variational quantum eigensolver
• Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
• Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
• Heuristic Time Complexity of NISQ Shortest-Vector-Problem Solvers
• Accelerating Decision Diagram-based Multi-node Quantum Simulation with Ring Communication and Automatic SWAP Insertion
• Quantum Active Learning
• Information scrambling in quantum walks: Discrete-time formulation of Krylov complexity
• Quantum Approximation Optimization Algorithm for the Trellis based Viterbi Decoding of Classical Error Correcting Codes
• Crosstalk-Based Parameterized Quantum Circuit Approximation
• Exploring new variational quantum circuit ansatzes for solving $SU(2)$ matrix models
• Predicting Open-Hole Laminates Failure Using Support Vector Machines With Classical and Quantum Kernels
• The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization
• Entanglement-informed Construction of Variational Quantum Circuits
• Expressivity of deterministic quantum computation with one qubit
• Environment-Mediated Long-Ranged Correlations in Many-Body System
• Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers
• Variational quantum algorithm based on Lagrange polynomial encoding to solve differential equations
• Fighting Exponentially Small Gaps by Counterdiabatic Driving
• Preparation Circuits for Matrix Product States by Classical Variational Disentanglement
• Evolved Quantum Boltzmann Machines
• Quantum Curriculum Learning
• Modified Conjugate Quantum Natural Gradient
• Variational quantum compiling for three-qubit gates design in quantum dots
• Quantum Simulation of Molecules without Fermionic Encoding of the Wave Function
• Quantitative convergence of trained quantum neural networks to a Gaussian process
• Ground state energy and magnetization curve of a frustrated magnetic system from real-time evolution on a digital quantum processor
• Revisiting semiconductor bulk hamiltonians using quantum computers
• Benchmarking the performance of a high-Q cavity qudit using random unitaries
• Quantum-annealing-inspired algorithms for multijet clustering
• Adaptive Circuit Learning of Born Machine: Towards Realization of Amplitude Embedding and Quantum Data Loading
• A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
• Scalable Quantum Computational Science: A Perspective from Block-Encodings and Polynomial Transformations
• The Accuracy vs. Sampling Overhead Trade-off in Quantum Error Mitigation Using Monte Carlo-Based Channel Inversion
• Tackling the Challenges of Adding Pulse-level Support to a Heterogeneous HPCQC Software Stack: MQSS Pulse
• Escaping Local Minima with Quantum Coherent Cooling
• "Proper" Shift Rules for Derivatives of Perturbed-Parametric Quantum Evolutions
• Expressibility, entangling power and quantum average causal effect for causally indefinite circuits
• An Efficient Gradient Sensitive Alternate Framework for VQE with Variable Ansatz
• Quantum Physics-Informed Neural Networks for Maxwell's Equations: Circuit Design, "Black Hole" Barren Plateaus Mitigation, and GPU Acceleration
• Quantum Generative Training Using Rényi Divergences
• A quantum tug of war between randomness and symmetries on homogeneous spaces
• Quantum Hopfield Model with Dilute Memories
• Optimal Particle-Conserved Linear Encoding for Practical Fermionic Simulation
• Barren plateaus are amplified by the dimension of qudits
• Tangent Space Excitation Ansatz for Quantum Circuits
• Adiabatic quantum computing with parameterized quantum circuits
• Policy Gradient Approach to Compilation of Variational Quantum Circuits
• Quantum-data-driven dynamical transition in quantum learning
• Unsupervised detection of decoupled subspaces: many-body scars and beyond
• Variational learning algorithms for quantum query complexity
• Quantum neural network autoencoder and classifier applied to an industrial case study
• An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States
• Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training
• Orbital Expansion Variational Quantum Eigensolver: Enabling Efficient Simulation of Molecules with Shallow Quantum Circuit
• Hybrid quantum gap estimation algorithm using a filtered time series
• Mitigating the measurement overhead of ADAPT-VQE with optimised informationally complete generalised measurements
• Excited state calculations using variational quantum eigensolver with spin-restricted ansätze and automatically-adjusted constraints
• State preparation and evolution in quantum computing: a perspective from Hamiltonian moments
• Making the cut: two methods for breaking down a quantum algorithm
• Feasibility of performing quantum chemistry calculations on quantum computers
• Robustness of optimized numerical estimation schemes for noisy variational quantum algorithms
• On character table of Clifford groups
• Gibbs state sampling via cluster expansions
• Dynamical transition in controllable quantum neural networks with large depth
• Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly
• Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures
• Batched Line Search Strategy for Navigating through Barren Plateaus in Quantum Circuit Training
• HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
• Quantum Machine Learning For Classical Data
• Performance analysis of a filtering variational quantum algorithm
• Exploiting many-body localization for scalable variational quantum simulation
• Benchmarking a heuristic Floquet adiabatic algorithm for the Max-Cut problem
• Fast-Forwarding with NISQ Processors without Feedback Loop
• Variational simulation of higher-spin systems on qubit-based quantum simulators
• Scaling of symmetry-restricted quantum circuits
• Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model
• Warm Start of Variational Quantum Algorithms for Quadratic Unconstrained Binary Optimization Problems
• OnionVQE Optimization Strategy for Ground State Preparation on NISQ Devices
• ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
• On the Hardness of Measuring Magic
• Resource-efficient quantum algorithm for linear systems of equations
• Accelerating Quantum Eigensolver Algorithms With Machine Learning
• Estimates of loss function concentration in noisy parametrized quantum circuits
• Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
• Learning the closest product state
• When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square
• Efficient Estimation and Sequential Optimization of Cost Functions in Variational Quantum Algorithms
• Efficient Online Quantum Circuit Learning with No Upfront Training
• Quantum natural gradient with thermal-state initialization
• Laplacian Eigenmaps with variational circuits: a quantum embedding of graph data
• A general approach to quantum integration of cross sections in high-energy physics
• Hybrid Quantum-Classical Eigensolver Without Variation or Parametric Gates
• Efficient trainability of linear optical modules in quantum optical neural networks
• Probabilistic imaginary-time evolution in state-vector-based and shot-based simulations and on quantum devices
• Consensus-based qubit configuration optimization for variational algorithms on neutral atom quantum systems
• Approximate Quadratization of High-Order Hamiltonians for Combinatorial Quantum Optimization
• Variational-toolbox-based separability detection of multiqubit states
• Quantum sequel of neural network training
• Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Neural-network-assisted Monte Carlo sampling trained by Quantum Approximate Optimization Algorithm
• Variational quantum algorithms with invariant probabilistic error cancellation on noisy quantum processors
• A variational quantum algorithm for entanglement quantification
• Optimisation-Free Recursive QAOA for the Binary Paint Shop Problem
• Generative flow-based warm start of the variational quantum eigensolver
• Beyond asymptotic reasoning: the practicalities of a quantum ground state projector based on the wall-Chebyshev expansion
• The Dual Role of Low-Weight Pauli Propagation: A Flawed Simulator but a Powerful Initializer for Variational Quantum Algorithms
• Signatures of quantum chaos and complexity in the Ising model on random graphs
• Optimal quantum reservoir learning in proximity to universality
• Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials
• Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
• Generalized Cross-Entropy Benchmarking for Random Circuits with Ergodicity
• Hamiltonian-reconstruction distance as a success metric for the Variational Quantum Eigensolver
• Direct Gradient Computation for Barren Plateaus in Parameterized Quantum Circuits
• Variational Quantum Subspace Construction via Symmetry-Preserving Cost Functions
• Trainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks
• Applying Quantum Autoencoders for Time Series Anomaly Detection
• Variational Quantum Algorithm for Solving the Liouvillian Gap
• Quantum imaginary time evolution and UD-MIS problem
• Optimizing Unitary Coupled Cluster Wave Functions on Quantum Hardware: Error Bound and Resource-Efficient Optimizer
• Natural gradient and parameter estimation for quantum Boltzmann machines
• On Assessing the Quantum Advantage for MaxCut Provided by Quantum Neural Network Ansätze
• TabularQGAN: A quantum generative model for tabular data synthesis
• Ancillary entangling Floquet kicks for accelerating quantum algorithms
• A variational quantum eigensolver tailored to multi-band tight-binding simulations of electronic structures
• Many-Fermion Simulation from the Contracted Quantum Eigensolver without Fermionic Encoding of the Wave Function
• Trainability issues in quantum policy gradients
• Fully Quantum Lattice Gas Automata Building Blocks for Computational Basis State Encodings
• Evaluating Ground State Energies of Chemical Systems with Low-Depth Quantum Circuits and High Accuracy
• Double-bracket quantum algorithms for high-fidelity ground state preparation
• Learning Variational Quantum Circuit Parameters with Classical Artificial Intelligence for Quantum Phase Transition Detection
• Clustering and enhanced classification using a hybrid quantum autoencoder
• From barren plateaus through fertile valleys: Conic extensions of parameterised quantum circuits
• Multivariate unbounded quantum regression via log-ratio probabilities mitigating barren plateaus
• Improving the trainability of VQE on NISQ computers for solving portfolio optimization using convex interpolation
• Quantum Algorithms for State Preparation and Data Classification based on Stabilizer Codes
• Hybrid Quantum-Classical Clustering for Preparing a Prior Distribution of Eigenspectrum
• Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
• Variational Quantum Algorithm Landscape Reconstruction by Low-Rank Tensor Completion
• Exploring Entanglement and Parameter Sensitivity in QAOA through Quantum Fisher Information
• A quantum eigenvalue solver based on tensor networks
• Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes
• Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms
• Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
• Quantum circuit evolutionary framework applied on set partitioning problem
• Fast suppression of classification error in variational quantum circuits
• Data-driven Quantum Dynamical Embedding Method for Long-term Prediction on Near-term Quantum Computers
• Inference of maximum parsimony phylogenetic trees with model-based classical and quantum methods
• A loop Quantum Approximate Optimization Algorithm with Hamiltonian updating
• Superior resilience to poisoning and amenability to unlearning in quantum machine learning
• Decoded Quantum Interferometry Under Noise
• Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning
• Quantum reservoir computing for predicting and characterizing chaotic maps
• Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
• A penalty-free quantum algorithm to find energy eigenstates
• Towards Compact Wavefunctions from Quantum-Selected Configuration Interaction
• Evidence for effectively constant shot complexity in the quantum approximate optimization algorithm without per-instance optimization
• Dynamic LOCC Circuits for Automated Entanglement Manipulation
• Slice-Wise Initial State Optimization to Improve Cost and Accuracy of the VQE on Lattice Models
• Quantum approximate optimization of finite-state bosonic systems
• Leveraging Analog Neutral Atom Quantum Computers for Diversified Pricing in Hybrid Column Generation Frameworks
• Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction
• Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
• Quantum feature-map learning with reduced resource overhead
• Adaptive time Compressed QITE (ACQ) and its geometrical interpretation
• Exploiting biased noise in variational quantum models
• Quantum Machine Learning with SQUID
• Transformer fault diagnosis using an efficient simulation-driven variational quantum classifier with domain-aware feature encoding
• Pseudo quantum advantages in perceptron storage capacity
• Performance Guarantees for Quantum Neural Estimation of Entropies
• Quantum algorithm for one-quasiparticle excitations in the thermodynamic limit via cluster-additive block diagonalization
• Moments of Quantum Channel Ensembles
• Towards Continuous-variable Quantum Neural Networks for Biomedical Imaging
• Exploring the performance of superposition of product states: from 1D to 3D quantum spin systems
• Noisy Monitored Quantum Circuits
• Quantum State Preparation with Resolution Refinement
• Feedback-Based Quantum Control for Safe and Synergistic Drug Combination Design
• Practical Quantum Reservoir Computing in Rydberg Atom Arrays
• Quantum Phase Recognition via Quantum Attention Mechanism
• Deterministic Ground State Preparation via Power-Cosine Filtering of Time Evolution Operators
• Quantum Computing Solution of the Bethe-Salpeter Equation for Relativistic Scalar Bound States via Tensor-Network VQE
• Active Sampling Sample-based Quantum Diagonalization from Finite-Shot Measurements
• Reflection-Based Adiabatic State Preparation
• QMC-Net: Data-Aware Quantum Representations for Remote Sensing Image Classification
• Medical Imaging Classification with Cold-Atom Reservoir Computing using Auto-Encoders and Surrogate-Driven Training
• Application of Deep Learning to Jet Charge Discrimination
• Velocity Verlet-based optimization for variational quantum eigensolvers
• Disentangling quantum autoencoder
• Variational decision diagrams for quantum-inspired machine learning applications
• Quantum generative classification with mixed states
• Resonant Coupling Parameter Estimation with Superconducting Qubits
• Optical Quantum Computing
• Expressive equivalence of classical and quantum restricted Boltzmann machines
• Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deployment
• Efficient learning and optimizing non-Gaussian correlated noise in digitally controlled qubit systems
• Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms
• Schmidt quantum compressor
• Interpolation-based coordinate descent method for parameterized quantum circuits
• Pulse engineering via projection of response functions
• Performance evaluation of variational quantum eigensolver and quantum dynamics algorithms on the advection-diffusion equation
• Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
• The informativeness of the gradient revisited
• Variational Quantum Generative Modeling by Sampling Expectation Values of Tunable Observables
• Direct entanglement ansatz learning (DEAL) with ZNE on error-prone superconducting qubits
• Learning complexity gradually in quantum machine learning models
• Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines
• Detecting underdetermination in parameterized quantum circuits