Circuit-centric quantum classifiers

Abstract

The current generation of quantum computing technologies call for quantum algorithms that require a limited number of qubits and quantum gates, and which are robust against errors. A suitable design approach are variational circuits where the parameters of gates are learnt, an approach that is particularly fruitful for applications in machine learning. In this paper, we propose a low-depth variational quantum algorithm for supervised learning. The input feature vectors are encoded into the amplitudes of a quantum system, and a quantum circuit of parametrised single and two-qubit gates together with a single-qubit measurement is used to classify the inputs. This circuit architecture ensures that the number of learnable parameters is poly-logarithmic in the input dimension. We propose a quantum-classical training scheme where the analytical gradients of the model can be estimated by running several slightly adapted versions of the variational circuit. We show with simulations that the circuit-centric quantum classifier performs well on standard classical benchmark datasets while requiring dramatically fewer parameters than other methods. We also evaluate sensitivity of the classification to state preparation and parameter noise, introduce a quantum version of dropout regularisation and provide a graphical representation of quantum gates as highly symmetric linear layers of a neural network.

17 pages, 9 Figures, 5 Tables

References in corpus (34)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• A variational eigenvalue solver on a quantum processor
• Elementary gates for quantum computation
• Supervised learning with quantum enhanced feature spaces
• The theory of variational hybrid quantum-classical algorithms
• Quantum support vector machine for big data classification
• A Quantum Approximate Optimization Algorithm
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Characterizing Quantum Supremacy in Near-Term Devices
• Evaluating analytic gradients on quantum hardware
• Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
• Quantum Computational Supremacy
• Quantum Data Fitting
• Quantum autoencoders for efficient compression of quantum data
• Quantum Entanglement in Neural Network States
• Quantum Circuits for General Multiqubit Gates
• Quantum-state preparation with universal gate decompositions
• Boson Sampling for Molecular Vibronic Spectra
• Classical simulation of commuting quantum computations implies collapse of the polynomial hierarchy
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Creating superpositions that correspond to efficiently integrable probability distributions
• Efficient decomposition of quantum gates
• Automated optimization of large quantum circuits with continuous parameters
• Unitary Evolution Recurrent Neural Networks
• Quantum Circuits for Isometries
• Approximation by Quantum Circuits
• Asymptotically Optimal Topological Quantum Compiling
• Practical optimization for hybrid quantum-classical algorithms
• Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs
• Quantum Recommendation Systems
• A quantum algorithm to train neural networks using low-depth circuits
• A Framework for Approximating Qubit Unitaries

Cited by in corpus (299)

• Variational Quantum Algorithms
• Supervised learning with quantum enhanced feature spaces
• Noisy intermediate-scale quantum (NISQ) algorithms
• The power of quantum neural networks
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Power of data in quantum machine learning
• A rigorous and robust quantum speed-up in supervised machine learning
• Quantum Natural Gradient
• Transfer learning in hybrid classical-quantum neural networks
• Quantum Computing for Finance: State of the Art and Future Prospects
• Robust data encodings for quantum classifiers
• General parameter-shift rules for quantum gradients
• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• The Bitter Truth About Quantum Algorithms in the NISQ Era
• Stochastic gradient descent for hybrid quantum-classical optimization
• Quantum machine learning beyond kernel methods
• Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
• Solving nonlinear differential equations with differentiable quantum circuits
• Exploiting symmetry in variational quantum machine learning
• Supervised quantum machine learning models are kernel methods
• Quantum generative adversarial network for generating discrete distribution
• Quantum Machine Learning for Chemistry and Physics
• Quantum noise protects quantum classifiers against adversaries
• Estimating the gradient and higher-order derivatives on quantum hardware
• Quantum Computing at the Frontiers of Biological Sciences
• Quantum Adversarial Machine Learning
• Avoiding barren plateaus using classical shadows
• Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks
• Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms
• Quantum autoencoders to denoise quantum data
• An efficient quantum algorithm for the time evolution of parameterized circuits
• On barren plateaus and cost function locality in variational quantum algorithms
• Variational Quantum Reinforcement Learning via Evolutionary Optimization
• Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicator
• Quantum Machine Learning: from physics to software engineering
• Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier
• Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule
• Recent advances for quantum classifiers
• Neural Predictor based Quantum Architecture Search
• Biology and medicine in the landscape of quantum advantages
• Universal discriminative quantum neural networks
• Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry
• Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces
• Quantum data compression by principal component analysis
• An end-to-end trainable hybrid classical-quantum classifier
• Barren plateaus in quantum tensor network optimization
• The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation
• Preparation of the SU(3) Lattice Yang-Mills Vacuum with Variational Quantum Methods
• Supervised learning with a quantum classifier using a multi-level system
• Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus
• Fast quantum circuit cutting with randomized measurements
• A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers
• Quantum Neural Network for Quantum Neural Computing
• NISQ Computers: A Path to Quantum Supremacy
• Machine Learning Phase Transitions with a Quantum Processor
• Classification with Quantum Machine Learning: A Survey
• Subtleties in the trainability of quantum machine learning models
• Learning quantum data with the quantum Earth Mover's distance
• Shadows of quantum machine learning
• Information Scrambling in Quantum Neural Networks
• Covariant quantum kernels for data with group structure
• Quantum kernels for real-world predictions based on electronic health records
• Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
• Adaptive pruning-based optimization of parameterized quantum circuits
• Generalization despite overfitting in quantum machine learning models
• Quantum classification of the MNIST dataset with Slow Feature Analysis
• Data compression for quantum machine learning
• Quantum enhancements for deep reinforcement learning in large spaces
• Analytic gradients in variational quantum algorithms: Algebraic extensions of the parameter-shift rule to general unitary transformations
• Determining the proton content with a quantum computer
• Markovian Quantum Neuroevolution for Machine Learning
• Generalized quantum circuit differentiation rules
• Noise-Resilient Variational Hybrid Quantum-Classical Optimization
• Quantum Computing: An Overview Across the System Stack
• A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
• Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks
• Towards quantum advantage via topological data analysis
• Hybrid Classical-Quantum Autoencoder for Anomaly Detection
• Quantum Embedding Search for Quantum Machine Learning
• Style-based quantum generative adversarial networks for Monte Carlo events
• Progress toward favorable landscapes in quantum combinatorial optimization
• Hybrid quantum ResNet for car classification and its hyperparameter optimization
• Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
• Natural Evolutionary Strategies for Variational Quantum Computation
• Toward Trainability of Quantum Neural Networks
• Variational learning for quantum artificial neural networks
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Expressivity of Quantum Neural Networks
• Quantum Architecture Search: A Survey
• A Grover-search Based Quantum Learning Scheme for Classification
• Higgs analysis with quantum classifiers
• Training Gaussian Boson Sampling Distributions
• Quantum Architecture Search via Deep Reinforcement Learning
• Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis
• Scrambling Ability of Quantum Neural Networks Architectures
• Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
• Overfitting in quantum machine learning and entangling dropout
• Quantum Transfer Learning for Real-World, Small, and High-Dimensional Datasets
• Quantum-Noise-Driven Generative Diffusion Models
• Quantum semi-supervised generative adversarial network for enhanced data classification
• Universal Adversarial Examples and Perturbations for Quantum Classifiers
• Completely Quantum Neural Networks
• Predicting toxicity by quantum machine learning
• Grammar-aware sentence classification on quantum computers
• Tree tensor network classifiers for machine learning: from quantum-inspired to quantum-assisted
• Generative machine learning with tensor networks: benchmarks on near-term quantum computers
• Generative training of quantum Boltzmann machines with hidden units
• Fock State-enhanced Expressivity of Quantum Machine Learning Models
• Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits
• Entangled Datasets for Quantum Machine Learning
• Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers
• Quantum Convolutional Neural Networks for Multi-Channel Supervised Learning
• Single-qubit universal classifier implemented on an ion-trap quantum device
• A Simple Quantum Neural Net with a Periodic Activation Function
• Quantum kernels to learn the phases of quantum matter
• How quantum computing can enhance biomarker discovery
• Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions
• Hybrid Quantum-Classical Graph Convolutional Network
• Quantum neural networks force fields generation
• Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
• Evaluation of Parameterized Quantum Circuits with Cross-Resonance Pulse-Driven Entanglers
• Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metric
• Neural network encoded variational quantum algorithms
• Resource Saving via Ensemble Techniques for Quantum Neural Networks
• Hybrid quantum-classical classifier based on tensor network and variational quantum circuit
• Array Antenna Power Pattern Analysis Through Quantum Computing
• Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data
• A duplication-free quantum neural network for universal approximation
• A General Approach to Dropout in Quantum Neural Networks
• Pulse-efficient quantum machine learning
• Reducing the amount of single-qubit rotations in VQE and related algorithms
• Security Aspects of Quantum Machine Learning: Opportunities, Threats and Defenses
• Rapid training of quantum recurrent neural networks
• Training robust and generalizable quantum models
• Quantum advantage for differential equation analysis
• Networked Quantum Services
• Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
• Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
• Fast optimization of parametrized quantum optical circuits
• QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks
• Ground and Excited States from Ensemble Variational Principles
• Quantum algorithms for scientific computing
• Quantum Optimization for Training Quantum Neural Networks
• The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs
• Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers
• Data classification by quantum radial basis function networks
• What can we learn from quantum convolutional neural networks?
• Quantum correlation alignment for unsupervised domain adaptation
• Randomness-enhanced expressivity of quantum neural networks
• Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
• VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability
• Neural quantum kernels: training quantum kernels with quantum neural networks
• Computationally Efficient Quantum Expectation with Extended Bell Measurements
• Simulation of a Variational Quantum Perceptron using Grover's Algorithm
• EHA: Entanglement-variational Hardware-efficient Ansatz for Eigensolvers
• Quantum-machine-learning channel discrimination
• Numerical hardware-efficient variational quantum simulation of a soliton solution
• Quantum Gaussian process model of potential energy surface for a polyatomic molecule
• Quantum computing through the lens of control: A tutorial introduction
• Variational Quanvolutional Neural Networks with enhanced image encoding
• Simulation of memristive synapses and neuromorphic computing on a quantum computer
• Demonstration of a bosonic quantum classifier with data re-uploading
• Constrained and Vanishing Expressivity of Quantum Fourier Models
• The role of data embedding in equivariant quantum convolutional neural networks
• Single-component gradient rules for variational quantum algorithms
• Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator
• On the Principles of Differentiable Quantum Programming Languages
• Can shallow quantum circuits scramble local noise into global white noise?
• Algorithm-Oriented Qubit Mapping for Variational Quantum Algorithms
• Quantum support vector data description for anomaly detection
• Adiabatic quantum algorithm for artificial graphene
• Measurement-induced entanglement phase transitions in variational quantum circuits
• Jet Discrimination with Quantum Complete Graph Neural Network
• A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network
• Quantum autoencoders for image classification
• Variational Quantum Algorithms for Trace Distance and Fidelity Estimation
• A Continuous Variable Born Machine
• Small quantum computers and large classical data sets
• Entanglement assisted training algorithm for supervised quantum classifiers
• Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
• Resource frugal optimizer for quantum machine learning
• Variational secure cloud quantum computing
• Qudit Machine Learning
• Near-term Efficient Quantum Algorithms for Entanglement Analysis
• Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis
• Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
• Image Classification with Rotation-Invariant Variational Quantum Circuits
• Application of the Diamond Gate in Quantum Fourier Transformations and Quantum Machine Learning
• Qsun: an open-source platform towards practical quantum machine learning applications
• Evaluating the Resilience of Variational Quantum Algorithms to Leakage Noise
• SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
• MAQA: A Quantum Framework for Supervised Learning
• Guided Quantum Compression for High Dimensional Data Classification
• Ansatz-Independent Variational Quantum Classifier
• Hybrid Quantum-Classical Autoencoders for End-to-End Radio Communication
• Photovoltaic power forecasting using quantum machine learning
• Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs
• Quantum-enhanced neural networks in the neural tangent kernel framework
• Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans
• Quantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models
• QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems
• Image Compression and Classification Using Qubits and Quantum Deep Learning
• Generating Universal Adversarial Perturbations for Quantum Classifiers
• Quantum inspired qubit qutrit neural networks for real time financial forecasting
• Gradients and frequency profiles of quantum re-uploading models
• Information flow in parameterized quantum circuits
• State preparation of lattice field theories using quantum optimal control
• Quantum Algorithm For Estimating Eigenvalue
• Quantum density estimation with density matrices: Application to quantum anomaly detection
• A Study on Quantum Graph Neural Networks Applied to Molecular Physics
• A Post-Training Approach for Mitigating Overfitting in Quantum Convolutional Neural Networks
• Maximising Quantum-Computing Expressive Power through Randomised Circuits
• Quantum Kernel Evaluation via Hong-Ou-Mandel Interference
• Learning quantum phases via single-qubit disentanglement
• Error-mitigated photonic quantum circuit Born machine
• Quantum Algorithm for Estimating Betti Numbers Using Cohomology Approach
• Generative Invertible Quantum Neural Networks
• Machine-Learning Insights into the Entanglement-trainability Correlation of Parameterized Quantum Circuits
• Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networks
• Variational Optimization for Quantum Problems using Deep Generative Networks
• An example of use of Variational Methods in Quantum Machine Learning
• Channel Attention for Quantum Convolutional Neural Networks
• State Classification via a Random-Walk-Based Quantum Neural Network
• Permutation-equivariant quantum convolutional neural networks
• Bayesian Quantum Circuit
• Quantum neural networks with multi-qubit potentials
• Bringing Quantum Systems under Control: A Tutorial Invitation to Quantum Computing and Its Relation to Bilinear Control Systems
• Single entanglement connection architecture between multi-layer bipartite Hardware Efficient Ansatz
• Hybrid Classical-Quantum method for Diabetic Foot Ulcer Classification
• Trainable Discrete Feature Embeddings for Variational Quantum Classifier
• BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading
• Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms
• Quantum Advantage in Variational Bayes Inference
• Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing
• Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images
• Quantum natural gradient without monotonicity
• QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
• A universal duplication-free quantum neural network
• Input Redundancy for Parameterized Quantum Circuits
• Multidimensional Quantum Generative Modeling by Quantum Hartley Transform
• Introduction to Quantum Error Correction with Stabilizer Codes
• Towards solving the BCS Hamiltonian gap in Near-Term Quantum Computers
• Learning Equivariant Maps with Variational Quantum Circuits
• Learning Fourier series with parametrized quantum circuits
• Fourier Analysis of Variational Quantum Circuits for Supervised Learning
• Noise tailoring for Robust Amplitude Estimation
• Optimized numerical gradient and Hessian estimation for variational quantum algorithms
• Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification
• Quanvolutional Neural Networks for Pneumonia Detection: An Efficient Quantum-Assisted Feature Extraction Paradigm
• Revisiting semiconductor bulk hamiltonians using quantum computers
• Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI
• Towards Improved Quantum Machine Learning for Molecular Force Fields
• Typical Machine Learning Datasets as Low-Depth Quantum Circuits
• Evolved Quantum Boltzmann Machines
• Expressivity of deterministic quantum computation with one qubit
• Quantum-Classical Autoencoder Architectures for End-to-End Radio Communication
• Model-Driven Engineering for Quantum Programming: A Case Study on Ground State Energy Calculation
• Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
• Quadratic speed-ups in quantum kernelized binary classification
• Variable Hyperparameterized Gaussian Kernel using Displaced Squeezed Vacuum State
• A Survey of Classical And Quantum Sequence Models
• Crosstalk-Based Parameterized Quantum Circuit Approximation
• Constant-time Quantum Algorithm for Homology Detection in Closed Curves
• Implications of Deep Circuits in Improving Quality of Quantum Question Answering
• Quantum Generative Training Using Rényi Divergences
• Normalized Gradient Descent for Variational Quantum Algorithms
• A Unified Framework for Quantum Supervised Learning
• OpticalGAN : Generative Adversarial Networks for Continuous Variable Quantum Computation
• Double descent in quantum kernel methods
• Adaptive Quantum Generative Training using an Unbounded Loss Function
• Finding eigenvectors with a quantum variational algorithm
• When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square
• Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training
• Quantum Enhanced Anomaly Detection for ADS-B Data using Hybrid Deep Learning
• Multi-channel convolutional neural quantum embedding
• Scaling of symmetry-restricted quantum circuits
• Guided Graph Compression for Quantum Graph Neural Networks
• Laplacian Eigenmaps with variational circuits: a quantum embedding of graph data
• On Quantum Perceptron Learning via Quantum Search
• Doubling Qubits in a Trapped-Ion System via Vibrational Dual-Rail Encoding
• High-expressibility Quantum Neural Networks using only classical resources
• Quantum Machine Learning for State Tomography Using Classical Data
• Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms
• Extended parameter shift rules with minimal derivative variance for parameterized quantum circuits
• Bayesian ACRONYM Tuning
• Noise-Resilient Quantum Reinforcement Learning
• Quantum feature-map learning with reduced resource overhead
• Variational Quantum Circuit Model for Knowledge Graphs Embedding
• Quantum Computation
• Quantum Machine Learning with SQUID
• Stabilization and Dissipative Information Transfer of a Superconducting Kerr-Cat Qubit
• QAISim: A Toolkit for Modeling and Simulation of AI in Quantum Cloud Computing Environments
• Quantum Algorithm For Solving Nonlinear Algebraic Equations
• Application of Langevin Dynamics to Advance the Quantum Natural Gradient Optimization Algorithm
• Schmidt quantum compressor
• Information geometry of nonmonotonic quantum natural gradient
• Quantum generative classification with mixed states
• Interpolation-based coordinate descent method for parameterized quantum circuits