Quantum Circuit Learning

Abstract

We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.

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• A Unified Framework for Quantum Supervised Learning
• Density matrix representation of hybrid tensor networks for noisy quantum devices
• Adaptive Circuit Learning of Born Machine: Towards Realization of Amplitude Embedding and Quantum Data Loading
• Error Analysis of the Variational Quantum Eigensolver Algorithm
• Typical Machine Learning Datasets as Low-Depth Quantum Circuits
• A circuit-differentiation framework for Green's functions on quantum computers
• The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization
• Variable Hyperparameterized Gaussian Kernel using Displaced Squeezed Vacuum State
• Variational quantum algorithm based on Lagrange polynomial encoding to solve differential equations
• Demonstration of a variational quantum eigensolver with a solid-state spin system under ambient conditions
• Quantum circuit-like learning: A fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning
• NISQ algorithm for the matrix elements of a generic observable
• Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
• Accelerating variational quantum algorithms with multiple quantum processors
• "Proper" Shift Rules for Derivatives of Perturbed-Parametric Quantum Evolutions
• Variational quantum compiling for three-qubit gates design in quantum dots
• Stabilizer configuration interaction: Finding molecular subspaces with error detection properties
• An Efficient Gradient Sensitive Alternate Framework for VQE with Variable Ansatz
• Evolved Quantum Boltzmann Machines
• Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection
• Quantum Approximation of Normalized Schatten Norms and Applications to Learning
• Quantum Curriculum Learning
• Normalized Gradient Descent for Variational Quantum Algorithms
• Quantum Cluster Algorithm for Data classification
• Classification using a two-qubit quantum chip
• Quantum neural network autoencoder and classifier applied to an industrial case study
• On the learning abilities of photonic continuous-variable Born machines
• ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
• Efficient Large-Scale Quantum Optimization via Counterdiabatic Ansatz
• Universal approximation of continuous functions with minimal quantum circuits
• Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures
• Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly
• Quantum natural gradient with thermal-state initialization
• Correcting and extending Trotterized quantum many-body dynamics
• Laplacian Eigenmaps with variational circuits: a quantum embedding of graph data
• Weighted Approximate Quantum Natural Gradient for Variational Quantum Eigensolver
• Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Neural-network-assisted Monte Carlo sampling trained by Quantum Approximate Optimization Algorithm
• Exploring exotic configurations with anomalous features using deep learning: Application of classical and quantum-classical hybrid anomaly detection
• Generative flow-based warm start of the variational quantum eigensolver
• Finding eigenvectors with a quantum variational algorithm
• Experimental kernel-based quantum machine learning in finite feature space
• Quantum Wasserstein distance between unitary operations
• Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
• Robustness of optimized numerical estimation schemes for noisy variational quantum algorithms
• Estimating the degree of non-Markovianity using variational quantum circuits
• Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
• Quantum circuit to estimate pi using quantum amplitude estimation
• Performance analysis of a filtering variational quantum algorithm
• Quantum Sparse Coding and Decoding Based on Quantum Network
• Solving Differential Equations via Continuous-Variable Quantum Computers
• Data-driven Quantum Dynamical Embedding Method for Long-term Prediction on Near-term Quantum Computers
• Pseudo quantum advantages in perceptron storage capacity
• Orthogonal frequency-division multiplexing for simultaneous gate operations on multiple qubits via a shared control line
• Realizing Topological Quantum Walks on NISQ Digital Quantum Computer
• Verifying Adversarial Robustness in Quantum Machine Learning: from theory to physical validation via a software tool
• Velocity Verlet-based optimization for variational quantum eigensolvers
• Quantum Machine Learning for Climate Modelling
• Accelerated spin-adapted ground state preparation with non-variational quantum algorithms
• Variational Quantum Generative Modeling by Sampling Expectation Values of Tunable Observables
• Unitary-transformed projective squeezing: applications for circuit-knitting and state-preparation of non-Gaussian states
• Quantum Machine Learning with SQUID
• Quantum Algorithms for State Preparation and Data Classification based on Stabilizer Codes
• Towards a NISQ Algorithm to Simulate Hermitian Matrix Exponentiation
• Detecting underdetermination in parameterized quantum circuits
• Quantum-classical simulation of quantum field theory by quantum circuit learning
• Variational Quantum Simulation of Chemical Dynamics with Quantum Computers
• A Quantum Convolutional Neural Network for Image Classification
• Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
• Disentangling quantum autoencoder
• Quantum generative classification with mixed states
• Optical Quantum Computing
• Quantum Model-Discovery
• Clustering and enhanced classification using a hybrid quantum autoencoder
• Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
• Quantum Machine Learning for State Tomography Using Classical Data
• Fast suppression of classification error in variational quantum circuits
• Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes
• Error and Resource Estimates of Variational Quantum Algorithms for Solving Differential Equations Based on Runge-Kutta Methods
• Extended parameter shift rules with minimal derivative variance for parameterized quantum circuits
• Noise-Resilient Quantum Reinforcement Learning
• Quantum reservoir computing for predicting and characterizing chaotic maps
• Quantum-Enhanced Neural Exchange-Correlation Functionals
• Double-bracket quantum algorithms for high-fidelity ground state preparation
• Quantum Computing Solution of the Bethe-Salpeter Equation for Relativistic Scalar Bound States via Tensor-Network VQE
• Quantum feature-map learning with reduced resource overhead
• Information geometry of nonmonotonic quantum natural gradient