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qml-vqa-library
A curated list of recent textbooks, reviews, perspectives, and research papers related to quantum machine learning, variational quantum algorithms, tensor networks, and classical machine learning applications in quantum systems.
https://github.com/ericardomuten/qml-vqa-library
- Supervised Learning with Quantum Computers
- Quantum Machine Learning: What Quantum Computing Means to Data Mining
- Quantum machine learning in high energy physics
- Quantum machine learning and its supremacy in high energy physics
- Quantum Reinforcement Learning with Quantum Photonics
- Variational Quantum Algorithms
- A non-review of Quantum Machine Learning: trends and explorations
- Quantum Chemistry in the Age of Quantum Computing
- Quantum Deep Learning Neural Networks
- Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
- Quantum machine learning: a classical perspective
- Quantum machine learning
- Unified framework for quantum classification
- QDNN: deep neural networks with quantum layers
- Quantum Machine Learning Algorithms for Drug Discovery Applications
- On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier
- Quantum state discrimination using noisy quantum neural networks
- Event Classification with Quantum Machine Learning in High-Energy Physics
- Data re-uploading for a universal quantum classifier
- Circuit-centric quantum classifiers
- Hierarchical quantum classifiers
- Quantum circuit learning
- Classification with Quantum Neural Networks on Near Term Processors
- Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
- A rigorous and robust quantum speed-up in supervised machine learning
- Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
- Supervised learning with quantum-enhanced feature spaces
- Quantum machine learning for quantum anomaly detection
- On quantum ensembles of quantum classifiers
- Implementing a distance-based classifier with a quantum interference circuit
- Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
- Methods for accelerating geospatial data processing using quantum computers
- Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
- A Tutorial on Quantum Convolutional Neural Networks (QCNN)
- Explorations in Quantum Neural Networks with Intermediate Measurements
- Quanvolutional neural networks: powering image recognition with quantum circuits
- Hybrid Quantum-Classical Convolutional Neural Networks
- Quantum convolutional neural networks
- A quantum deep convolutional neural network for image recognition
- Quantum Algorithms for Deep Convolutional Neural Networks
- A Tutorial on Quantum Graph Recurrent Neural Network (QGRNN)
- Hybrid Quantum-Classical Graph Convolutional Network
- Performance of Particle Tracking Using a Quantum Graph Neural Network
- A Quantum Graph Neural Network Approach to Particle Track Reconstruction
- Particle Track Reconstruction with Quantum Algorithms
- Quantum Graph Neural Networks
- Anomaly detection with variational quantum generative adversarial networks
- Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer
- Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions
- Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
- Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors
- Quantum versus classical generative modelling in finance
- Experimental Quantum Generative Adversarial Networks for Image Generation
- Quantum semi-supervised generative adversarial network for enhanced data classification
- Near-term quantum-classical associative adversarial networks
- Quantum Generative Adversarial Networks for learning and loading random distributions
- Quantum Generative Adversarial Learning
- Quantum generative adversarial networks
- Variational quantum Boltzmann machines
- Quantum Boltzmann Machine
- Simulating Many-Body Systems with a Projective Quantum Eigensolver
- Qubit-ADAPT-VQE: An Adaptive Algorithm for Constructing Hardware-Efficient Ansätze on a Quantum Processor
- Measurement-Based Variational Quantum Eigensolver
- Classically-Boosted Variational Quantum Eigensolver
- Meta-Variational Quantum Eigensolver: Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation
- Resource-efficient quantum algorithm for protein folding (Application of CVaR-VQE)
- Penalty methods for a variational quantum eigensolver
- Application of Quantum Machine Learning to VLSI Placement
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- Subspace-search variational quantum eigensolver for excited states
- Variational Quantum Computation of Excited States
- A variational eigenvalue solver on a photonic quantum processor
- Warm-starting quantum optimization
- Qubit-efficient encoding schemes for binary optimisation problems
- Quantum gradient algorithm for general polynomials
- Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
- Improving Variational Quantum Optimization using CVaR
- The Quantum Alternating Operator Ansatz on Maximum k-Vertex Cover
- Quantum optimization using variational algorithms on near-term quantum devices
- A Quantum Approximate Optimization Algorithm
- Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
- Variational quantum policies for reinforcement learning
- Variational Quantum Circuits for Deep Reinforcement Learning
- Reinforcement Learning with Quantum Variational Circuit
- Quantum autoencoders with enhanced data encoding
- Quantum Autoencoders to Denoise Quantum Data
- Quantum autoencoders for efficient compression of quantum data
- Optimizing quantum heuristics with meta-learning
- Machine Learning of Noise-Resilient Quantum Circuits
- Avoiding local minima in Variational Quantum Algorithms with Neural Networks
- Layerwise learning for quantum neural networks
- Encoding-dependent generalization bounds for parametrized quantum circuits
- Efficient Discrete Feature Encoding for Variational Quantum Classifier
- Robust data encodings for quantum classifiers
- The power of quantum neural networks
- Power of data in quantum machine learning
- The Inductive Bias of Quantum Kernels
- Supervised quantum machine learning models are kernel methods
- Connecting geometry and performance of two-qubit parameterized quantum circuits
- Expressibility of the alternating layered ansatz for quantum computation
- Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability
- Dimensional Expressivity Analysis of Parametric Quantum Circuits
- Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms
- Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus
- Diagnosing barren plateaus with tools from quantum optimal control
- Cost function dependent barren plateaus in shallow parametrized quantum circuits
- Barren plateaus in quantum neural network training landscapes
- Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information
- Single-component gradient rules for variational quantum algorithms
- Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule
- the noisy parameter-shift rule
- Quantum Natural Gradient
- Evaluating analytic gradients on quantum hardware
- Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition
- Parallel quantum simulation of large systems on small NISQ computers
- Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
- Ray-Based Framework for State Identification in Quantum Dot Devices
- Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies
- On the Quantum versus Classical Learnability of Discrete Distributions
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