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.
Scheme of a hybrid quantum-classical algorithm for supervised learning[1].
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- Textbooks
- Reviews & Perspectives
- Quantum Classifier
- Quantum Convolutional Neural Networks
- Quantum Graph Neural Networks
- Quantum Generative Models & Quantum GANs
- Quantum Boltzmann Machines
- Variational Quantum Eigensolver
- Quantum Optimization
- Quantum Reinforcement Learning
- Quantum Autoencoders
- Training & Circuit Construction Techniques
- Embedding/Encoding Techniques
- Circuit Learning Capability Analysis (Expressivity, Entanglement, etc.)
- Barren Plateaus Analysis
- Gradient Techniques
- Tensor Networks
- Quantum Image Processing
- Classical Machine Learning Applications in Quantum Computing
- Uncategorized (yet)
Textbooks ^
- Supervised Learning with Quantum Computers (2018)
- Quantum Machine Learning: What Quantum Computing Means to Data Mining (2014)
Reviews & Perspectives ^
- Quantum machine learning in high energy physics (2021)
- Quantum machine learning and its supremacy in high energy physics (2021)
- Quantum Reinforcement Learning with Quantum Photonics (2021)
- Variational Quantum Algorithms (2020)
- A non-review of Quantum Machine Learning: trends and explorations (2020)
- Quantum Chemistry in the Age of Quantum Computing (2019)
- Quantum Deep Learning Neural Networks (2019)
- Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers (2018)
- Quantum machine learning: a classical perspective (2018)
- Quantum machine learning (2017)
Quantum Classifier ^
Quantum Neural Networks & Variational Quantum Classifier ^
- Unified framework for quantum classification (2021)
- QDNN: deep neural networks with quantum layers (2021)
- Quantum Machine Learning Algorithms for Drug Discovery Applications (2021)
- On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier (2021)
- Quantum state discrimination using noisy quantum neural networks (2021)
- Event Classification with Quantum Machine Learning in High-Energy Physics (2021)
- Data re-uploading for a universal quantum classifier (2020)
- Circuit-centric quantum classifiers (2020)
- Hierarchical quantum classifiers (2018)
- Quantum circuit learning (2018)
- Classification with Quantum Neural Networks on Near Term Processors (2018)
Quantum Support Vector Machine ^
- Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC (2021)
- A rigorous and robust quantum speed-up in supervised machine learning (2020)
- Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware (2019)
- Supervised learning with quantum-enhanced feature spaces (2019)
- Quantum machine learning for quantum anomaly detection (2018)
Quantum Ensembles ^
Quantum k-Nearest Neighbors ^
Quantum Convolutional Neural Networks ^
Near Term (without QRAM) ^
- Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition (2021)
- Methods for accelerating geospatial data processing using quantum computers (2021)
- Quantum Convolutional Neural Networks for High Energy Physics Data Analysis (2020)
- A Tutorial on Quantum Convolutional Neural Networks (QCNN) (2020)
- Explorations in Quantum Neural Networks with Intermediate Measurements (2020)
- Quanvolutional neural networks: powering image recognition with quantum circuits (2020)
- Hybrid Quantum-Classical Convolutional Neural Networks (2019)
- Quantum convolutional neural networks (2019)
Need QRAM ^
- A quantum deep convolutional neural network for image recognition (2020)
- Quantum Algorithms for Deep Convolutional Neural Networks (2020)
Quantum Graph Neural Networks ^
- A Tutorial on Quantum Graph Recurrent Neural Network (QGRNN) (2021)
- Hybrid Quantum-Classical Graph Convolutional Network (2021)
- Performance of Particle Tracking Using a Quantum Graph Neural Network (2020)
- A Quantum Graph Neural Network Approach to Particle Track Reconstruction (2020)
- Particle Track Reconstruction with Quantum Algorithms (2019)
- Quantum Graph Neural Networks (2019)
Quantum Generative Models & Quantum GANs ^
- Anomaly detection with variational quantum generative adversarial networks (2021)
- Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer (2021)
- Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions (2021)
- Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics (2021)
- Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors (2021)
- Quantum versus classical generative modelling in finance (2021)
- Experimental Quantum Generative Adversarial Networks for Image Generation (2020)
- Quantum semi-supervised generative adversarial network for enhanced data classification (2020)
- Near-term quantum-classical associative adversarial networks (2019)
- Quantum Generative Adversarial Networks for learning and loading random distributions (2019)
- Quantum Generative Adversarial Learning (2018)
- Quantum generative adversarial networks (2018)
Quantum Boltzmann Machines ^
Variational Quantum Eigensolver ^
- Simulating Many-Body Systems with a Projective Quantum Eigensolver (2021)
- Qubit-ADAPT-VQE: An Adaptive Algorithm for Constructing Hardware-Efficient Ansätze on a Quantum Processor (2021)
- Measurement-Based Variational Quantum Eigensolver (2021)
- Classically-Boosted Variational Quantum Eigensolver (2021)
- Meta-Variational Quantum Eigensolver: Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation (2021)
- Resource-efficient quantum algorithm for protein folding (Application of CVaR-VQE) (2021)
- Penalty methods for a variational quantum eigensolver (2021)
- Application of Quantum Machine Learning to VLSI Placement (2020)
- An adaptive variational algorithm for exact molecular simulations on a quantum computer (2019)
- Subspace-search variational quantum eigensolver for excited states (2019)
- Variational Quantum Computation of Excited States (2019)
- A variational eigenvalue solver on a photonic quantum processor (2014)
Quantum Optimization ^
- Warm-starting quantum optimization (2021)
- Qubit-efficient encoding schemes for binary optimisation problems (2021)
- Quantum gradient algorithm for general polynomials (2021)
- Quantum approximate optimization of non-planar graph problems on a planar superconducting processor (2021)
- Improving Variational Quantum Optimization using CVaR (2020)
- The Quantum Alternating Operator Ansatz on Maximum k-Vertex Cover (2020)
- Quantum optimization using variational algorithms on near-term quantum devices (2018) (this one is like a review of VQE)
- A Quantum Approximate Optimization Algorithm (2014)
Quantum Reinforcement Learning ^
- Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning (2021)
- Variational quantum policies for reinforcement learning (2021)
- Variational Quantum Circuits for Deep Reinforcement Learning (2020)
- Reinforcement Learning with Quantum Variational Circuit (2020)
Quantum Autoencoders ^
- Quantum autoencoders with enhanced data encoding (2021)
- Quantum Autoencoders to Denoise Quantum Data (2020)
- Quantum autoencoders for efficient compression of quantum data (2017)
Training & Circuit Construction Techniques ^
- Optimizing quantum heuristics with meta-learning (2021)
- Machine Learning of Noise-Resilient Quantum Circuits (2021)
- Avoiding local minima in Variational Quantum Algorithms with Neural Networks (2021)
- Layerwise learning for quantum neural networks (2021)
Embedding/Encoding Techniques ^
- Encoding-dependent generalization bounds for parametrized quantum circuits (2021)
- Efficient Discrete Feature Encoding for Variational Quantum Classifier (2020)
- Robust data encodings for quantum classifiers (2020)
Circuit Learning Capability Analysis (Expressivity, Entanglement, etc.) ^
- The power of quantum neural networks (2021)
- Power of data in quantum machine learning (2021)
- The Inductive Bias of Quantum Kernels (2021)
- Supervised quantum machine learning models are kernel methods (2021)
- Connecting geometry and performance of two-qubit parameterized quantum circuits (2021)
- Expressibility of the alternating layered ansatz for quantum computation (2021)
- Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability (2021)
- Dimensional Expressivity Analysis of Parametric Quantum Circuits (2021)
- Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms (2019)
Barren Plateaus Analysis ^
- Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus (2021)
- Diagnosing barren plateaus with tools from quantum optimal control (2021)
- Cost function dependent barren plateaus in shallow parametrized quantum circuits (2021)
- Barren plateaus in quantum neural network training landscapes (2018)
Gradient Techniques ^
- Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information (2021)
- Single-component gradient rules for variational quantum algorithms (2021)
- Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule (2021)
- the noisy parameter-shift rule (2020)
- Quantum Natural Gradient (2020)
- Evaluating analytic gradients on quantum hardware (2019)
- Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition (2019)
Tensor Networks ^
Quantum Image Processing ^
Classical Machine Learning Applications in Quantum Computing ^
- Ray-Based Framework for State Identification in Quantum Dot Devices (2021)
- Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies (2021)
Uncategorized (yet) ^
1. Macaluso A., Clissa L., Lodi S., Sartori C. (2020) A Variational Algorithm for Quantum Neural Networks. In: Krzhizhanovskaya V. et al. (eds) Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12142. Springer, Cham. https://doi.org/10.1007/978-3-030-50433-5_45