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 Neural Networks & Variational Quantum Classifier
  • Quantum Support Vector Machine
  • Quantum Ensembles
  • Quantum k-Nearest Neighbors
• Quantum Convolutional Neural Networks
  • Near Term (without QRAM)
  • Need QRAM
• 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)

• Supervised Learning with Quantum Computers (2018)
• Quantum Machine Learning: What Quantum Computing Means to Data Mining (2014)

• 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)

• 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)

• 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)

• On quantum ensembles of quantum classifiers (2020)

• Implementing a distance-based classifier with a quantum interference circuit (2017)

• 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)

• A quantum deep convolutional neural network for image recognition (2020)
• Quantum Algorithms for Deep Convolutional Neural Networks (2020)

• 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)

• 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)

• Variational quantum Boltzmann machines (2021)
• Quantum Boltzmann Machine (2018)

• 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)

• 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 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 with enhanced data encoding (2021)
• Quantum Autoencoders to Denoise Quantum Data (2020)
• Quantum autoencoders for efficient compression of quantum data (2017)

• 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)

• 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)

• 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)

• 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)

• 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)

• Parallel quantum simulation of large systems on small NISQ computers (2021)

• Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment (2017)

• 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)

• On the Quantum versus Classical Learnability of Discrete Distributions (2021)

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