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.

Table of Contents

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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)

Contents

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 ^

• On quantum ensembles of quantum classifiers (2020)

Quantum k-Nearest Neighbors ^

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

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

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 ^

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

Quantum Image Processing ^

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

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) ^

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

References

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