This repository will contain the major papers, books and blog posts on QML
- Schuld & Petruccione, 2022, Machine Learning with Quantum Computers
- Hidary, 2022, Quantum Computing: An Applied Approach, 2nd edition
- Pattanyak, 2021, Quantum Machine Learning with PythonGitHub
- Ganguly, 2021, Quantum Machine Learning: An Applied Approach
- Zickert, 2021, Hands-On Quantum Machine Learning, Vol-1
- Dawid et al., 2022, Modern applications of machine learning in quantum sciences
- Di Matteo et al., 2022, Quantum computing with differentiable quantum transforms
- Fedorov et al., 2022, Quantum computing at the quantum advantage threshold: a down-to-business review
- Garcia, Benito, Garcia-Penalvo, 2022. Systematic Literature Review: Quantum Machine Learning and its applications
- Gentinetta et al., 2022, The complexity of quantum support vector machines
- Abhijith J. et al., 2022, Quantum Algorithm Implementations for Beginners
- Kerenidis & Prakash, 2022, Quantum machine learning with subspace states
- Li et al., 2022, Quantum Neural Network Classifiers: A tutorial
- Pozza et al., 2022, Quantum Reinforcement Learning: The maze problem
- Radha & Jao, 2022, Generalized quantum Similiraty Learning
- Schuld & Killoran, 2022, Is quantum advantage the right goal for quantum machine learning?
- Simeone, 2022, An Introduction to Quantum Machine Learning for Engineers
- Tilly et al., 2022, The VQE: a review of methods and best practices
- Yang, Lu and Li, 2022, Accelerated quantum Monte Carlo with mitigated error on noisy quantum computer
- Asfaw et al., 2021, Building a Quantum Engineering Undergraduate Program
- Beer et al., 2021, Quantum machine learning of graph-structured data
- Bharti et al. 2021, Noisy intermediate-scale quantum (NISQ) algorithms
- Biamonte, 2021, On The Mathematical Structure of Quantum Models of Computation Based on Hamiltonian Minimisation
- Bondesan & Welling, 2021, The Hinton in your Neural Network: a Quantum Field Theory View of Deep Learning
- Caro et al., 2021, Generalization in quantum machine learning from few training data
- Ding et al., 2021, Quantum Stream Learning
- Ezhov, 2021, On quantum Neural Networks
- Gratsea & Huembeli, 2021, Exploring Quantum Perceptron and Quantum Neural Network structures with a teacher-student scheme
- Herbert, 2021, Quantum Monte-Carlo Integration: The Full Advantage in Minimal Circuit Depth
- Highman & Bedford, 2021, Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer
- Huang et al., 2021, Quantum advantage in learning from experiments
- Huang et al., 2021, The power of data in quantum machine learning
- Jaderberg et al., 2021, Quantum self-supervised Learning
- Kartsaklis et al., 2021, lambeq: An Efficient High-Level Python Library for Quantum NLP
- Kerenedis, 2021, Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
- Liu et al., 2021, Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
- Lopatnikova, Tran and Sisson, 2021, An Introduction to Quantum Computing for Statisticians and Data Scientists
- Martyn et al., 2021, Grand Unification of Quantum Algorithms
- Massoli et al., 2021, A Leap among Entanglement and Neural Networks: A Quantum Survey
- Perlin et al., 2021, Quantum circuit cutting with maximum-likelihood tomography
- Perrier, Youssry and Ferrie, 2021, QDataset: Quantum Datasets for Machine Learning GitHub
- Qian et al., 2021, The dilemma of quantum neural networks
- Roget, Di Molfetta and Kadri, 2021, Quantum Perceptron Revisited: Computational-Statistical Tradeoffs
- Schuld, 2021, Supervised quantum machine learning models are kernel methods
- Tacchino et al., 2021, Variational learning for quantum artificial neural networks
- Wei et al., 2021, A Quantum Convolutional Neural Network on NISQ Devices
- Wossnig, 2021, Quantum Machine Learning For Classical Data
- Yarkoni et al., 2021, Quantum Annealing for Industry Applications: Introduction and Review
- Abbas et al. 2020, The power of quantum neural networks
- Abbas et al. 2020, On quantum ensemble of quantum classifiers
- Arthur & Date, 2020, Balanced k-Means Clustering on an Adiabatic Quantum Computer
- Bausch, 2020, Recurrent Quantum Neural Network
- Beer et al., 2020, Training deep quantum neural networks
- Cerezo et al., 2020, Variational Quantum Algorithms
- Chen, Yoo and Fang, 2020, Quantum Long Short Term Memory
- Gabor et al., 2020, The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
- Garg & Ramakrishnan, 2020, Advances in Quantum Deep Learning: An Overview
- Gentile et al., 2020, Learning models of quantum systems from experiments
- Khairy et al., 2020, Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems
- Oliviera et al., 2020, Quantum One-class Classification With a Distance-based Classifier
- Pan et al., 2020, Experimental semi-autonomous eigensolver using reinforcement learning
- Perelshtein et al., 2020, Large-scale quantum hybrid solution for linear systems of equations
- Poland, Beer and Osborne, 2020, No Free Lunch for Quantum Machine Learning
- Schuld, Sweke, Meyer, 2020, The effect of data encoding on the expressive power of variational quantum machine learning models
- Tang et al., 2020, CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations
- Wang et al., 2020, Noise-Induced Barren Plateaus in Variational Quantum Algorithms
- Xia et al., 2020, Quantum-enhanced data classification with a variational entangled sensor network
- Zhang & Ni, 2020, Recent Advances in Quantum Machine Learning
- Benedetti et al., 2019, Parameterized quantum circuits as machine learning models
- Orus, Mugel, Lizaso, 2019, Quantum computing for finance: Overview and prospects
- Tacchino et al., 2019, An artificial neuron implemented on an actual quantum processor
- Wang et al., 2019, Quantized Generative Adversarial Network
- Zoufal, Lucchi and Werner, 2019, Quantum Generative Adversarial Networks for learning and loading random distributions
- Bergholm et al., 2018, PennyLane: Automatic differentiation of hybrid quantum-classical computations
- Kopczyk, 2018, Quantum Machine Learning for data scientists
- Schuld & Killoran, 2018, Quantum machine learning in feature Hilbert spaces
- Zhao et al., 2018, Bayesian Deep Learning on a Quantum ComputerGitHub
- Cao, Guerreschi, Aspuru-Guzik, 2017, Quantum Neuron: an elementary building block for machine learning on quantum computersGithub
- Liu & Rebentrost, 2017, Quantum machine learning for quantum anomaly detection
- Otterbach et al., 2017, Unsupervised Machine Learning on a Hybrid Quantum ComputerGitHub
- Perdomo-Ortiz et al. 2017, Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
- Biamonte et al., 2016, Quantum machine Learning
- Aaronson, 2015, Quantum Machine Learning Algorithms: Read the Fine Print
- Fahri & Goldstone, 2014, Quantum Approximate Optimization Algorithms
- Schuld, Synayskly and Petruccione, 2014, The quest for a Quantum Neural Network
- Schuld, Synayskly and Petruccione, 2014, Simulating a perceptron on a quantum computer
- Schuld, Synayskly and Petruccione, 2014, An introduction to quantum machine learning
- Wittek, 2014, Quantum Machine Learning: What Quantum Computing Means to Data Mining
- Llyod, Mohseni, Rebentrost, 2013, Quantum algorithms for supervised and unsupervised machine learning
- Qiskit medium, 2022, We are releasing a free hands-on quantum machine learning course online
- Qunasys, , Accelerating variational quantum algorithms
- What is quantum CNN?
- Dunjko et al., 2020, A non-review of Quantum Machine Learning: trends and explorations
- IBM quantum research, At what cost can we simulate l'orge quantum circuit on small quantum computers
- Pennylane, How to QML
- IEEE Spectrum, 2022, Quantum Error Correction
- Google AI Blog, 2021, Quantum Machine Learning and the Power of Data
- QTML 2021
- Ijaz, An introduction to Quantum Machine Learning
- Schuld, 2020, Quantum Machine Learning
- Schuld, 2020, QUantum Machine Learning and Pennylane
- Wittek, 2015, What Can We Expect from Quantum Machine Learning?
- Preskill, 2022, PH219, Quantum Computing
- Peter Wittek, 2019, QML
- Qiskit, 2022, QML
- Qiskit, 2021, Quantum Machine Learning | 2021 Qiskit Global Summer School
- Pennylane, QML
- Xanadu, Codebook
- CERN, Elias Fernandez-Combarro Alvarez, "A practical introduction to quantum computing: from qubits to quantum machine learning and beyond" 7 lectures
- Llyod, 2016, Quantum Machine Learning