This repository will contain the major papers, books and blog posts on QML
- Hidary, 2022, Quantum Computing: An Applied Approach, 2nd edition
- Schuld & Petruccione, 2022, Machine Learning with Quantum Computers
- Wong, 2022. Introduction to classical and quantum computing
- Pattanyak, 2021, Quantum Machine Learning with PythonGitHub
- Ganguly, 2021, Quantum Machine Learning: An Applied Approach
- Zickert, 2021, Hands-On Quantum Machine Learning, Vol-1
- Aaronson, 2022, How Much Structure Is Needed for Huge Quantum Speedups?
- Abhijith J. et al., 2022, Quantum Algorithm Implementations for Beginners
- Akhalwaya et al., 2022, Exponential advantage on noisy quantum computers
- Anand et al., 2022, Exploring the role of parameters in variational quantum algorithms
- Anschuetz et al., 2022 Interpretable Quantum Advantage in Neural Sequence Learning
- Back & Run & Kim, 2022, Scalable Quantum Convolutional Neural Networks
- Beaudoin et al., 2022, Quantum Machine Learning for Material Synthesis and Hardware Security
- Bermejo & Orus, 2022, Variational Quantum and Quantum-Inspired Clustering
- Buessen & Segal & Khait, 2022, Simulating time evolution on distributed quantum computers
- Callison & Browne, 2022, Improved maximum-likelihood quantum amplitude estimation
- Caro et al., 2022, Generalization in quantum machine learning from few training data
- Chaudhary et al., 2022, Towards a scalable discrete quantum generative adversarial neural network
- Cherrat et al., 2022, Quantum Vision Transformers
- Consiglio & Appollaro & Wiesniak, 2022, A Variational Approach to the Quantum Separability Problem
- Cimini et al., 2022, Deep reinforcement learning for quantum multiparameter estimation
- Cruz & Monteiro, 2022, Quantum Error Correction via Noise Guessing Decoding
- Dasgupta & Paine, 2022, Loading Probability Distributions in a Quantum circuit
- Dawid et al., 2022, Modern applications of machine learning in quantum sciences
- Di Matteo et al., 2022, Quantum computing with differentiable quantum transforms
- Ding & Spector, 2022, Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
- Duffield & Benedetti & Rosenkranz, 2022, Bayesian Learning of Parameterised Quantum Circuits
- Emmanoulopoulos & Dimoska, 2022, Quantum Machine Learning in Finance: Time Series Forecasting
- Ewaniuk et al., 2022, Realistic quantum photonic neural networks
- Fadol et al., 2022, Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC
- Fedorov et al., 2022, Quantum computing at the quantum advantage threshold: a down-to-business review
- Ferris et al., 2022, Quantum Simulation on Noisy Superconducting Quantum Computers
- Fujii et al., 2022, Deep Variational Quantum Eigensolver: a divide-and-conquer method for solving a larger problem with smaller size quantum computers
- 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
- Ghosh & Ghosh, 2022, Classical and quantum machine learning applications in spintronics
- Gili et al., 2022, Do Quantum Circuit Born Machines Generalize?
- Gomez et al., 2022, Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
- Grossi et al., 2022, Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
- Guan & Fang & Ying, 2022, Verifying Fairness in Quantum Machine Learning
- Gyurik & Dunjko, 2022, On establishing learning separations between classical and quantum machine learning with classical data
- Heimann & Schönhoff & Franck Kirchner, 2022, Learning capability of parametrized quantum circuits
- Hu et al., 2022, Quantum Neural Network Compression
- Ikeda, 2022, Quantum Extensive Form Games
- Incudini et al., 2022, Computing graph edit distance on quantum devices
- Incudini & Martini & Di Pierro, 2022, Structure Learning of Quantum Embeddings
- Innocenti et al., 2022, On the potential and limitations of quantum extreme learning machines
- Jäger & Krems, 2022, Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
- Kerenidis & Prakash, 2022, Quantum machine learning with subspace states
- Kim & Huh & Park, 2022, Classical-to-quantum convolutional neural network transfer learning
- Kiss et al., 2022, Quantum neural networks force fields generation
- Koike-Akino & Wang & Wang, 2022, AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
- Krenn et al., 2022, Artificial Intelligence and Machine Learning for Quantum Technologies
- Krol et al. 2022, Efficient parameterized compilation for hybrid quantum programming
- Kyriienko & Magnusson, 2022, Unsupervised quantum machine learning for fraud detection
- Lai & Kuo & Liao, 2022, Syndrome decoding by quantum approximate optimization
- Li et al., 2022, Quantum Neural Network Classifiers: A tutorial
- Liang et al., 2022, PAN: Pulse Ansatz on NISQ Machines
- Liao et al., 2022, Decohering Tensor Network Quantum Machine Learning Models
- Lin & Li & Huang, 2022, Quaternion-based machine learning on topological quantum systems
- Lisnichenko & Protasov, 2022, Case study on quantum convolutional neural network scalability
- Maheshwari & Garcia-Zapirain & Sierra-Sosa, 2022, Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review
- Majumder & Lewis & Bose, 2022, Variational Quantum Circuits for Multi-Qubit Gate Automata
- Mancilla & Pere, 2022, A Preprocessing Perspective for Quantum Machine Learning Classification Advantage Using NISQ Algorithms
- Marconi et al., 2022, The role of coherence theory in attractor quantum neural networks
- Markidis, 2022, On the Physics-Informed Neural Networks for Quantum Computers
- Martin & Plekhanov & Lubasch, 2022, Barren plateaus in quantum tensor network optimization
- Mensa et al., 2022, Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
- Miyahara & Roychowdhury, 2022, Quantum Advantage in Variational Bayes Inference
- Nakaji & Tezuka & Yamamoto, 2022, Deterministic and random features for large-scale quantum kernel machine
- Neumann & Wezeman, 2022, Distributed Quantum Machine Learning
- Nguemto & Leyton-Ortega, 2022, Re-QGAN: an optimized adversarial quantum circuit learning framework
- Olivera-Atencio et al., 2022, Quantum reinforcement learning in the presence of thermal dissipation
- Ono et al., 2022, Demonstration of a bosonic quantum classifier with data re-uploading
- Oshiyama & Ohzeki, 2022, Benchmark of quantum‑inspired heuristic solvers for quadratic unconstrained binary optimization
- Otgonbaatar et al., 2022, Quantum Transfer Learning for Real-World, Small, and Large-Scale Datasets
- Patel et al., 2022, Reinforcement Learning Assisted Recursive QAOA
- Park et al., 2022, Quantum multi-programming for Grover’s search
- Pechal et al., 2022, Direct implementation of a perceptron in superconducting circuit quantum hardware
- Peham & Burgholzer, 2022, Equivalence Checking of Quantum Circuits with the ZX-Calculus
- Peters & Schuld, 2022, Generalization despite overfitting in quantum machine learning models
- Phan et al., 2022, On quantum factoring using noisy intermediate-scale quantum computers
- Polson & Sokolov & Xu, 2022, Quantum Bayes AI
- Pozza et al., 2022, Quantum Reinforcement Learning: The maze problem
- Qi, 2022, Federated Quantum Natural Gradient Descent for Quantum Federated Learning
- Qin et al., 2022, Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
- Radha & Jao, 2022, Generalized quantum Similarity Learning
- Sajjan et al., 2022, Quantum machine learning for chemistry and physics
- Schuld & Killoran, 2022, Is quantum advantage the right goal for quantum machine learning?
- Sharma & Kumar, 2022, A Comparative Study of Classical and Quantum Machine Learning Models for Sentimental Analysis
- Schenk et al., 2022, Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
- Simeone, 2022, An Introduction to Quantum Machine Learning for Engineers
- Simonetti, Perri & Gervasi, 2022, An example of use of Variational Methods in Quantum Machine Learning
- Srikumar & Hill & Hollenberg, 2022, A kernel-based quantum random forest for improved classification
- Takeda et al., 2022, Quantum-inspired algorithm applied to extreme learning
- Tancara et al., 2022, Kernel-based quantum regressor models learn non-Markovianity
- Thanasilp et al., 2022, Exponential concentration and untrainability in quantum kernel methods
- Tibaldi et al., 2022, Bayesian Optimization for QAOA
- Tilly et al., 2022, The VQE: a review of methods and best practices
- Uvarov, 2022, Variational quantum algorithms for local Hamiltonian problems
- Viktorovich, 2022, Variational quantum algorithms for local Hamiltonian problems
- Wang & Jiang, 2022, Data reconstruction based on quantum neural networks
- Wang et al. 2022, Symmetric Pruning in Quantum Neural Networks
- Wazni, Lo, McPheat, Sadrzadeh, 2022, A Quantum Natural Language Processing Approach to Pronoun Resolution
- Wilkinson & Hartmann, 2022, Evaluating the performance of sigmoid quantum perceptrons in quantum neural networks
- Wu & Tao & Li, 2022, Scalable Quantum Neural Networks for Classification
- Yang, Lu and Li, 2022, Accelerated quantum Monte Carlo with mitigated error on noisy quantum computer
- Yun & Park & Kim, 2022, Quantum Multi-Agent Meta Reinforcement Learning
- Yun et al., 2022, Slimmable Quantum Federated Learning
- Zaborniak et al., 2022, A QUBO model of the RNA folding problem optimized by variational hybrid quantum annealing
- Zhang et al., 2022, QUARK: A Gradient-Free Quantum Learning Framework For Classification Tasks
- Altares-Lopez & Ribeiro & Garcıa-Ripoll, 2021, Automatic design of quantum feature maps
- Asfaw et al., 2021, Building a Quantum Engineering Undergraduate Program
- Atchade-Adelomou et al., 2021, quantum Case-Based Reasoning (qCBR)
- 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
- Dutta et al., 2021, Single-qubit universal classifier implemented on an ion-trap quantum device
- 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
- Huggins et al., 2021, Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers
- 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
- Li & Deng, 2021, Recent advances for quantum classifiers
- 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
- Motta & Rice, 2021, Emerging quantum computing algorithms for quantum chemistry
- 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
- Fujii et al. 2020, Deep Variational Quantum Eigensolver: a divide-and-conquer method for solving a larger problem with smaller size quantum computers
- 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
- Liu et al., 2020, Efficient quantum algorithm for dissipative nonlinear differential equations
- 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
- Pérez-Salinas et al., 2020, Data re-uploading for a universal quantum classifier
- 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
- Havlicek et al., 2019, Supervised learning with quantum enhanced feature spaces
- Orus, Mugel, Lizaso, 2019, Quantum computing for finance: Overview and prospects
- Tacchino et al., 2019, An artificial neuron implemented on an actual quantum processor
- Verdon et al., 2019, Learning to learn with quantum neural networks via classical neural networks
- 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
- Cao et al., 2022, Quantum Chemistry in the Age of Quantum Computing
- Cortese & Braje, 2018, Loading Classical Data into a Quantum Computer
- 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
- Arunachalam & de Wolf, 2017, A Survey of Quantum Learning Theory
- Cao, Guerreschi, Aspuru-Guzik, 2017, Quantum Neuron: an elementary building block for machine learning on quantum computersGithub
- Dunjko & Briegel, 2017, Machine learning & artificial intelligence in the quantum domain
- 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
- Montanaro 2016, Quantum algorithms: an overview
- 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
- Sgarbas, 2007, The road to Quantum Artificial Intelligence
- Kaur & Venegas-Gomez, 2022, Defining the quantum workforce landscape: a review of global quantum education initiatives
- Peron et al., 2022, Quantum Undergraduate Education and Scientific Training
- Asfaw et al., 2022, Building a Quantum Engineering Undergraduate Program
- Dzurak et al., 2021, Development of an Undergraduate Quantum Engineering Degree
- Ozhigov 2021, Quantum computations (course of lectures)
- Siddhu & Tayur, 2021, Five Starter Pieces: Quantum Information Science via Semi-definite Programs
- Tang et al., 2021, Teaching quantum information technologies and a practical module for online and offline undergraduate students
- 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
- Quantum Google AI, 2022, Quantum Summer Symposium
- QPL 2022, Quantum Physics and Logic
- 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