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| # What papers were published in 2022 in QML? | ||
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| ### Papers | ||
| #### 2021 | ||
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| - [ ] [Altares-Lopez & Ribeiro & Garcıa-Ripoll, 2021, Automatic design of quantum feature maps](https://arxiv.org/pdf/2105.12626.pdf) | ||
| - [ ] [Asfaw et al., 2021, Building a Quantum Engineering Undergraduate Program](https://arxiv.org/pdf/2108.0131.pdf) | ||
| - [ ] [Atchade-Adelomou et al., 2021, quantum Case-Based Reasoning (qCBR)](https://arxiv.org/abs/2104.00409) | ||
| - [ ] [Beer et al., 2021, Quantum machine learning of graph-structured data](https://arxiv.org/pdf/2103.10837.pdf) | ||
| - [ ] [Bharti et al. 2021, Noisy intermediate-scale quantum (NISQ) algorithms](https://arxiv.org/abs/2101.08448) | ||
| - [ ] [Biamonte, 2021, On The Mathematical Structure of Quantum Models of Computation Based on Hamiltonian Minimisation](https://arxiv.org/pdf/2009.10088.pdf) | ||
| - [ ] [Bondesan & Welling, 2021, The Hinton in your Neural Network: a Quantum Field Theory View of Deep Learning](https://arxiv.org/abs/2103.04913v1) | ||
| - [ ] [Caro et al., 2021, Generalization in quantum machine learning from few training data](https://arxiv.org/abs/2111.05292) | ||
| - [ ] [Choi & Oh & Kim, 2021, A Tutorial on Quantum Graph Recurrent Neural | ||
| Network (QGRNN)](https://ieeexplore.ieee.org/document/9333917) | ||
| - [ ] [Ding et al., 2021, Quantum Stream Learning](https://arxiv.org/pdf/2112.06628v1.pdf) | ||
| - [ ] [Dutta et al., 2021, Single-qubit universal classifier implemented on an ion-trap quantum device](https://arxiv.org/pdf/2106.14059.pdf) | ||
| - [ ] [Ezhov, 2021, On quantum Neural Networks](https://arxiv.org/pdf/2104.07106.pdf) | ||
| - [ ] [Gratsea & Huembeli, 2021, Exploring Quantum Perceptron and Quantum Neural Network structures with a teacher-student scheme](https://arxiv.org/pdf/2105.01477.pdf) | ||
| - [ ] [Henry et al., 2021, Quantum evolution kernel : Machine learning on graphs with programmable arrays of qubits](https://arxiv.org/pdf/2107.03247.pdf) | ||
| - [ ] [Herbert, 2021, Quantum Monte-Carlo Integration: The Full Advantage in Minimal Circuit Depth](https://arxiv.org/abs/2105.09100) | ||
| - [ ] [Highman & Bedford, 2021, Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer](https://arxiv.org/abs/2107.08710v1) | ||
| - [ ] [Huang et al., 2021, Quantum advantage in learning from experiments](https://arxiv.org/abs/2112.00778) | ||
| - [ ] [Huang et al., 2021, The power of data in quantum machine learning](https://www.nature.com/articles/s41467-021-22539-9) | ||
| - [ ] [Huggins et al., 2021, Efficient and noise resilient measurements for quantum | ||
| chemistry on near-term quantum computers](https://www.nature.com/articles/s41534-020-00341-7.pdf) | ||
| - [ ] [Jaderberg et al., 2021, Quantum self-supervised Learning](https://arxiv.org/abs/2103.14653) | ||
| - [ ] [Kartsaklis et al., 2021, lambeq: An Efficient High-Level Python Library for Quantum NLP](https://www.arxiv.org/abs/2110.04236) | ||
| - [ ] [Kerenedis, 2021, Quantum Algorithms for Unsupervised Machine Learning and Neural Networks](https://arxiv.org/pdf/2111.03598.pdf) | ||
| - [ ] [Kyriienko & Paine & Elfving, 2021, Solving nonlinear differential equations with differentiable quantum circuits](https://arxiv.org/pdf/2011.10395.pdf) | ||
| - [ ] [Li & Deng, 2021, Recent advances for quantum classifiers](https://arxiv.org/pdf/2108.13421.pdf) | ||
| - [ ] [Liu et al., 2021, Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers](https://arxiv.org/abs/2102.05566) | ||
| - [ ] [Lopatnikova, Tran and Sisson, 2021, An Introduction to Quantum Computing for Statisticians and Data Scientists](https://arxiv.org/abs/2112.06587) | ||
| - [ ] [Martyn et al., 2021, Grand Unification of Quantum Algorithms](https://journals.aps.org/prxquantum/pdf/10.1103/PRXQuantum.2.040203) | ||
| - [ ] [Massoli et al., 2021, A Leap among Entanglement and Neural Networks: A Quantum Survey](https://arxiv.org/abs/2107.03313) | ||
| - [ ] [Motta & Rice, 2021, Emerging quantum computing algorithms for quantum chemistry](https://arxiv.org/pdf/2109.02873.pdf) | ||
| - [ ] [Perlin et al., 2021, Quantum circuit cutting with maximum-likelihood tomography](https://www.nature.com/articles/s41534-021-00390-6) | ||
| - [ ] [Perrier, Youssry and Ferrie, 2021, QDataset: Quantum Datasets for Machine Learning](https://arxiv.org/abs/2108.06661) [GitHub](https://github.com/eperrier/QDataSet) | ||
| - [ ] [Qian et al., 2021, The dilemma of quantum neural networks](https://arxiv.org/pdf/2106.04975.pdf) | ||
| - [ ] [Roget, Di Molfetta and Kadri, 2021, Quantum Perceptron Revisited: Computational-Statistical Tradeoffs](https://arxiv.org/pdf/2106.02496.pdf) | ||
| - [ ] [Schuld, 2021, Supervised quantum machine learning models are kernel methods](https://arxiv.org/abs/2101.11020) | ||
| - [ ] [Tacchino et al., 2021, Variational learning for quantum artificial neural networks](https://arxiv.org/pdf/2103.02498.pdf) | ||
| - [ ] [Wei et al., 2021, A Quantum Convolutional Neural Network on NISQ Devices](https://arxiv.org/pdf/2104.06918.pdf) | ||
| - [ ] [Wossnig, 2021, Quantum Machine Learning For Classical Data](https://arxiv.org/pdf/2105.03684.pdf) | ||
| - [ ] [Yarkoni et al., 2021, Quantum Annealing for Industry Applications: Introduction and Review](https://arxiv.org/abs/2112.07491) | ||
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| #### 2020 | ||
| - [ ] [Abbas et al. 2020, The power of quantum neural networks](https://arxiv.org/abs/2011.00027) | ||
| - [ ] [Abbas et al. 2020, On quantum ensemble of quantum classifiers](https://arxiv.org/abs/2001.10833) | ||
| - [ ] [Arthur & Date, 2020, Balanced k-Means Clustering on an Adiabatic Quantum Computer](https://arxiv.org/pdf/2008.04419.pdf) | ||
| - [ ] [Bausch, 2020, Recurrent Quantum Neural Network](https://arxiv.org/pdf/2006.14619.pdf) | ||
| - [ ] [Beer et al., 2020, Training deep quantum neural networks](https://www.nature.com/articles/s41467-020-14454-2.pdf) | ||
| - [ ] [Cerezo et al., 2020, Variational Quantum Algorithms](https://arxiv.org/abs/2012.09265) | ||
| - [ ] [Chen, Yoo and Fang, 2020, Quantum Long Short Term Memory](https://arxiv.org/abs/2009.01783) | ||
| - [ ] [Fujii et al. 2020, Deep Variational Quantum Eigensolver: a divide-and-conquer method for solving a larger problem with smaller size quantum computers](https://arxiv.org/abs/2007.10917) | ||
| - [ ] [Gabor et al., 2020, The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline](https://arxiv.org/abs/2004.14035) | ||
| - [ ] [Garg & Ramakrishnan, 2020, Advances in Quantum Deep Learning: An Overview](https://arxiv.org/pdf/2005.04316.pdf) | ||
| - [ ] [Gentile et al., 2020, Learning models of quantum systems from experiments](https://arxiv.org/abs/2002.06169) | ||
| - [ ] [Khairy et al., 2020, Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems](https://ojs.aaai.org//index.php/AAAI/article/view/5616) | ||
| - [ ] [Liu et al., 2020, Efficient quantum algorithm for dissipative nonlinear differential equations](https://arxiv.org/abs/2011.03185) | ||
| - [ ] [Oliviera et al., 2020, Quantum One-class Classification With a Distance-based Classifier](https://arxiv.org/pdf/2007.16200.pdf) | ||
| - [ ] [Pan et al., 2020, Experimental semi-autonomous eigensolver using reinforcement learning](https://arxiv.org/pdf/2007.15521.pdf) | ||
| - [ ] [Perelshtein et al., 2020, Large-scale quantum hybrid solution for linear systems of equations](https://arxiv.org/pdf/2003.12770.pdf) | ||
| - [ ] [Pérez-Salinas et al., 2020, Data re-uploading for a universal quantum classifier](https://quantum-journal.org/papers/q-2020-02-06-226/?utm_source=researcher_app&utm_medium=referral&utm_campaign=RESR_MRKT_Researcher_inbound) | ||
| - [ ] [Poland, Beer and Osborne, 2020, No Free Lunch for Quantum Machine Learning](https://arxiv.org/pdf/2003.14103.pdf) | ||
| - [ ] [Schuld, Sweke, Meyer, 2020, The effect of data encoding on the expressive power of variational quantum machine learning models](https://arxiv.org/abs/2008.08605) | ||
| - [ ] [Tang et al., 2020, CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations](https://arxiv.org/abs/2012.02333) | ||
| - [ ] [Wang et al., 2020, Noise-Induced Barren Plateaus in Variational Quantum Algorithms](https://arxiv.org/pdf/2007.14384.pdf) | ||
| - [ ] [Xia et al., 2020, Quantum-enhanced data classification with a variational entangled sensor network](https://arxiv.org/abs/2006.11962) | ||
| - [ ] [Zhang & Ni, 2020, Recent Advances in Quantum Machine Learning](https://eprints.lancs.ac.uk/id/eprint/154554/1/QML_survey.pdf) | ||
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| #### 2019 | ||
| - [ ] [Benedetti et al., 2019, Parameterized quantum circuits as machine learning models](https://iopscience.iop.org/article/10.1088/2058-9565/ab4eb5/meta) | ||
| - [ ] [Havlicek et al., 2019, Supervised learning with quantum enhanced feature spaces](https://arxiv.org/abs/1804.11326) | ||
| - [ ] [Orus, Mugel, Lizaso, 2019, Quantum computing for finance: Overview and prospects](https://reader.elsevier.com/reader/sd/pii/S2405428318300571?token=28567476E673DC1C0822AC2F1154825443428F74A17965BB0E4D30561A2E7C12D38E491BF32236FCE86B36A40EF401FC&originRegion=us-east-1&originCreation=20220629022528) | ||
| - [ ] [Tacchino et al., 2019, An artificial neuron implemented on an actual quantum processor](https://www.nature.com/articles/s41534-019-0140-4.pdf) | ||
| - [ ] [Verdon et al., 2019, Learning to learn with quantum neural networks via classical neural networks](https://arxiv.org/abs/1907.05415) | ||
| - [ ] [Verdon et al. 2019, Quantum Graph Neural Networks](https://arxiv.org/abs/1909.12264) | ||
| - [ ] [Wang et al., 2019, Quantized Generative Adversarial Network](https://arxiv.org/abs/1901.08263) | ||
| - [ ] [Zoufal, Lucchi and Werner, 2019, Quantum Generative Adversarial Networks for learning and loading random distributions](https://www.nature.com/articles/s41534-019-0223-2) | ||
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| #### 2018 | ||
| - [ ] [Bergholm et al., 2018, PennyLane: Automatic differentiation of hybrid quantum-classical computations](https://arxiv.org/abs/1811.04968) | ||
| - [ ] [Cao et al., 2022, Quantum Chemistry in the Age of Quantum Computing](https://arxiv.org/abs/1812.09976) | ||
| - [ ] [Cortese & Braje, 2018, Loading Classical Data into a Quantum Computer](https://arxiv.org/pdf/1803.01958.pdf) | ||
| - [ ] [Kopczyk, 2018, Quantum Machine Learning for data scientists](https://arxiv.org/abs/1804.10068) | ||
| - [ ] [Schuld & Killoran, 2018, Quantum machine learning in feature Hilbert spaces](https://arxiv.org/abs/1803.07128) | ||
| - [ ] [Zhao et al., 2018, Bayesian Deep Learning on a Quantum Computer](https://arxiv.org/pdf/1806.11463.pdf)[GitHub](https://gitlab.com/apozas/bayesian-dl-quantum) | ||
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| #### 2007-2017 | ||
| - [ ] [Arunachalam & de Wolf, 2017, A Survey of Quantum Learning Theory](https://arxiv.org/abs/1701.06806) | ||
| - [ ] [Cao, Guerreschi, Aspuru-Guzik, 2017, Quantum Neuron: an elementary building block for machine learning on quantum computers](https://arxiv.org/abs/1711.11240)[Github](https://github.com/inJeans/qnn) | ||
| - [ ] [Dunjko & Briegel, 2017, Machine learning & artificial intelligence in the quantum domain](https://arxiv.org/pdf/1709.02779.pdf) | ||
| - [ ] [Liu & Rebentrost, 2017, Quantum machine learning for quantum anomaly detection](https://arxiv.org/abs/1710.07405) | ||
| - [ ] [Otterbach et al., 2017, Unsupervised Machine Learning on a Hybrid Quantum Computer](https://arxiv.org/abs/1712.05771)[GitHub](https://github.com/BOHRTECHNOLOGY/quantum_tsp) | ||
| - [ ] [Perdomo-Ortiz et al. 2017, Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers](https://arxiv.org/abs/1708.09757) | ||
| - [ ] [Biamonte et al., 2016, Quantum machine Learning](https://arxiv.org/abs/1611.09347) | ||
| - [ ] [Montanaro 2016, Quantum algorithms: an overview](https://www.nature.com/articles/npjqi201523.pdf) | ||
| - [ ] [Aaronson, 2015, Quantum Machine Learning Algorithms: Read the Fine Print](https://www.scottaaronson.com/papers/qml.pdf) | ||
| - [ ] [Fahri & Goldstone, 2014, Quantum Approximate Optimization Algorithms](https://arxiv.org/pdf/1411.4028.pdf) | ||
| - [ ] [Schuld, Synayskly and Petruccione, 2014, The quest for a Quantum Neural Network](https://arxiv.org/pdf/1408.7005.pdf) | ||
| - [ ] [Schuld, Synayskly and Petruccione, 2014, Simulating a perceptron on a quantum computer](https://arxiv.org/abs/1412.3635) | ||
| - [ ] [Schuld, Synayskly and Petruccione, 2014, An introduction to quantum machine learning](https://arxiv.org/abs/1409.3097) | ||
| - [ ] [Wittek, 2014, Quantum Machine Learning: What Quantum Computing Means to Data Mining](https://www.researchgate.net/profile/Peter-Wittek/publication/264825604_Quantum_Machine_Learning_What_Quantum_Computing_Means_to_Data_Mining/links/5ababcfba6fdcc71647085db/Quantum-Machine-Learning-What-Quantum-Computing-Means-to-Data-Mining.pdf) | ||
| - [ ] [Llyod, Mohseni, Rebentrost, 2013, Quantum algorithms for supervised and unsupervised machine learning](https://arxiv.org/abs/1307.0411) | ||
| - [ ] [Sgarbas, 2007, The road to Quantum Artificial Intelligence](https://arxiv.org/pdf/0705.3360.pdf) | ||
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| --- | ||
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| ### Quantum Workforce | ||
| #### 2022 | ||
| - [ ] [Kaur & Venegas-Gomez, 2022, Defining the quantum workforce landscape: a review of global quantum education initiatives](https://arxiv.org/pdf/2202.08940.pdf) | ||
| - [ ] [Peron et al., 2022, Quantum Undergraduate Education and Scientific Training](https://arxiv.org/abs/2109.13850) | ||
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| #### 2021 | ||
| - [ ] [Asfaw et al., 2022, Building a Quantum Engineering Undergraduate Program](https://arxiv.org/pdf/2108.01311.pdf) | ||
| - [ ] [Dzurak et al., 2021, Development of an Undergraduate Quantum Engineering Degree](https://arxiv.org/pdf/2110.12598.pdf) | ||
| - [ ] [Ozhigov 2021, Quantum computations (course of lectures)](https://arxiv.org/pdf/2107.08047.pdf) | ||
| - [ ] [Siddhu & Tayur, 2021, Five Starter Pieces: Quantum Information Science via Semi-definite Programs](https://arxiv.org/pdf/2112.08276.pdf) | ||
| - [ ] [Tang et al., 2021, Teaching quantum information technologies and a practical module for online and offline undergraduate students](https://arxiv.org/abs/2112.06548) | ||
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| --- | ||
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| ### Blogs | ||
| - [ ] [IEEE Spectrum, 2022, Quantum Error Correction](https://spectrum.ieee.org/quantum-error-correction) | ||
| - [x] [Qiskit medium, 2022, We are releasing a free hands-on quantum machine learning course online](https://medium.com/qiskit/were-releasing-a-free-hands-on-quantum-machine-learning-course-online-c9313e78ea2d) | ||
| - [ ] [Schuetz & Brubaker & Katzgraber, 2022, Combinatorial Optimization with Physics-Inspired Graph Neural Networks, Amazon Braket](https://aws.amazon.com/blogs/quantum-computing/combinatorial-optimization-with-physics-inspired-graph-neural-networks/) | ||
| - [ ] [Albornoz, 2021, How to QML, Pennylane](https://pennylane.ai/blog/2021/10/how-to-start-learning-quantum-machine-learning/) | ||
| - [ ] [Ceroni, 2021, The Quantum Graph Recurrent Neural Network, Pennylane](https://pennylane.ai/qml/demos/tutorial_qgrnn.html) | ||
| - [ ] [Google AI Blog, 2021, Quantum Machine Learning and the Power of Data](http://ai.googleblog.com/2021/06/quantum-machine-learning-and-power-of.html "Quantum Machine Learning and the Power of Data") | ||
| - [ ] [Dunjko et al., 2020, A non-review of Quantum Machine Learning: trends and explorations](https://quantum-journal.org/views/qv-2020-03-17-32/) | ||
| - [ ] [Qunasys, Accelerating variational quantum algorithms](https://qunasys.medium.com/accelerating-variational-quantum-algorithms-147b9bf02dc0) | ||
| - [ ] [What is quantum CNN?](https://analyticsindiamag.com/what-is-a-quantum-convolutional-neural-network/) | ||
| - [ ] [IBM quantum research, At what cost can we simulate large quantum circuit on small quantum computers](https://research.ibm.com/blog/circuit-knitting-with-classical-communication) | ||
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| --- | ||
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| ### Conferences | ||
| - [ ] [Quantum Google AI, 2022, Quantum Summer Symposium](https://www.youtube.com/playlist?list=PLpO2pyKisOjLmyDOYwa8akgOHnCkXrKFg) | ||
| - [ ] [QPL 2022, Quantum Physics and Logic](https://m.youtube.com/playlist?list=PLRW1t_lfNuYNRNgWnfUGwKhhWfIi2EpLe) | ||
| - [ ] [QTML 2021](https://www.youtube.com/watch?v=meTsqSkNLKI&list=PLaEuBnOE7AzNoNoSWgxd594PzCpJA6cGz&index=1) | ||
| - [ ] [Ijaz, An introduction to Quantum Machine Learning](https://www.youtube.com/watch?v=-DWng3jyBIM) | ||
| - [ ] [Schuld, 2020, Quantum Machine Learning](https://www.youtube.com/watch?v=C_lBYKV_pJo) | ||
| - [ ] [Schuld, 2020, QUantum Machine Learning and Pennylane](https://www.youtube.com/watch?v=pe1d0RyCNxY) | ||
| - [ ] [Wittek, 2015, What Can We Expect from Quantum Machine Learning?](https://www.youtube.com/watch?v=EKWGLERVLuc) | ||
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| --- | ||
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| ### MOOC | ||
| - [ ] [Preskill, 2022, PH219, Quantum Computing](http://theory.caltech.edu/~preskill/ph219/ph219_2022.html) | ||
| - [ ] [Peter Wittek, 2019, QML](https://www.youtube.com/playlist?list=PLmRxgFnCIhaMgvot-Xuym_hn69lmzIokg) | ||
| - [ ] [Qiskit, 2022, QML](https://qiskit.org/learn/course/machine-learning-course) | ||
| - [ ] [Qiskit, 2021, Quantum Machine Learning | 2021 Qiskit Global Summer School](https://www.youtube.com/playlist?list=PLOFEBzvs-VvqJwybFxkTiDzhf5E11p8BI) | ||
| - [ ] [Pennylane, QML](https://pennylane.ai/qml/index.html) | ||
| - [ ] [Xanadu, Codebook](https://codebook.xanadu.ai/) | ||
| - [ ] [CERN, Elias Fernandez-Combarro Alvarez, "A practical introduction to quantum computing: from qubits to quantum machine learning and beyond" 7 lectures](https://indico.cern.ch/category/12909/) | ||
| - [ ] [Llyod, 2016, Quantum Machine Learning](https://www.youtube.com/watch?v=Lbndu5EIWvI&t=3009s) | ||
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| --- | ||
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| ### Libraries | ||
| - [ ] [Qiskit](https://www.qiskit.org) | ||
| - [ ] [Pennylane](https://pennylane.ai) | ||
| - [ ] [Lambeq](https://github.com/CQCL/lambeq) | ||
| - [ ] [Forest](https://github.com/rigetti/forest-software) | ||
| - [ ] [Tensorflow-quantum](https://www.tensorflow.org/quantum) | ||
| - [ ] [Braket](https://github.com/aws/amazon-braket-sdk-python) | ||
| - [ ] [Cirq](https://quantumai.google/cirq) | ||
| - [ ] [Ocean](https://github.com/dwavesystems/dwave-ocean-sdk) | ||
| - [ ] [Strawberry Fields](https://github.com/xanaduai/strawberryfields) | ||
| - [ ] [Q#](https://azure.microsoft.com/en-ca/resources/development-kit/quantum-computing/) | ||
| - [ ] [OpenQAOA](https://arxiv.org/pdf/2210.08695.pdf) | ||
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| --- | ||
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| ### IBM List of papers | ||
| [IBM, Qiskit papers](https://airtable.com/shr5QnbLgraHRPx35/tblqDKDgMVdH6YGSE) | ||
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