From df85c8b824604286bc1e02c5d0107f305e8bc9db Mon Sep 17 00:00:00 2001 From: Christophe Pere Date: Fri, 25 Nov 2022 16:24:59 -0500 Subject: [PATCH] creation of 2019, 18, 07-17 --- 2007-2017/2007-2017.md | 22 +++++++ 2018/2018.md | 12 ++++ 2019/2019.md | 15 +++++ 2020/2020.md | 28 +++++++++ 2021/2021.md | 137 ----------------------------------------- README.md | 65 ++++--------------- 6 files changed, 89 insertions(+), 190 deletions(-) create mode 100644 2007-2017/2007-2017.md create mode 100644 2018/2018.md create mode 100644 2019/2019.md create mode 100644 2020/2020.md diff --git a/2007-2017/2007-2017.md b/2007-2017/2007-2017.md new file mode 100644 index 0000000..0e1b45c --- /dev/null +++ b/2007-2017/2007-2017.md @@ -0,0 +1,22 @@ +# What papers were published from 2007 to 2017 in QML? + + +### Papers +#### 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) diff --git a/2018/2018.md b/2018/2018.md new file mode 100644 index 0000000..852529c --- /dev/null +++ b/2018/2018.md @@ -0,0 +1,12 @@ +# What papers were published in 2018 in QML? + + +### Papers +#### 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) diff --git a/2019/2019.md b/2019/2019.md new file mode 100644 index 0000000..2222a6c --- /dev/null +++ b/2019/2019.md @@ -0,0 +1,15 @@ +# What papers were published in 2019 in QML? + + +### Papers +#### 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) + diff --git a/2020/2020.md b/2020/2020.md new file mode 100644 index 0000000..4a411aa --- /dev/null +++ b/2020/2020.md @@ -0,0 +1,28 @@ +# What papers were published in 2020 in QML? + +### Papers +#### 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) + diff --git a/2021/2021.md b/2021/2021.md index 8bcdf60..757dbc0 100644 --- a/2021/2021.md +++ b/2021/2021.md @@ -46,140 +46,3 @@ chemistry on near-term quantum computers](https://www.nature.com/articles/s41534 - [ ] [Yarkoni et al., 2021, Quantum Annealing for Industry Applications: Introduction and Review](https://arxiv.org/abs/2112.07491) -#### 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) - -#### 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) - -#### 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) - -#### 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) - ---- - -### 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) - -#### 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) - ---- - -### 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) - - - - - ---- - -### 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) - ---- - -### 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) - ---- - -### 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) - ---- - -### IBM List of papers -[IBM, Qiskit papers](https://airtable.com/shr5QnbLgraHRPx35/tblqDKDgMVdH6YGSE) - - diff --git a/README.md b/README.md index 02984d5..d9e7061 100644 --- a/README.md +++ b/README.md @@ -34,65 +34,24 @@ This repository will contain the major papers, books and blog posts on QML #### 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) + +- [List of all papers from 2020](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2020/2020.md) + #### 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) + +- [List of all papers from 2019](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2019/2019.md) + #### 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) + +- [List of all papers from 2018](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2018/2018.md) + #### 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) + + +- [List of all papers from 2007 to 2017](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2007-2017/2007-2017.md) ---