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https://github.com/Christophe-pere/Roadmap-to-QML

This repository will contain the major papers, books and blog posts on QML
https://github.com/Christophe-pere/Roadmap-to-QML

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This repository will contain the major papers, books and blog posts on QML

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# Roadmap-to-QML

This repository will contain the major papers, books and blog posts on QML 



---

## Content

- [Quantum Machine Learning](#quantum-machine-learning)

  - [Books](#books)

  - [Papers](#papers)

  - [Blogs](#blogs)

  - [Conferences](#conferences)

  - [MOOC](#moocs)

  - [Quantum Libraries](#libraries)



---

## Quantum Machine Learning

### Books

- [ ] [Combarro & González-Castillo, 2023, A Practical Guide to Quantum Machine Learning and Quantum Optimization, Packt Publishing](https://www.packtpub.com/product/a-practical-guide-to-quantum-machine-learning-and-quantum-optimization/9781804613832)

- [ ] [Hidary, 2022, Quantum Computing: An Applied Approach, 2nd edition](https://github.com/JackHidary/quantumcomputingbook)

- [ ] [Schuld & Petruccione, 2022, Machine Learning with Quantum Computers](https://link.springer.com/book/10.1007/978-3-030-83098-4)

- [ ] [Wong, 2022. Introduction to classical and quantum computing](https://www.thomaswong.net/introduction-to-classical-and-quantum-computing-1e3p.pdf)

- [ ] [Pattanyak, 2021, Quantum Machine Learning with Python](https://link.springer.com/book/10.1007/978-1-4842-6522-2)[GitHub](https://github.com/Apress/quantum-machine-learning-python)

- [ ] [Ganguly, 2021, Quantum Machine Learning: An Applied Approach](https://link.springer.com/book/10.1007/978-1-4842-7098-1?noAccess=true)

- [ ] [Zickert, 2021, Hands-On Quantum Machine Learning, Vol-1](https://github.com/quantum-machine-learning/Hands-On-Quantum-Machine-Learning-With-Python-Vol-1)



---

### Reviews



- [List of reviews](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/Reviews/Reviews.md)



### Papers



#### 2024 



- [List of all papers from 2024](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2024/2024.md)



#### 2023 



- [List of all papers from 2023](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2023/2023.md)



#### 2022



- [List of all papers from 2022](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2022/2022.md)



#### 2021



- [List of all papers from 2021](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2021/2021.md)



#### 2020



- [List of all papers from 2020](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2020/2020.md)



#### 2019



- [List of all papers from 2019](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2019/2019.md)



#### 2018



- [List of all papers from 2018](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2018/2018.md)



#### 2007-2017



- [List of all papers from 2007 to 2017](https://github.com/Christophe-pere/Roadmap-to-QML/blob/main/2007-2017/2007-2017.md)



---



### Quantum Workforce 



#### 2024 



- [De Maio et al., 2024, Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing](https://arxiv.org/pdf/2403.00885.pdf)

- [Rönkkö et al., 2024, On-Premises Superconducting Quantum Computer for Education and Research](https://arxiv.org/pdf/2402.07315)

- [Meyer et al., 2024, Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks](https://arxiv.org/pdf/2404.06314)

- [Rosenberg & Holincheck & Colandene, 2024, Science, Technology, Engineering, and Mathematics Undergraduates’ Knowledge and Interest in Quantum Careers: Barriers and Opportunities to Building a Diverse Quantum Workforce](https://arxiv.org/pdf/2404.03439.pdf)



#### 2023 

- [ ] [Dündar-Coecke et al., 2023, Quantum Picturalism: Learning Quantum Theory in High School](https://arxiv.org/pdf/2312.03653)

- [ ] [Freericks & Doughty, 2023, Should we trade off higher-level mathematics for abstraction to improve student understanding of quantum mechanics?](https://arxiv.org/pdf/2305.00062.pdf)

- [ ] [Goorney et al., 2023, The Quantum Curriculum Transformation Framework for the development of Quantum Information Science and Technology Education](https://arxiv.org/pdf/2308.10371)

- [ ] [De Luca & Chen, 2023, Teaching Quantum Machine Learning in Computer Science](https://ieeexplore.ieee.org/abstract/document/10092171)

- [ ] [Melnikov et al., 2023, Quantum Machine Learning: from physics to software engineering](https://arxiv.org/pdf/2301.01851.pdf) 



#### 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

- [ ] [Baker, 2023, Quantum detection of time series anomalies](https://pennylane.ai/qml/demos/tutorial_univariate_qvr.html)

- [ ] [Draškić, 2023, How to run big quantum circuits on small quantum computers in PennyLane](https://pennylane.ai/blog/2023/01/how-to-run-big-quantum-circuits-on-small-quantum-computers-in-pennylane/)

- [ ] [East, 2022, Introduction to Geometric Quantum Machine Learning](https://pennylane.ai/qml/demos/tutorial_geometric_qml.html)

- [ ] [Schuld 2022, Why measuring performance is our biggest blind spot in quantum machine learning](https://pennylane.ai/blog/2022/03/why-measuring-performance-is-our-biggest-blind-spot-in-quantum-machine-learning/)

- [ ] [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)

- [ ] [Derks et al., 2021, Training and evaluating quantum kernels](https://pennylane.ai/qml/demos/tutorial_kernels_module.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

- [ ] [2023 Mathematical Aspects of Quantum Learning Workshop, IPAM](https://www.youtube.com/playlist?list=PLHyI3Fbmv0SckwZK0xfc7itiq9nLWJeUF)

- [ ] [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, Quantum simulation](https://qiskit.org/learn/course/machine-learning-course)

- [ ] [Qiskit, 2021, Quantum Machine Learning](https://www.youtube.com/playlist?list=PLOFEBzvs-VvqJwybFxkTiDzhf5E11p8BI)

- [ ] [Qiskit, 2020, Quantum computing and Quantum Hardware](https://qiskit.org/learn/summer-school/introduction-to-quantum-computing-and-quantum-hardware-2020/)

- [ ] [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)

- [ ] [sQUlearn, Kremlin et al., 2023, A Python Library for Quantum Machine Learning](https://arxiv.org/pdf/2311.08990)

- [ ] [Qadence, Pasqal](https://github.com/pasqal-io/qadence)[Qadence paper](https://arxiv.org/pdf/2401.09915)

- [ ] [Wu et al., 2024, The Cytnx Library for Tensor Networks](https://arxiv.org/pdf/2401.01921.pdf)



---



### IBM List of papers

[IBM, Qiskit papers](https://airtable.com/shr5QnbLgraHRPx35/tblqDKDgMVdH6YGSE)