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Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
“Reinforcement Learning: An Introduction”
R.S. Sutton and A.G. Barto · 1998
Earlier work this paper cites.
“Integration with Respect to the Haar Measure on Unitary, Orthogonal and Symplectic Group”
Benoît Collins and Piotr Śniady · 2006
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“Quantum Computation and Quantum Information: 10th Anniversary Edition”
Michael A. Nielsen and Isaac L. Chuang · 2012
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“Deep Direct Reinforcement Learning for Financial Signal Representation and Trading”
Yue Deng, Feng Bao, Youyong Kong, Zhiquan Ren, and Qionghai Dai · 2016
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“Efficient classical simulation of matchgate circuits with generalized inputs and measurements”
Daniel J. Brod · 2016
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“Deep Reinforcement Learning: A Brief Survey”
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath · 2017
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“Attention is all you need”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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“A distributional perspective on reinforcement learning”
Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
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“Empirical Asset Pricing Via Machine Learning”
Shihao Gu, Bryan T. Kelly, and Dacheng Xiu · 2018
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“Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model” (2018)
Hyeong Kyu Choi · 2018
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“Quantum computational finance: Monte Carlo pricing of financial derivatives”
Patrick Rebentrost, Brajesh Gupt, and Thomas R. Bromley · 2018
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“Barren plateaus in quantum neural network training landscapes”
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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“Quantum circuit learning”
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii · 2018
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“Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning” (2018)
Jiayao Zhang, Guangxu Zhu, Robert W. Heath Jr., and Kaibin Huang · 2018
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“Quantum gradient estimation and its application to quantum reinforcement learning”
Arjan Cornelissen · 2018
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“Distributional Reinforcement Learning With Quantile Regression”
Will Dabney, Mark Rowland, Marc Bellemare, and Rémi Munos · 2018
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“Deep hedging”
Hans Buehler, Lukas Gonon, Joseph Teichmann, and Ben Wood · 2019
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“Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning”
Hans Buehler, Lukas Gonon, Josef Teichmann, Ben Wood, Baranidharan Mohan, and Jonathan Kochems · 2019
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“Stock Market Prediction Based on Generative Adversarial Network”
Kang Zhang, Guoqiang Zhong, Junyu Dong, Shengke Wang, and Yong Wang · 2019
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“The Power of Block-Encoded Matrix Powers: Improved Regression Techniques via Faster Hamiltonian Simulation”
Shantanav Chakraborty, András Gilyén, and Stacey Jeffery · 2019
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“Optimizing quantum optimization algorithms via faster quantum gradient computation”
András Gilyén, Srinivasan Arunachalam, and Nathan Wiebe · 2019
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“Quantum Algorithms for Portfolio Optimization”
Iordanis Kerenidis, Anupam Prakash, and Dániel Szilágyi · 2019
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“Orthogonal Deep Neural Networks”
Shuai Li, Kui Jia, Yuxin Wen, Tongliang Liu, and Dacheng Tao · 2019
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“A Comparative Analysis of Expected and Distributional Reinforcement Learning”
Clare Lyle, Marc G. Bellemare, and Pablo Samuel Castro · 2019
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“Deep Hedging: Learning to Simulate Equity Option Markets”
Magnus Wiese, Lianjun Bai, Ben Wood, and Hans Buehler · 2019
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“A generative modeling approach for benchmarking and training shallow quantum circuits”
Marcello Benedetti, Delfina Garcia-Pintos, Oscar Perdomo, Vicente Leyton-Ortega, Yunseong Nam, and Alejandro Perdomo-Ortiz · 2019
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“Supervised learning with quantum-enhanced feature spaces”
Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta · 2019
Earlier work this paper cites.
“Evaluating analytic gradients on quantum hardware”
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
Cited alongside, same era.
“Pagan: Portfolio Analysis with Generative Adversarial Networks”
Yada Zhu, Giovanni Mariani, and Jianbo Li · 2020
Cited alongside, same era.
“A game plan for quantum computing”
Alexandre Ménard, Ivan Ostojic, Mark Patel, and Daniel Volz · 2020
Cited alongside, same era.
“Enhancing Explainability of Neural Networks Through Architecture Constraints”
Zebin Yang, Aijun Zhang, and Agus Sudjianto · 2020
Cited alongside, same era.
“Classification with Quantum Neural Networks on Near Term Processors”
Edward Farhi and Hartmut Neven · 2020
Cited alongside, same era.
“Data re-uploading for a universal quantum classifier”
Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre · 2020
“Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer”
Pradeep Niroula, Ruslan Shaydulin, Romina Yalovetzky, Pierre Minssen, Dylan Herman, Shaohan Hu, and Marco Pistoia · 2022
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“A survey of quantum computing for finance” (2022)
Dylan Herman, Cody Googin, Xiaoyuan Liu, Alexey Galda, Ilya Safro, Yue Sun, Marco Pistoia, and Yuri Alexeev · 2022
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“Classical and Quantum Algorithms for Orthogonal Neural Networks” (2022)
Iordanis Kerenidis, Jonas Landman, and Natansh Mathur · 2022
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“Discovering faster matrix multiplication algorithms with reinforcement learning”
Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis, and Pushmeet Kohli · 2022
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https://www.quantinuum.com/ (2022)
“Quantinuum H1-1, H1-2” · 2022
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Cited alongside, same era.
“Input Redundancy for Parameterized Quantum Circuits”
Francisco Javier Gil Vidal and Dirk Oliver Theis · 2020
Cited alongside, same era.
“A method for loading classical data into quantum states for applications in machine learning and optimization”
Iordanis Kerenidis · 2020
Cited alongside, same era.
“Variational Quantum Circuits for Deep Reinforcement Learning”
Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, and Hsi-Sheng Goan · 2020
Cited alongside, same era.
“Reinforcement Learning with Quantum Variational Circuit”
Owen Lockwood and Mei Si · 2020
Cited alongside, same era.
“Pseudo-dimension of quantum circuits”
Matthias C. Caro and Ishaun Datta · 2020
Cited alongside, same era.
“Deep Reinforcement Learning for Algorithmic Trading”
Álvaro Cartea, Sebastian Jaimungal, and Leandro Sánchez-Betancourt · 2021
Cited alongside, same era.
“Fermion sampling: A robust quantum computational advantage scheme using fermionic linear optics and magic input states”
Michał Oszmaniec, Ninnat Dangniam, Mauro E.S. Morales, and Zoltán Zimborás · 2022
Later among the works it cites.
“Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures” (2022)
Hans Buehler, Phillip Murray, Mikko S. Pakkanen, and Ben Wood · 2022
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“Deep hedging: Continuous reinforcement learning for hedging of general portfolios across multiple risk aversions”
Phillip Murray, Ben Wood, Hans Buehler, Magnus Wiese, and Mikko Pakkanen · 2022
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“Quantum Methods for Neural Networks and Application to Medical Image Classification”
Jonas Landman, Natansh Mathur, Yun Yvonna Li, Martin Strahm, Skander Kazdaghli, Anupam Prakash, and Iordanis Kerenidis · 2022
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“A Survey on Quantum Reinforcement Learning” (2022)
Nico Meyer, Christian Ufrecht, Maniraman Periyasamy, Daniel D. Scherer, Axel Plinge, and Christopher Mutschler · 2022
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“Quantum Vision Transformers” (2022)
El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur, Jonas Landman, Martin Strahm, and Yun Yvonna Li · 2022
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“Quantum machine learning with subspace states” (2022)
Iordanis Kerenidis and Anupam Prakash · 2022
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“Group-Invariant Quantum Machine Learning”
Martin Larocca, Frédéric Sauvage, Faris M. Sbahi, Guillaume Verdon, Patrick J. Coles, and M. Cerezo · 2022
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“A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers” (2022)
Xuchen You, Shouvanik Chakrabarti, and Xiaodi Wu · 2022
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“Diagnosing Barren Plateaus with Tools from Quantum Optimal Control”
Martin Larocca, Piotr Czarnik, Kunal Sharma, Gopikrishnan Muraleedharan, Patrick J. Coles, and Marco Cerezo · 2022
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“Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits” (2022)
Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, and Dacheng Tao · 2022
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“Unentangled quantum reinforcement learning agents in the OpenAI Gym” (2022)
Jen-Yueh Hsiao, Yuxuan Du, Wei-Yin Chiang, Min-Hsiu Hsieh, and Hsi-Sheng Goan · 2022
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“Quantum policy gradient algorithms” (2022)
Sofiene Jerbi, Arjan Cornelissen, Māris Ozols, and Vedran Dunjko · 2022
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“Quantum Computing Methods for Supply Chain Management”
Hansheng Jiang, Zuo-Jun Max Shen, and Junyu Liu · 2022
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“Deep Bellman Hedging”
Hans Buehler, Murray Phillip, and Ben Wood · 2022
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“Lie-algebraic classical simulations for variational quantum computing” (2023)
Matthew L. Goh, Martin Larocca, Lukasz Cincio, M. Cerezo, and Frédéric Sauvage · 2023
Closest in time.
“Expressivity of Variational Quantum Machine Learning on the Boolean Cube” (2022)
Dylan Herman, Rudy Raymond, Muyuan Li, Nicolas Robles, Antonio Mezzacapo, and Marco Pistoia · 2023
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“The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ansätze” (2023)
Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti, Niraj Kumar, Romina Yalovetzky, Jamie Heredge, Shree Hari Sureshbabu, and Marco Pistoia · 2023
Closest in time.
“A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits” (2023)
Michael Ragone, Bojko N. Bakalov, Frédéric Sauvage, Alexander F. Kemper, Carlos Ortiz Marrero, Martin Larocca, and M. Cerezo · 2023
Closest in time.
“Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning” (2023)
Léo Monbroussou, Jonas Landman, Alex B. Grilo, Romain Kukla, and Elham Kashefi · 2023
Closest in time.
“Quantum reinforcement learning via policy iteration”
El Amine Cherrat, Iordanis Kerenidis, and Anupam Prakash · 2023
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