2000 character limit reached
A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search (2404.06174v3)
Published 9 Apr 2024 in quant-ph
Abstract: In the field of quantum computing, variational quantum algorithms (VQAs) represent a pivotal category of quantum solutions across a broad spectrum of applications. These algorithms demonstrate significant potential for realising quantum computational advantage. A fundamental aspect of VQAs involves formulating expressive and efficient quantum circuits (namely ansatz), and automating the search of such ansatz is known as quantum architecture search (QAS). Recently, reinforcement learning (RL) techniques is utilized to automate the search for ansatzes, known as RL-QAS. This study investigates RL-QAS for crafting ansatz tailored to the variational quantum state diagonalisation problem. Our investigation includes a comprehensive analysis of various dimensions, such as the entanglement thresholds of the resultant states, the impact of initial conditions on the performance of RL-agent, the phase transition behaviour of correlation in concurrence bounds, and the discrete contributions of qubits in deducing eigenvalues through conditional entropy metrics. We leverage these insights to devise an entanglement-guided admissible ansatz in QAS to diagonalise random quantum states using optimal resources. Furthermore, the methodologies presented herein offer a generalised framework for constructing reward functions within RL-QAS applicable to variational quantum algorithms.
- Aram W Harrow and Ashley Montanaro, “Quantum computational supremacy,” Nature 549, 203–209 (2017).
- Ashley Montanaro, “Quantum algorithms: an overview,” npj Quantum Information 2, 1–8 (2016).
- Abhishek Sadhu, Meghana Ayyala Somayajula, Karol Horodecki, and Siddhartha Das, “Practical limitations on robustness and scalability of quantum internet,” arXiv preprint arXiv:2308.12739 (2023).
- Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, and Hong Yao, “Differentiable quantum architecture search,” Quantum Science and Technology 7, 045023 (2022).
- Yuxuan Du, Tao Huang, Shan You, Min-Hsiu Hsieh, and Dacheng Tao, “Quantum circuit architecture search for variational quantum algorithms,” npj Quantum Information 8, 62 (2022).
- En-Jui Kuo, Yao-Lung L Fang, and Samuel Yen-Chi Chen, “Quantum architecture search via deep reinforcement learning,” arXiv preprint arXiv:2104.07715 (2021).
- Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, and Hong Yao, “Neural predictor based quantum architecture search,” Machine Learning: Science and Technology 2, 045027 (2021).
- Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics 18, 023023 (2016).
- Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al., “Variational quantum algorithms,” Nature Reviews Physics 3, 625–644 (2021).
- Aritra Sarkar, “Automated quantum software engineering: why? what? how?” arXiv e-prints , arXiv–2212 (2022).
- Mateusz Ostaszewski, Lea M Trenkwalder, Wojciech Masarczyk, Eleanor Scerri, and Vedran Dunjko, “Reinforcement learning for optimization of variational quantum circuit architectures,” Advances in Neural Information Processing Systems 34, 18182–18194 (2021).
- Akash Kundu, Przemysław Bedełek, Mateusz Ostaszewski, Onur Danaci, Yash J Patel, Vedran Dunjko, and Jarosław A Miszczak, “Enhancing variational quantum state diagonalization using reinforcement learning techniques,” New Journal of Physics 26, 013034 (2024).
- Yash J Patel, Akash Kundu, Mateusz Ostaszewski, Xavier Bonet-Monroig, Vedran Dunjko, and Onur Danaci, “Curriculum reinforcement learning for quantum architecture search under hardware errors,” arXiv preprint arXiv:2402.03500 (2024).
- Richard S Sutton and Andrew G Barto, Reinforcement learning: An introduction (MIT press, 2018).
- Wenjie Wu, Ge Yan, Xudong Lu, Kaisen Pan, and Junchi Yan, “Quantumdarts: differentiable quantum architecture search for variational quantum algorithms,” in International Conference on Machine Learning (PMLR, 2023) pp. 37745–37764.
- Akash Kundu, “Reinforcement learning-assisted quantum architecture search for variational quantum algorithms,” arXiv preprint arXiv:2402.13754 (2024).
- Ryan LaRose, Arkin Tikku, Étude O’Neel-Judy, Lukasz Cincio, and Patrick J Coles, “Variational quantum state diagonalization,” npj Quantum Information 5, 57 (2019).
- Kok Chuan Tan and Tyler Volkoff, “Variational quantum algorithms to estimate rank, quantum entropies, fidelity, and fisher information via purity minimization,” Physical Review Research 3, 033251 (2021).
- Marco Cerezo, Alexander Poremba, Lukasz Cincio, and Patrick J Coles, “Variational quantum fidelity estimation,” Quantum 4, 248 (2020).
- Akash Kundu and Jarosław Adam Miszczak, “Variational certification of quantum devices,” Quantum Science and Technology 7, 045017 (2022).
- Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M Chow, and Jay M Gambetta, “Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,” nature 549, 242–246 (2017).
- Hasan Mustafa, Sai Nandan Morapakula, Prateek Jain, and Srinjoy Ganguly, “Variational quantum algorithms for chemical simulation and drug discovery,” in 2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) (IEEE, 2022) pp. 1–8.
- Alain Delgado, Juan Miguel Arrazola, Soran Jahangiri, Zeyue Niu, Josh Izaac, Chase Roberts, and Nathan Killoran, “Variational quantum algorithm for molecular geometry optimization,” Physical Review A 104, 052402 (2021).
- Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, and Prasanna Balaprakash, “Learning to optimize variational quantum circuits to solve combinatorial problems,” in Proceedings of the AAAI conference on artificial intelligence, Vol. 34 (2020) pp. 2367–2375.
- Xiaoyuan Liu, Anthony Angone, Ruslan Shaydulin, Ilya Safro, Yuri Alexeev, and Lukasz Cincio, “Layer vqe: A variational approach for combinatorial optimization on noisy quantum computers,” IEEE Transactions on Quantum Engineering 3, 1–20 (2022).
- Adam Glos, Aleksandra Krawiec, and Zoltán Zimborás, “Space-efficient binary optimization for variational quantum computing,” npj Quantum Information 8, 39 (2022).
- Mark M Wilde, Quantum information theory (Cambridge university press, 2013).
- Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost, “Quantum principal component analysis,” Nature Physics 10, 631–633 (2014).
- Sam A. Hill and William K. Wootters, “Entanglement of a pair of quantum bits,” Phys. Rev. Lett. 78, 5022–5025 (1997).
- William K. Wootters, “Entanglement of formation of an arbitrary state of two qubits,” Phys. Rev. Lett. 80, 2245–2248 (1998).
- Joseph Lee Rodgers and W Alan Nicewander, “Thirteen ways to look at the correlation coefficient,” The American Statistician 42, 59–66 (1988).
- N. J. Cerf and C. Adami, “Negative entropy and information in quantum mechanics,” Phys. Rev. Lett. 79, 5194–5197 (1997).
- N. J. Cerf and C. Adami, “Quantum extension of conditional probability,” Phys. Rev. A 60, 893–897 (1999).
- Ryszard Horodecki, Paweł Horodecki, Michał Horodecki, and Karol Horodecki, “Quantum entanglement,” Rev. Mod. Phys. 81, 865–942 (2009).
- Nicolas Brunner, Daniel Cavalcanti, Stefano Pironio, Valerio Scarani, and Stephanie Wehner, “Bell nonlocality,” Reviews of modern physics 86, 419 (2014).
- Abhishek Sadhu and Siddhartha Das, “Testing of quantum nonlocal correlations under constrained free will and imperfect detectors,” Physical Review A 107, 012212 (2023).
- Michael JD Powell, “A direct search optimization method that models the objective and constraint functions by linear interpolation,” in Advances in Optimization and Numerical Analysis. Mathematics and Its Applications, Vol. 275, edited by S. Gomez and JP. Hennart (Springer, 1994).
- Michael JD Powell, “A fast algorithm for nonlinearly constrained optimization calculations,” in Numerical Analysis: Proceedings of the Biennial Conference Held at Dundee, June 28–July 1, 1977 (Springer, 2006) pp. 144–157.
- Hado Van Hasselt, Arthur Guez, and David Silver, “Deep reinforcement learning with double q-learning,” in Proceedings of the AAAI conference on artificial intelligence, Vol. 30 (2016).
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
