Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals-Reference-Cited by-同舟云学术

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Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals

Published:2023-09-17 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Quantum Computing and Engineering (QCE)
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Author:

Wright Emily¹,De Sousa Rogério¹

Affiliation:

1. University of Victoria,Department of Physics and Astronomy,Victoria,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10313590/10313591/10313699.pdf?arnumber=10313699

Reference61 articles.

1. A rapid monotonically convergent iteration algorithm for quantum optimal control over the expectation value of a positive definite operator

2. Efficient z gates for quantum computing;mckay;Physical Review A,2017

3. Procedure for systematically tuning up cross-talk in the cross-resonance gate;sheldon;Physical Review A,2016

4. Optimal control of coupled spin dynamics: design of NMR pulse sequences by gradient ascent algorithms

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fast-forwarding molecular ground state preparation with optimal control on analog quantum simulators;The Journal of Chemical Physics;2024-07-01

2. Reinforcement learning pulses for transmon qubit entangling gates;Machine Learning: Science and Technology;2024-06-01

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