Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
The quantum alternating operator ansatz (QAOA) is a prominent example of variational quantum algorithms. We propose a generalized QAOA called CD-QAOA, which is inspired by the counterdiabatic driving procedure, designed for quantum many-body systems and optimized using a reinforcement learning (RL)...
Saved in:
Main Authors: | Jiahao Yao, Lin Lin, Marin Bukov |
---|---|
Format: | article |
Language: | EN |
Published: |
American Physical Society
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/fba1d9d8fbc84730afc49b3402694c83 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Robust preparation of many-body ground states in Jaynes–Cummings lattices
by: Kang Cai, et al.
Published: (2021) -
Shortcuts to adiabaticity by counterdiabatic driving for trapped-ion displacement in phase space
by: Shuoming An, et al.
Published: (2016) -
Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
by: Weishun Zhong, et al.
Published: (2021) -
Ground state solutions and infinitely many solutions for a nonlinear Choquard equation
by: Tianfang Wang, et al.
Published: (2021) -
Wave-Particle Duality of Many-Body Quantum States
by: Christoph Dittel, et al.
Published: (2021)