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)...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jiahao Yao, Lin Lin, Marin Bukov
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/fba1d9d8fbc84730afc49b3402694c83
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fba1d9d8fbc84730afc49b3402694c83
record_format	dspace
spelling	oai:doaj.org-article:fba1d9d8fbc84730afc49b3402694c832021-12-02T19:14:08ZReinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving10.1103/PhysRevX.11.0310702160-3308https://doaj.org/article/fba1d9d8fbc84730afc49b3402694c832021-09-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.11.031070http://doi.org/10.1103/PhysRevX.11.031070https://doaj.org/toc/2160-3308The 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) approach. The resulting hybrid control algorithm proves versatile in preparing the ground state of quantum-chaotic many-body spin chains by minimizing the energy. We show that using terms occurring in the adiabatic gauge potential as generators of additional control unitaries, it is possible to achieve fast high-fidelity many-body control away from the adiabatic regime. While each unitary retains the conventional QAOA-intrinsic continuous control degree of freedom such as the time duration, we consider the order of the multiple available unitaries appearing in the control sequence as an additional discrete optimization problem. Endowing the policy gradient algorithm with an autoregressive deep learning architecture to capture causality, we train the RL agent to construct optimal sequences of unitaries. The algorithm has no access to the quantum state, and we find that the protocol learned on small systems may generalize to larger systems. By scanning a range of protocol durations, we present numerical evidence for a finite quantum speed limit in the nonintegrable mixed-field spin-1/2 Ising and Lipkin-Meshkov-Glick models, and for the suitability to prepare ground states of the spin-1 Heisenberg chain in the long-range and topologically ordered parameter regimes. This work paves the way to incorporate recent success from deep learning for the purpose of quantum many-body control.Jiahao YaoLin LinMarin BukovAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 11, Iss 3, p 031070 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Jiahao Yao Lin Lin Marin Bukov Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
description	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) approach. The resulting hybrid control algorithm proves versatile in preparing the ground state of quantum-chaotic many-body spin chains by minimizing the energy. We show that using terms occurring in the adiabatic gauge potential as generators of additional control unitaries, it is possible to achieve fast high-fidelity many-body control away from the adiabatic regime. While each unitary retains the conventional QAOA-intrinsic continuous control degree of freedom such as the time duration, we consider the order of the multiple available unitaries appearing in the control sequence as an additional discrete optimization problem. Endowing the policy gradient algorithm with an autoregressive deep learning architecture to capture causality, we train the RL agent to construct optimal sequences of unitaries. The algorithm has no access to the quantum state, and we find that the protocol learned on small systems may generalize to larger systems. By scanning a range of protocol durations, we present numerical evidence for a finite quantum speed limit in the nonintegrable mixed-field spin-1/2 Ising and Lipkin-Meshkov-Glick models, and for the suitability to prepare ground states of the spin-1 Heisenberg chain in the long-range and topologically ordered parameter regimes. This work paves the way to incorporate recent success from deep learning for the purpose of quantum many-body control.
format	article
author	Jiahao Yao Lin Lin Marin Bukov
author_facet	Jiahao Yao Lin Lin Marin Bukov
author_sort	Jiahao Yao
title	Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
title_short	Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
title_full	Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
title_fullStr	Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
title_full_unstemmed	Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
title_sort	reinforcement learning for many-body ground-state preparation inspired by counterdiabatic driving
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/fba1d9d8fbc84730afc49b3402694c83
work_keys_str_mv	AT jiahaoyao reinforcementlearningformanybodygroundstatepreparationinspiredbycounterdiabaticdriving AT linlin reinforcementlearningformanybodygroundstatepreparationinspiredbycounterdiabaticdriving AT marinbukov reinforcementlearningformanybodygroundstatepreparationinspiredbycounterdiabaticdriving
_version_	1718377009101406208

Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving

Ejemplares similares