Reinforcement Learning for Many-Body Ground State Preparation based on Counter-Diabatic Driving

Abstract
The Quantum Approximate Optimization Ansatz (QAOA) is a prominent example of variational quantum algorithms. We propose a generalized QAOA ansatz called CD-QAOA, which is inspired by the counter-diabatic (CD) 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...
Paper Details
Title
Reinforcement Learning for Many-Body Ground State Preparation based on Counter-Diabatic Driving
Published Date
Mar 15, 2021
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