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

Understand

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.