Optimizing adiabatic quantum pathways via a learning algorithm

TL;DR

This paper introduces a gradient-free learning algorithm with pulse smoothing to optimize adiabatic quantum pathways, improving the efficiency and robustness of adiabatic quantum computation under real-world perturbations.

Contribution

It presents a novel multiobjective optimization approach for designing adiabatic pathways, outperforming traditional schedules and gradient-based methods in key performance metrics.

Findings

Significant reduction in adiabatic time compared to baseline schedules.

Enhanced maintenance of the instantaneous ground-state population.

Effective application to complex quantum Hamiltonians.

Abstract

Designing proper time-dependent control fields for slowly varying the system to the ground state that encodes the problem solution is crucial for adiabatic quantum computation. However, inevitable perturbations in real applications demand us to accelerate the evolution so that the adiabatic errors can be prevented from accumulation. Here, by treating this trade-off task as a multiobjective optimization problem, we propose a gradient-free learning algorithm with pulse smoothing technique to search optimal adiabatic quantum pathways and apply it to the Landau-Zener Hamiltonian and Grover search Hamiltonian. Numerical comparisons with a linear schedule, local adiabatic theorem induced schedule, and gradient-based algorithm searched schedule reveal that the proposed method can achieve significant performance improvements in terms of the adiabatic time and the instantaneous ground-state population maintenance. The proposed method can be used to solve more complex and real adiabatic quantum computation problems.