Year: 2023
Type: article
Abstract: Quantum control aims to manipulate quantum systems toward specific quantum states or desired operations. Designing highly accurate and effective control steps is vitally important to various quantum applications, including energy minimization and circuit compilation. In this paper we focus on discrete binary quantum control problems and apply different optimization algorithms and techniques to improve computational efficiency and solution quality. Specifically, we develop a generic model and extend it in several ways. We introduce a squared L2 -penalty function to handle additional side constraints, to model requirements such as allowing at most one control to be active. We introduce a total variation (TV) regularizer to reduce the number of switches in the control. We modify the popular gradient ascent pulse engineering (GRAPE) algorithm, develop a new alternating direction method of multipliers (ADMM) algorithm to solve the continuous relaxation of the penalized model, and then apply rounding techniques to obtain binary control solutions. We propose a modified trust-region method to further improve the solutions. Our algorithms can obtain high-quality control results, as demonstrated by numerical studies on diverse quantum control examples.
Source: Quantum
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $215
Awards: G1003286108, G102163711, G107546177, G1313983767, G1664757510, G2044717868, EXTREME-SCALE STOCHASTIC OPTIMIZATION AND SIMULATION VIA LEARNING-ENHANCED DECOMPOSITION AND PARALLELIZATION, G2287611682, G262962599, G3075337988, G3086902720, G3448829509, G498139845, G513797252, G5398989114, G6848031779, G6918803902, EXTREME-SCALE STOCHASTIC OPTIMIZATION AND SIMULATION VIA LEARNING-ENHANCED DECOMPOSITION AND PARALLELIZATION, Collaborative Research: Emerging Optimization Methods for Planning and Operating Shared Mobility Systems under Uncertain Budget and Market Demand, G878601127, G969889393, G993149817
OpenAlex ID: W4313495236