Iterative Gradient Ascent Pulse Engineering algorithm for quantum optimal control

TL;DR

The paper introduces an iterative version of the GRAPE algorithm, called iGRAPE, which decomposes large quantum control problems into smaller parts for parallel processing, significantly speeding up quantum state preparation.

Contribution

It proposes a novel iterative GRAPE algorithm that reduces computational resources by decomposing large problems into smaller, parallelizable subproblems, enabling efficient control of larger quantum systems.

Findings

iGRAPE achieves up to 13-fold speedup over GRAPE for 12-qubit systems.

Experimental verification of iGRAPE on a 4-qubit NMR system.

Demonstrates feasibility of scalable quantum control optimization.

Abstract

Gradient ascent pulse engineering algorithm (GRAPE) is a typical method to solve quantum optimal control problems. However, it suffers from an exponential resource in computing the time evolution of quantum systems with the increasing number of qubits, which is a barrier for its application in large-qubit systems. To mitigate this issue, we propose an iterative GRAPE algorithm (iGRAPE) for preparing a desired quantum state, where the large-scale, resource-consuming optimization problem is decomposed into a set of lower-dimensional optimization subproblems by disentanglement operations. Consequently these subproblems can be solved in parallel with less computing resources. For physical platforms such as nuclear magnetic resonance (NMR) and superconducting quantum systems, we show that iGRAPE can provide up to 13-fold speedup over GRAPE when preparing desired quantum states in systems within 12 qubits. Using a four-qubit NMR system, we also experimentally verify the feasibility of the iGRAPE algorithm.