Scalable quantum dynamics compilation via quantum machine learning

TL;DR

This paper introduces a scalable variational quantum compilation method leveraging quantum machine learning to efficiently synthesize multi-qubit dynamics, outperforming traditional techniques in 1D and 2D systems.

Contribution

It demonstrates a novel VQC approach that generalizes from small data sets to complex entangled states, enabling more efficient quantum simulations on near-term hardware.

Findings

Outperforms state-of-the-art in 1D quantum compilation

Extends VQC to 2D systems with resource advantages

Uses tensor network methods to compress time-evolved states

Abstract

Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared to deterministic methods such as Trotterization, variational quantum compilation (VQC) methods employ variational optimization to reduce gate costs while maintaining high accuracy. In this work, we explore the potential of a VQC scheme by making use of out-of-distribution generalization results in quantum machine learning (QML): By learning the action of a given many-body dynamics on a small data set of product states, we can obtain a unitary circuit that generalizes to highly entangled states such as the Haar random states. The efficiency in training allows us to use tensor network methods to compress such time-evolved product states by exploiting their low entanglement features. Our approach exceeds state-of-the-art compilation results in both system size and accuracy in one dimension ($1$D). For the first time, we extend VQC to systems on two-dimensional (2D) strips with a quasi-1D treatment, demonstrating a significant resource advantage over standard Trotterization methods, highlighting the method's promise for advancing quantum simulation tasks on near-term quantum processors.