Work

Scalable quantum dynamics compilation via quantum machine learning

Year: 2026

Type: article

Abstract: Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multiqubit 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 make a paradigm shift in VQC’s framework design. First, we use a cost function from recently established out-of-distribution generalization results in quantum machine learning: By learning the action of a given many-body dynamics on a small dataset of product states, we can obtain a unitary circuit that generalizes to highly entangled states such as the Haar random states. Moreover, a key observation is that the low complexity in the data states allows us to use tensor-network methods to compress such time-evolved product states by exploiting their low entanglement features. Utilizing warm start methods, our approach exceeds state-of-the-art compilation results in both system size and accuracy in one dimension. For the first time, we extend VQC to systems to two-dimensional systems, demonstrating a significant resource advantage over standard Trotterization methods. The flexibility of our framework highlights the method's promise for advancing quantum simulation tasks on near-term quantum processors.

Source: Physical Review Research

DOI: 10.1103/wswv-nq6d

Authors: Yuxuan Zhang, Roeland Wiersema, Juan Carrasquilla, Lukasz Cincio, Yong Baek Kim

Institutions: University of Toronto, Vector Institute, University of Waterloo, ETH Zurich, Los Alamos National Laboratory

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625