Title: Scalable quantum dynamics compilation via quantum machine learning
Speaker: Yuxuan Zhang from University of Toronto and Vector Institute
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 elementarygates as possible. Compared to deterministic methods such as Trotterization, variational quantumcompilation (VQC) methods employ variational optimization to reduce gate costs while maintaininghigh accuracy. In this work, we explore the potential of a VQC scheme by making use of out-ofdistribution generalization results in quantum machine learning (QML): By learning the action ofa given many-body dynamics on a small data set of product states, we can obtain a unitary circuitthat generalizes to highly entangled states such as the Haar random states. The efficiency in trainingallows us to use tensor network methods to compress such time-evolved product states by exploitingtheir low entanglement features. Our approach exceeds state-of-the-art compilation results in bothsystem size and accuracy in one dimension (1D). For the first time, we extend VQC to systemson two-dimensional (2D) strips with a quasi-1D treatment, demonstrating a significant resourceadvantage over standard Trotterization methods, highlighting the method’s promise for advancingquantum simulation tasks on near-term quantum processors.
arXiv: arxiv.org/abs/2409.16346