Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements

• URL: http://arxiv.org/abs/2408.06590v1
• Date: Tue, 13 Aug 2024 02:56:43 GMT
• Title: Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements
• Authors: Feng Zhang, Cai-Zhuang Wang, Thomas Iadecola, Peter P. Orth, Yong-Xin Yao,
• Abstract summary: We show an improved version of the adaptive variational quantum dynamics simulation (AVQDS) method, which we call AVQDS(T) The algorithm adaptively adds layers of disjoint unitary gates to the ansatz circuit so as to keep the McLachlan distance, a measure of the accuracy of the variational dynamics, below a fixed threshold. We also show a method based on eigenvalue truncation to solve the linear equations of motion for the variational parameters with enhanced noise resilience.
• Score: 4.2643127089535104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The adaptive variational quantum dynamics simulation (AVQDS) method performs real-time evolution of quantum states using automatically generated parameterized quantum circuits that often contain substantially fewer gates than Trotter circuits. Here we report an improved version of the method, which we call AVQDS(T), by porting the Tiling Efficient Trial Circuits with Rotations Implemented Simultaneously (TETRIS) technique. The algorithm adaptively adds layers of disjoint unitary gates to the ansatz circuit so as to keep the McLachlan distance, a measure of the accuracy of the variational dynamics, below a fixed threshold. We perform benchmark noiseless AVQDS(T) simulations of quench dynamics in local spin models demonstrating that the TETRIS technique significantly reduces the circuit depth and two-qubit gate count. We also show a method based on eigenvalue truncation to solve the linear equations of motion for the variational parameters with enhanced noise resilience. Finally, we propose a way to substantially alleviate the measurement overhead of AVQDS(T) while maintaining high accuracy by synergistically integrating quantum circuit calculations on quantum processing units with classical calculations using, e.g., tensor networks to evaluate the quantum geometric tensor. We showcase that this approach enables AVQDS(T) to deliver more accurate results than simulations using a fixed ansatz of comparable final depth for a significant time duration with fewer quantum resources.

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