Crosstalk-Based Parameterized Quantum Circuit Approximation

TL;DR

This paper introduces a crosstalk-aware approximation method for variational quantum algorithms that improves circuit performance by hardware-specific crosstalk mitigation, reducing circuit complexity and enhancing expressibility and trainability.

Contribution

It presents a hardware-driven ansatz approximation approach leveraging crosstalk behavior, with a novel circuit-level optimization and crosstalk mitigation strategy for VQAs.

Findings

Crosstalk mitigation improves expressibility and trainability of quantum circuits.

Optimized ansatz outperforms base configurations on quantum chemistry benchmarks.

Hardware-specific crosstalk mitigation enhances quantum circuit performance.

Abstract

In this paper, we propose an ansatz approximation approach for variational quantum algorithms (VQAs) that uses one of the hardware's main attributes, its crosstalk behavior, as its main approximation driver. By utilizing crosstalk-adaptive scheduling, we are able to apply a circuit-level approximation/optimization to our ansatz. Our design procedure involves first characterizing the hardware's crosstalk and then approximating the circuit by a desired level of crosstalk mitigation, all while effectively reducing its duration and gate counts. We demonstrate the effect of crosstalk mitigation on expressibility, trainability, and entanglement: key components that drive the utility of parameterized circuits. We tested our approach on real quantum hardware against a base configuration, and our results showed superior performance for the circuit-level optimized ansatz over a base ansatz for two quantum chemistry benchmarks. We take into consideration that applications vary in their response to crosstalk, and we believe that this approximation strategy can be used to create ansatze that are expressive, trainable, and with crosstalk mitigation levels tailored for specific workloads.