State Dependent Optimization with Quantum Circuit Cutting

TL;DR

This paper introduces a state-dependent optimization framework for quantum circuits that enhances circuit performance and noise mitigation by integrating initial-state knowledge, measure-state optimization, and flexible circuit cutting strategies.

Contribution

It proposes a novel SDO framework combining ISDO, MSDO, and biased observable selection, along with a non-separate circuit cutting method for scalable quantum circuit optimization.

Findings

Consistently mitigates noise in quantum circuits.

Improves performance of QAOA, QFT, and BV circuits.

Validates methods through noisy simulations.

Abstract

Quantum circuits can be reduced through optimization to better fit the constraints of quantum hardware. One such method, initial-state dependent optimization (ISDO), reduces gate count by leveraging knowledge of the input quantum states. Surprisingly, we found that ISDO is broadly applicable to the downstream circuits produced by circuit cutting. Circuit cutting also requires measuring upstream qubits and has some flexibility of selection observables to do reconstruction. Therefore, we propose a state-dependent optimization (SDO) framework that incorporates ISDO, our newly proposed measure-state dependent optimization (MSDO), and a biased observable selection strategy. Building on the strengths of the SDO framework and recognizing the scalability challenges of circuit cutting, we propose non-separate circuit cutting-a more flexible approach that enables optimizing gates without fully separating them. We validate our methods on noisy simulations of QAOA, QFT, and BV circuits. Results show that our approach consistently mitigates noise and improves overall circuit performance, demonstrating its promise for enhancing quantum algorithm execution on near-term hardware.