Optimizing State Preparation for Variational Quantum Regression on NISQ Hardware

TL;DR

This paper presents a novel ZX-calculus-based optimization approach for state preparation in variational quantum regression, enabling execution on NISQ hardware by reducing circuit complexity and mitigating noise effects.

Contribution

It introduces a new state preparation method combined with ZX-calculus optimizations, improving the feasibility of quantum regression algorithms on noisy intermediate-scale quantum devices.

Findings

Successful execution of quantum regression on current hardware

Enhanced circuit efficiency through ZX-calculus optimizations

Broad applicability to other quantum circuits requiring real-valued state preparation

Abstract

The execution of quantum algorithms on modern hardware is often constrained by noise and qubit decoherence, limiting the circuit depth and the number of gates that can be executed. Circuit optimization techniques help mitigate these limitations, enhancing algorithm feasibility. In this work, we implement, optimize, and execute a variational quantum regression algorithm using a novel state preparation method. By leveraging ZX-calculus-based optimization techniques, such as Pauli pushing, phase folding, and Hadamard pushing, we achieve a more efficient circuit design. Our results demonstrate that these optimizations enable the successful execution of the quantum regression algorithm on current hardware. Furthermore, the techniques presented are broadly applicable to other quantum circuits requiring arbitrary real-valued state preparation, advancing the practical implementation of quantum algorithms.