RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training

TL;DR

RobustState is a noise-aware variational training method for quantum state preparation that significantly improves fidelity and robustness on real quantum hardware by incorporating actual noise into the optimization process.

Contribution

It introduces a novel noise-aware training approach for variational quantum state preparation that enhances robustness and fidelity on NISQ devices, adaptable to various ansatzes and algorithms.

Findings

Achieves up to 7.1× error reduction and 96% fidelity improvement.

Enhances fidelity by 50-72% over baseline methods.

Demonstrated effectiveness on 4 quantum algorithms across 10 real quantum machines.

Abstract

Quantum state preparation, a crucial subroutine in quantum computing, involves generating a target quantum state from initialized qubits. Arbitrary state preparation algorithms can be broadly categorized into arithmetic decomposition (AD) and variational quantum state preparation (VQSP). AD employs a predefined procedure to decompose the target state into a series of gates, whereas VQSP iteratively tunes ansatz parameters to approximate target state. VQSP is particularly apt for Noisy-Intermediate Scale Quantum (NISQ) machines due to its shorter circuits. However, achieving noise-robust parameter optimization still remains challenging. We present RobustState, a novel VQSP training methodology that combines high robustness with high training efficiency. The core idea involves utilizing measurement outcomes from real machines to perform back-propagation through classical simulators, thus incorporating real quantum noise into gradient calculations. RobustState serves as a versatile, plug-and-play technique applicable for training parameters from scratch or fine-tuning existing parameters to enhance fidelity on target machines. It is adaptable to various ansatzes at both gate and pulse levels and can even benefit other variational algorithms, such as variational unitary synthesis. Comprehensive evaluation of RobustState on state preparation tasks for 4 distinct quantum algorithms using 10 real quantum machines demonstrates a coherent error reduction of up to 7.1 $\times$ and state fidelity improvement of up to 96\% and 81\% for 4-Q and 5-Q states, respectively. On average, RobustState improves fidelity by 50\% and 72\% for 4-Q and 5-Q states compared to baseline approaches.