Robuststate: Boosting fidelity of quantum state preparation via noise-aware variational training

Quantum state preparation, a crucial subroutine in quantum computing, involves generating a target quantum state from initialized qubits. Variational quantum state preparation (VQSP) is particularly apt for Noisy-Intermediate Scale Quantum (NISQ) machines due to its shorter circuits compared to arithmetic decomposition (AD). However, achieving noise-robust parameter optimization remains challenging. This paper presents RobustState, a novel VQSP training methodology that combines high robustness with high training efficiency by utilizing measurement outcomes from real machines to perform back-propagation through classical simulators, incorporating real quantum noise into gradient calculations. RobustState is 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 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 and state fidelity improvement of up to 96% and 81% for 4-Q and 5-Q states, respectively. PDF