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

arXiv: https://arxiv.org/abs/2311.16035 · HTML: https://mlsanity.com/paper/2311.16035/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 104 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [Predicting Many Properties of a Quantum System from Very Few Measurements](https://mlsanity.com/paper/2002.08953/) (2020)
- [Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems](https://mlsanity.com/paper/2104.13386/) (2021)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization](https://mlsanity.com/paper/2110.11331/) (2021)
- [QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning](https://mlsanity.com/paper/2202.13239/) (2022)
- [Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers](https://mlsanity.com/paper/1901.11054/) (2019)
- [Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow](https://mlsanity.com/paper/2109.03430/) (2021)
- [QuEst: Graph Transformer for Quantum Circuit Reliability Estimation](https://mlsanity.com/paper/2210.16724/) (2022)
- [Optimized Quantum Compilation for Near-Term Algorithms with OpenPulse](https://mlsanity.com/paper/2004.11205/) (2020)
- [Best Approximate Quantum Compiling Problems](https://mlsanity.com/paper/2106.05649/) (2021)
- [Qubit Mapping and Routing via MaxSAT](https://mlsanity.com/paper/2208.13679/) (2022)
- [SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture](https://mlsanity.com/paper/2211.12711/) (2022)
- [HAMMER: boosting fidelity of noisy Quantum circuits by exploiting Hamming behavior of erroneous outcomes](https://mlsanity.com/paper/2208.09371/) (2022)
- [Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers](https://mlsanity.com/paper/1902.01474/) (2019)
- [TopGen: Topology-Aware Bottom-Up Generator for Variational Quantum Circuits](https://mlsanity.com/paper/2210.08190/) (2022)
- [Q-Pilot: Field Programmable Qubit Array Compilation with Flying Ancillas](https://mlsanity.com/paper/2311.16190/) (2023)

## Built on by (most cited first, 2 of 2)

- [Quantum State Preparation with the QNN-based SRBB Algorithm](https://mlsanity.com/paper/2503.13647/) (2025)
- [SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation](https://mlsanity.com/paper/2408.05435/) (2024)

## Abstract (arXiv)

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 × 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.