# Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow

arXiv: https://arxiv.org/abs/2109.03430 · HTML: https://mlsanity.com/paper/2109.03430/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 15 of 15)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks](https://mlsanity.com/paper/2002.04116/) (2020)
- [Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search](https://mlsanity.com/paper/1901.11211/) (2019)
- [Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/1911.00139/) (2019)
- [A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage](https://mlsanity.com/paper/2006.14815/) (2020)
- [Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers](https://mlsanity.com/paper/1901.11054/) (2019)
- [Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start](https://mlsanity.com/paper/2007.09087/) (2020)
- [Achieving Super-Linear Speedup across Multi-FPGA for Real-Time DNN Inference](https://mlsanity.com/paper/1907.08985/) (2019)
- [On the qubit routing problem](https://mlsanity.com/paper/1902.08091/) (2019)
- [When Machine Learning Meets Quantum Computers: A Case Study](https://mlsanity.com/paper/2012.10360/) (2020)
- [Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices](https://mlsanity.com/paper/2102.06336/) (2021)
- [Optimal Layout Synthesis for Quantum Computing](https://mlsanity.com/paper/2007.15671/) (2020)
- [A Hardware-Aware Heuristic for the Qubit Mapping Problem in the NISQ Era](https://mlsanity.com/paper/2010.03397/) (2020)
- [Quantum Accelerator Stack: A Research Roadmap](https://mlsanity.com/paper/2102.02035/) (2021)

## Built on by (most cited first, 13 of 13)

- [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)
- [Quantum Neural Network Compression](https://mlsanity.com/paper/2207.01578/) (2022)
- [Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?](https://mlsanity.com/paper/2207.07626/) (2022)
- [Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers](https://mlsanity.com/paper/2311.16082/) (2023)
- [Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey](https://mlsanity.com/paper/2409.07626/) (2024)
- [QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model](https://mlsanity.com/paper/2304.11511/) (2023)
- [A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices](https://mlsanity.com/paper/2307.09771/) (2023)
- [PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud](https://mlsanity.com/paper/2404.13475/) (2024)
- [Review of Ansatz Designing Techniques for Variational Quantum Algorithms](https://mlsanity.com/paper/2212.04913/) (2022)
- [DGR: Tackling Drifted and Correlated Noise in Quantum Error Correction via Decoding Graph Re-weighting](https://mlsanity.com/paper/2311.16214/) (2023)
- [RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training](https://mlsanity.com/paper/2311.16035/) (2023)
- [U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/2401.05357/) (2023)

## Abstract (arXiv)

In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error correction is promising but requires an extensive number (e.g., over 1,000) of physical qubits to create one "perfect" qubit, exceeding the capacity of the existing quantum computers. This paper aims to tackle the noise issue from another angle: instead of creating perfect qubits for general quantum algorithms, we investigate the potential to mitigate the noise issue for dedicate algorithms. Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise. As a result, the implementation of QNN needs no or a small number of additional physical qubits, which is more realistic for the near-term quantum computers. To achieve this goal, an application-specific compiler is essential: on the one hand, the error cannot be learned if the mapping from logical qubits to physical qubits exists randomness; on the other hand, the compiler needs to be efficient so that the lengthy training procedure can be completed in a reasonable time. In this paper, we utilize the recent QNN framework, QuantumFlow, as a case study. Experimental results show that the proposed approach can optimize QNN models for different errors in qubits, achieving up to 28% accuracy improvement compared with the model obtained by the error-agnostic training.