# Computational Performance Bounds Prediction in Quantum Computing with Unstable Noise

arXiv: https://arxiv.org/abs/2507.17043 · HTML: https://mlsanity.com/paper/2507.17043/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage](https://mlsanity.com/paper/2006.14815/) (2020)
- [On-Device Unsupervised Image Segmentation](https://mlsanity.com/paper/2303.12753/) (2023)
- [Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation](https://mlsanity.com/paper/1903.10963/) (2019)
- [Simultaneous execution of quantum circuits on current and near-future NISQ systems](https://mlsanity.com/paper/2112.07091/) (2021)
- [A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices](https://mlsanity.com/paper/2307.09771/) (2023)
- [Adaptive job and resource management for the growing quantum cloud](https://mlsanity.com/paper/2203.13260/) (2022)
- [Financial Risk Management on a Neutral Atom Quantum Processor](https://mlsanity.com/paper/2212.03223/) (2022)
- [How Parallel Circuit Execution Can Be Useful for NISQ Computing?](https://mlsanity.com/paper/2112.00387/) (2021)
- [DGR: Tackling Drifted and Correlated Noise in Quantum Error Correction via Decoding Graph Re-weighting](https://mlsanity.com/paper/2311.16214/) (2023)
- [A Novel Diffusion Model for Pairwise Geoscience Data Generation with Unbalanced Training Dataset](https://mlsanity.com/paper/2501.00941/) (2025)
- [DISQ: Dynamic Iteration Skipping for Variational Quantum Algorithms](https://mlsanity.com/paper/2308.06634/) (2023)

## Abstract (arXiv)

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise's impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. In this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10x narrower than the state-of-the-art analytical approach.