# Predictive Models from Quantum Computer Benchmarks

arXiv: https://arxiv.org/abs/2305.08796 · HTML: https://mlsanity.com/paper/2305.08796/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [QuEst: Graph Transformer for Quantum Circuit Reliability Estimation](https://mlsanity.com/paper/2210.16724/) (2022)
- [Learning a quantum computer's capability](https://mlsanity.com/paper/2304.10650/) (2023)

## Built on by (most cited first, 3 of 3)

- [Learning to rank quantum circuits for hardware-optimized performance enhancement](https://mlsanity.com/paper/2404.06535/) (2024)
- [What is my quantum computer good for? Quantum capability learning with physics-aware neural networks](https://mlsanity.com/paper/2406.05636/) (2024)
- [Featuremetric benchmarking: Quantum computer benchmarks based on circuit features](https://mlsanity.com/paper/2504.12575/) (2025)

## Abstract (arXiv)

Holistic benchmarks for quantum computers are essential for testing and summarizing the performance of quantum hardware. However, holistic benchmarks -- such as algorithmic or randomized benchmarks -- typically do not predict a processor's performance on circuits outside the benchmark's necessarily very limited set of test circuits. In this paper, we introduce a general framework for building predictive models from benchmarking data using capability models. Capability models can be fit to many kinds of benchmarking data and used for a variety of predictive tasks. We demonstrate this flexibility with two case studies. In the first case study, we predict circuit (i) process fidelities and (ii) success probabilities by fitting error rates models to two kinds of volumetric benchmarking data. Error rates models are simple, yet versatile capability models which assign effective error rates to individual gates, or more general circuit components. In the second case study, we construct a capability model for predicting circuit success probabilities by applying transfer learning to ResNet50, a neural network trained for image classification. Our case studies use data from cloud-accessible quantum computers and simulations of noisy quantum computers.