# Learning a quantum computer's capability

arXiv: https://arxiv.org/abs/2304.10650 · HTML: https://mlsanity.com/paper/2304.10650/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 80 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [Universal Quantum Control through Deep Reinforcement Learning](https://mlsanity.com/paper/1803.01857/) (2018)
- [Fidelity-based Probabilistic Q-learning for Control of Quantum Systems](https://mlsanity.com/paper/1806.03145/) (2018)

## Built on by (most cited first, 5 of 5)

- [Artificial intelligence for representing and characterizing quantum systems](https://mlsanity.com/paper/2509.04923/) (2025)
- [Learning to rank quantum circuits for hardware-optimized performance enhancement](https://mlsanity.com/paper/2404.06535/) (2024)
- [Predictive Models from Quantum Computer Benchmarks](https://mlsanity.com/paper/2305.08796/) (2023)
- [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)

Accurately predicting a quantum computer's capability -- which circuits it can run and how well it can run them -- is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide which quantum computers to build and use. In this work, we propose a hardware-agnostic method to efficiently construct scalable predictive models of a quantum computer's capability for almost any class of circuits, and demonstrate our method using convolutional neural networks (CNNs). Our CNN-based approach works by efficiently representing a circuit as a three-dimensional tensor and then using a CNN to predict its success rate. Our CNN capability models obtain approximately a 1% average absolute prediction error when modeling processors experiencing both Markovian and non-Markovian stochastic Pauli errors. We also apply our CNNs to model the capabilities of cloud-access quantum computing systems, obtaining moderate prediction accuracy (average absolute error around 2-5%), and we highlight the challenges to building better neural network capability models.