# Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization

arXiv: https://arxiv.org/abs/2206.14115 · HTML: https://mlsanity.com/paper/2206.14115/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://mlsanity.com/paper/1802.07191/) (2018)
- [BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search](https://mlsanity.com/paper/1910.11858/) (2019)
- [Quantum generative adversarial networks](https://mlsanity.com/paper/1804.08641/) (2018)
- [Quantum circuit architecture search for variational quantum algorithms](https://mlsanity.com/paper/2010.10217/) (2020)
- [Quantum Architecture Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.07715/) (2021)
- [A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver](https://mlsanity.com/paper/1910.09694/) (2019)
- [Deep Neural Architecture Search with Deep Graph Bayesian Optimization](https://mlsanity.com/paper/1905.06159/) (2019)
- [A scale-dependent notion of effective dimension](https://mlsanity.com/paper/2001.10872/) (2020)

## Built on by (most cited first, 16 of 16)

- [Quantum Architecture Search: A Survey](https://mlsanity.com/paper/2406.06210/) (2024)
- [Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning](https://mlsanity.com/paper/2407.18202/) (2024)
- [Quantum Machine Learning Architecture Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2407.20147/) (2024)
- [Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects](https://mlsanity.com/paper/2407.00736/) (2024)
- [Neural auto-designer for enhanced quantum kernels](https://mlsanity.com/paper/2401.11098/) (2024)
- [Quantum Architecture Search with Unsupervised Representation Learning](https://mlsanity.com/paper/2401.11576/) (2024)
- [Leveraging Diffusion Models for Parameterized Quantum Circuit Generation](https://mlsanity.com/paper/2505.20863/) (2025)
- [Toward Automated Quantum Variational Machine Learning](https://mlsanity.com/paper/2312.01567/) (2023)
- [Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach](https://mlsanity.com/paper/2504.10733/) (2025)
- [BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search](https://mlsanity.com/paper/2507.12189/) (2025)
- [How to find expressible and trainable parameterized quantum circuits?](https://mlsanity.com/paper/2603.14451/) (2026)
- [Decoding surface codes with deep reinforcement learning and probabilistic policy reuse](https://mlsanity.com/paper/2212.11890/) (2022)
- [QArchSearch: A Scalable Quantum Architecture Search Package](https://mlsanity.com/paper/2310.07858/) (2023)
- [Quantum Circuit Training with Growth-Based Architectures](https://mlsanity.com/paper/2411.16560/) (2024)
- [Neural Architecture Search Algorithms for Quantum Autoencoders](https://mlsanity.com/paper/2509.15451/) (2025)
- [Zero-shot Quantum Neural Architecture Search](https://mlsanity.com/paper/2605.27410/) (2026)

## Abstract (arXiv)

Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neural architecture search. We tackle this challenge by designing a quantum circuits metric for Bayesian optimization with Gaussian process. To this goal, we propose a new quantum gates distance that characterizes the gates' action over every quantum state and provide a theoretical perspective on its geometrical properties. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems including training a quantum generative adversarial network, solving combinatorial optimization in the MaxCut problem, and simulating quantum Fourier transform. Our method can be extended to characterize behaviors of various quantum machine learning models.