# QArchSearch: A Scalable Quantum Architecture Search Package

arXiv: https://arxiv.org/abs/2310.07858 · HTML: https://mlsanity.com/paper/2310.07858/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 11 Oct 2023.

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

- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Random Search and Reproducibility for Neural Architecture Search](https://mlsanity.com/paper/1902.07638/) (2019)
- [Quantum circuit architecture search for variational quantum algorithms](https://mlsanity.com/paper/2010.10217/) (2020)
- [Reinforcement learning for optimization of variational quantum circuit architectures](https://mlsanity.com/paper/2103.16089/) (2021)
- [Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization](https://mlsanity.com/paper/2206.14115/) (2022)
- [Resource-Efficient Neural Architect](https://mlsanity.com/paper/1806.07912/) (2018)
- [Multilevel Combinatorial Optimization Across Quantum Architectures](https://mlsanity.com/paper/1910.09985/) (2019)
- [Importance of Diagonal Gates in Tensor Network Simulations](https://mlsanity.com/paper/2106.15740/) (2021)

## Built on by (most cited first, 1 of 1)

- [Neural Architecture Search Algorithms for Quantum Autoencoders](https://mlsanity.com/paper/2509.15451/) (2025)

## Abstract (arXiv)

The current era of quantum computing has yielded several algorithms that promise high computational efficiency. While the algorithms are sound in theory and can provide potentially exponential speedup, there is little guidance on how to design proper quantum circuits to realize the appropriate unitary transformation to be applied to the input quantum state. In this paper, we present textttQArchSearch, an AI based quantum architecture search package with the textttQTensor library as a backend that provides a principled and automated approach to finding the best model given a task and input quantum state. We show that the search package is able to efficiently scale the search to large quantum circuits and enables the exploration of more complex models for different quantum applications. textttQArchSearch runs at scale and high efficiency on high-performance computing systems using a two-level parallelization scheme on both CPUs and GPUs, which has been demonstrated on the Polaris supercomputer.