Leveraging verifiable structure in quantum circuits to find more efficient compilations with agentic AI
Abstract
We discuss an effective paradigm of utilizing AI agents to better compile quantum circuits when one can constrain bottleneck subroutines to have some form of classically verifiable structure. For example, when critical subroutines are composed exclusively of reversible classical logic, phasing operations, and measurement based uncomputation, one can efficiently simulate execution for classical inputs. This property facilitates efficient certification of approximate circuit synthesis via a form of fuzz testing, provided suitable assumptions on the input distribution. While this pattern was recently exploited to improve quantum cryptanalysis circuits, here we highlight the flexibility of the broader approach by discussing three important applications. (1) The computation of 1/√x that bottlenecks real space simulations of the Coulomb operator such as those important for modeling inertial confinement fusion reactors. (2) The decoding of Reed-Solomon codes which bottlenecks the decoded quantum interferometry algorithm when used to approximately solve the optimal polynomial intersection problem. And (3) the quantum look-up tables and controlled application of Givens rotations that bottleneck the most performant quantum algorithms for simulating chemistry in second quantization. We discuss reference implementations of these algorithms and how they can be constrained and verified in the interest of seeding a Kaggle competition designed to accelerate AI-based research into the optimization of their compilation.
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BibTeX
@misc{khattar2026leveraging,
title = {{Leveraging verifiable structure in quantum circuits to find more efficient compilations with agentic AI}},
author = {Tanuj Khattar and Nicholas C. Rubin and Craig Gidney and Michael Broughton and Guang Hao Low and Ryan Babbush},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23832},
eprint = {2610.23832},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23832},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Leveraging verifiable structure in quantum circuits to find more efficient compilations with agentic AI
AU - Tanuj Khattar
AU - Nicholas C. Rubin
AU - Craig Gidney
AU - Michael Broughton
AU - Guang Hao Low
AU - Ryan Babbush
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.23832
UR - https://arxiv.org/abs/2610.23832
AB - We discuss an effective paradigm of utilizing AI agents to better compile quantum circuits when one can constrain bottleneck subroutines to have some form of classically verifiable structure. For example, when critical subroutines are composed exclusively of reversible classical logic, phasing operations, and measurement based uncomputation, one can efficiently simulate execution for classical inputs. This property facilitates efficient certification of approximate circuit synthesis via a form of fuzz testing, provided suitable assumptions on the input distribution. While this pattern was recently exploited to improve quantum cryptanalysis circuits, here we highlight the flexibility of the broader approach by discussing three important applications. (1) The computation of 1/√x that bottlenecks real space simulations of the Coulomb operator such as those important for modeling inertial confinement fusion reactors. (2) The decoding of Reed-Solomon codes which bottlenecks the decoded quantum interferometry algorithm when used to approximately solve the optimal polynomial intersection problem. And (3) the quantum look-up tables and controlled application of Givens rotations that bottleneck the most performant quantum algorithms for simulating chemistry in second quantization. We discuss reference implementations of these algorithms and how they can be constrained and verified in the interest of seeding a Kaggle competition designed to accelerate AI-based research into the optimization of their compilation.
ER -