Deployment of Large IQP Circuit Born Machines on Qubit-Limited Hardware Using Noise Injection
Abstract
Quantum circuit Born machines (QCBMs) built with instantaneous quantum polynomial (IQP) circuits have loss functions that can be efficiently estimated classically, but require quantum computers during inference. Thus, classical computers can train large IQP QCBMs that exceed the qubit numbers of available quantum hardware. This motivates research into how qubit-limited quantum computers can be used to sample from larger IQP QCBMs. Leveraging insights from classical simulation of noisy IQP circuits, we propose a deployment protocol for IQP QCBMs that injects noise at the logical level to probabilistically decompose the original circuit into smaller clusters that can be sampled on qubit-limited quantum processors. We also apply lightweight resampling of known noisy qubits to mitigate the impact of the introduced error. Numerical simulations of our protocol applied to shallow IQP QCBMs and IBM Heron experiments deploying a 316-qubit IQP circuit onto 156-qubit hardware show that mitigation improves the similarity of the empirical distribution to the target distribution. The proposed deployment protocol also crucially preserves correlation structures more accurately than a classical baseline that has accurate single-qubit marginals.
Related
- Noise-regularized training of quantum circuit Born machines under a finite shot budget
- Classical Pre-Training of Large Shallow Parametrized Quantum Circuits through Pauli Propagation and deployment on Quantum Hardware
- Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
- Nonstabilizerness of the entangling layer in quantum circuit Born machines
- Exploiting the Weyl-Chamber Structure for Hardware-Aware Quantum Circuit Compression
BibTeX
@misc{ryu2026deployment,
title = {{Deployment of Large IQP Circuit Born Machines on Qubit-Limited Hardware Using Noise Injection}},
author = {Ju-Young Ryu and Wooyeong Song and Kwangil Bae and Wonhyuk Lee and Ilkwon Sohn},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.35163},
eprint = {2609.35163},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.35163},
}
RIS
TY - UNPB
TI - Deployment of Large IQP Circuit Born Machines on Qubit-Limited Hardware Using Noise Injection
AU - Ju-Young Ryu
AU - Wooyeong Song
AU - Kwangil Bae
AU - Wonhyuk Lee
AU - Ilkwon Sohn
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.35163
UR - https://arxiv.org/abs/2609.35163
AB - Quantum circuit Born machines (QCBMs) built with instantaneous quantum polynomial (IQP) circuits have loss functions that can be efficiently estimated classically, but require quantum computers during inference. Thus, classical computers can train large IQP QCBMs that exceed the qubit numbers of available quantum hardware. This motivates research into how qubit-limited quantum computers can be used to sample from larger IQP QCBMs. Leveraging insights from classical simulation of noisy IQP circuits, we propose a deployment protocol for IQP QCBMs that injects noise at the logical level to probabilistically decompose the original circuit into smaller clusters that can be sampled on qubit-limited quantum processors. We also apply lightweight resampling of known noisy qubits to mitigate the impact of the introduced error. Numerical simulations of our protocol applied to shallow IQP QCBMs and IBM Heron experiments deploying a 316-qubit IQP circuit onto 156-qubit hardware show that mitigation improves the similarity of the empirical distribution to the target distribution. The proposed deployment protocol also crucially preserves correlation structures more accurately than a classical baseline that has accurate single-qubit marginals.
ER -