Abstract:Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for selecting sparse subnetworks from large shallow neural networks. Instead of directly solving an optimization problem over a large-scale network, this algorithm constructs a sparse subnetwork by sampling hidden nodes from an optimized probability distribution defined using the ridgelet transform. The quantum algorithm performs this sampling in time $O(D)$ in the data dimension $D$, whereas a naive classical implementation relies on handling exponentially many candidate nodes and hence takes $\exp[O(D)]$ time. In this work, we construct and analyze a quantum-inspired fully classical algorithm for the same sampling task. We show that our algorithm runs in time $O(\operatorname{poly}(D))$, thereby removing the exponential dependence on $D$ from the previous classical approach. Numerical simulations show that the proposed sampler achieves empirical risk comparable to exact sampling from the optimized distribution and substantially lower than sampling from the non-optimized uniform distribution, while also exhibiting exponentially improved runtime scaling compared with the conventional classical implementation. These successful dequantization results show that sparse subnetwork selection via optimized sampling can be achieved classically with polynomial data-dimension scaling on conventional computers without quantum hardware, providing an alternative to the existing quantum algorithm.
| Comments: | 28 pages, 3 figures |
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.13979 [quant-ph] |
| (or arXiv:2605.13979v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13979 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Natsuto Isogai [view email]
[v1]
Wed, 13 May 2026 18:00:56 UTC (169 KB)