Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm
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 an...
| Authors | Natsuto Isogai, Hayata Yamasaki, Sho Sonoda et al. |
|---|---|
| Primary category | quant-ph |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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