Accelerating inference for multilayer neural networks with quantum computers

Conference item

Fault-tolerant Quantum Processing Units (QPUs) promise to deliver exponential speed-ups in select computational tasks, yet their integration into modern deep learning pipelines remains unclear. In this work, we take a step towards bridging this gap by presenting the first fully-coherent quantum implementation of a multilayer neural network with non-linear activation functions. Our constructions mirror widely used deep learning architectures based on ResNet, and consist of residual blocks with multi-filter 2D convolutions, sigmoid activations, skip-connections, and layer normalizations. We analyse the complexity of inference for networks under three quantum data access regimes. Without any assumptions, we establish a quadratic speedup over classical methods for shallow bilinear-style networks. With efficient quantum access to the weights, we obtain a quartic speedup over classical methods. With efficient quantum access to both the inputs and the network weights, we prove that a network with an N-dimensional vectorized input, k residual block layers, and a final residual-linear-pooling layer can be implemented with an error of ϵ with O(polylog(N/ϵ) k ) inference cost.

Authors: Rattew, A; Huang, P-W; Guo, N; Pira, L; Rebentrost, P

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: OpenReview
• Host title: Proceedings of the 14th International Conference on Learning Representations (ICLR 2026)
• Article number: 22266
• Acceptance date: 2026-01-26
• Event title: 14th International Conference on Learning Representations (ICLR 2026)
• Event location: Rio de Janeiro, Brazil
• Event website: https://iclr.cc/Conferences/2026
• Event start date: 2026-04-23
• Event end date: 2026-04-27
• Language: English