Quantum photonic neural networks in time

• URL: http://arxiv.org/abs/2603.23798v1
• Date: Wed, 25 Mar 2026 00:16:50 GMT
• Title: Quantum photonic neural networks in time
• Abstract summary: We introduce the architecture and timing algorithm to realize a time-bin-encoded quantum photonic neural network (QPNN)<n>Unlike the typical spatially-encoded QPNN, time-encoded networks require the same number of photonic elements.<n>We show how to include imperfections such as losses, routing errors and most notably distinguishable photons.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the architecture and timing algorithm to realize a time-bin-encoded quantum photonic neural network (QPNN): a reconfigurable nonlinear photonic circuit inspired by the brain and trained to process quantum information. Unlike the typical spatially-encoded QPNN, time-encoded networks require the same number of photonic elements (e.g. phase shifters or switches) regardless of their size or depth. Here, we present a model of such a network and show how to include imperfections such as losses, routing errors and most notably distinguishable photons. As an example, we train the QPNN to realize a controlled-NOT gate, based on a hypothetical ideal Kerr nonlinearity. We then extend our model to a realistic two-photon nonlinearity due to scattering from a single, semiconductor quantum dot coupled to a photonic waveguide. We show that, using this realistic nonlinearity, the QPNN can be trained to act as a Bell-state analyzer which operates with a fidelity of 0.96 and at a rate only limited by losses. We further show that time gating can raise this fidelity to over 0.99, while still maintaining an efficiency exceeding 0.9. Overall, this work lays a framework for the first QPNN encoded in time, and provides a clear path to the scaling of these networks.

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