Ivanna M. Boras Vazquez

arXiv2026

Directional telecom photons from a chirally coupled quantum dot

Chiral quantum light-matter interfaces, where the internal spin state of a quantum emitter determines the direction in which it emits, are essential building blocks of non-reciprocal quantum devices, deterministic quantum logical gates and entanglement generation protocols. Yet, a chiral quantum interface that operates at telecom wavelengths, and is compatible with telecommunication infrastructure and silicon photonics, does not yet exist. Here, we report on an integrated chiral quantum interface in the original telecom band (1260-1360 nm), created by interfacing InAs quantum dots with a waveguide-coupled InP microdisk. We tune the quantum dot transitions through the photonic cavity using a strong magnetic field, observing a peak cavity enhancement of 3.3 and an emission directionality of 0.985, demonstrating the near-ideal chiral quantum coupling required for quantum information processing on integrated photonic devices.

quant-ph↗

arXiv2026

Quantum photonic neural networks in time

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.

quant-ph↗