SearcharxivSearch

arXiv subjects

Ian Chin

Publications and source records attributed to Ian Chin.

2 recordsLinked to original sources

Integrated Dual-Resonator Architecture for Telecom Photon-Memory Entanglement

Scalable quantum networks require the efficient generation and storage of entanglement between photonic qubits and quantum memories. Quantum repeaters based on absorptive rare-earth-ion photonic memories offer a promising route toward highly multiplexed quantum networking, but unifying spectrally matched photon sources and quantum memories within a common architecture remains a major challenge. Here we demonstrate an integrated photonic architecture for telecom photon-memory entanglement generation based on dual self-similar silicon carbide microring resonators. Connected by a fiber link, one resonator operates as an entangled photon-pair source, while the other functions as a cavity-enhanced atomic-frequency-comb quantum memory. The memory resonator reaches an ensemble cooperativity of 1.9 after hyperfine initialization and is spectrally matched to the source, enabling storage of entangled telecom photons without spectral modification. We generate and characterize photon-memory entanglement from a quantum interference visibility preserved before and after storage. Harnessing the strong source correlations and the high multimode capacity of the memory, we access high-dimensional entanglement spanning 63 temporal modes, reaching a maximum photon information efficiency of 5.1 Ebits per detected photon and a peak on-chip photon-memory entanglement rate of 5.6 kEbits per second. These results establish a route toward chip-scale quantum networking hardware operating over telecommunications infrastructure.

quant-ph

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.

quant-ph