arXiv subjects

Beomjoon Chae

Publications and source records attributed to Beomjoon Chae.

2 recordsLinked to original sources

arXiv2026

Fully programmable slow light based on a spinor representation of generalized coupled-resonator-induced transparency

Electromagnetically induced transparency (EIT), arising from quantum interference in coherently driven atomic systems, has inspired a variety of photonic analogues, such as coupled-resonator-induced transparency (CRIT) built on the quantum-state modelling using resonators. Although CRIT serves as a building block for slow light in photonic integrated circuits, recent advances in topological photonics motivate a further generalization of both EIT and CRIT using gauge-field degrees of freedom. Here, we propose generalized CRIT via a spinor representation with dual-channel gauge fields, enabling fully programmable CRIT featuring dynamical spectral engineering. We generalize the traditional EIT framework by introducing a spinor representation of bright- and dark-mode resonances, yielding a unified description of design parameters through universal unitary operations. Implementing a coupled-resonator building block that accesses the entire design space through dual-channel gauge fields, we demonstrate a programmable slow-light band in a one-dimensional CRIT lattice. These results address urgent needs in optical interconnects, such as tunable delay lines, reconfigurable synchronization, and linear frequency conversion.

physics.optics↗

arXiv2026

Scalable Memory Sharing in Photonic Quantum Memristors for Reservoir Computing

Although photons are robust, room-temperature carriers well suited to quantum machine learning, the absence of photon-photon interactions hinder the realization of memory functionalities that are critical for capturing long-range context. Recently, measurement-based implementations of photonic quantum memristors (PQMRs) have enabled tunable non-Markovian responses. However, their memory remains confined to local elements, in contrast to biological or artificial networks where memory is shared across the system. Here, we propose a scalable PQMR network that enables measurement-based memory sharing. Each memristive node updates its internal state using the history of its own and neighbouring quantum states, thereby realizing distributed memory. By modelling each node as a photonic quantum memtransistor, we demonstrate pronounced enhancements in both classical and quantum hysteresis at the device level, as well as enhanced network-level quantum hysteresis. Implemented as a quantum reservoir, the architecture achieves improved Fashion-MNIST classification accuracy and confidence via increased data separability. Our approach paves the way toward high-capacity quantum machine learning using memristive devices compatible with linear-optical quantum computing.

quant-ph↗