Self-controlling photonic-on-chip networks with deep reinforcement learning

We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.

Remote Quantum-Safe Authentication of Entities with Physical Unclonable Functions

Physical unclonable functions have been shown to be a useful resource of randomness for implementing various cryptographic tasks including entity authentication. All the related entity authentication protocols that have been discussed in the literature so far, either they are vulnerable to an emulation attack, or they are limited to short distances. Hence, quantum-safe remote entity authentication over large distances remains an open question. In the first part of this work, we discuss the requirements that an entity authentication protocol has to offer, to be useful for remote entity authentication in practice. Subsequently, we propose a protocol, which can operate over large distances, and offers security against both classical and quantum adversaries. The proposed protocol relies on standard techniques, it is fully compatible with the infrastructure of existing and future photonic networks, and it can operate in parallel with other quantum protocols, including QKD protocols.

Self-Controlling Photonic-on-Chip Networks With Deep Reinforcement Learning

We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.

Spontaneous PT-symmetry breaking in lasing dynamics

Parity-time (PT) symmetric lasers exploit the modulation of optical gain and loss and have led to important fundamental demonstrations in non-Hermitian physics. The current theoretical analysis of PT-symmetric laser physics is performed on the basis of the adiabatic elimination of the medium polarization. This approximation doesn’t hold true for a more general optical system with strong photon-particle interactions, where the Rabi oscillation of active particles plays a non-negligible role in the lasing action. Here, we propose a model that takes into account the internal dynamics of active particles and numerically investigate the PT symmetry of macroscopic- and microscopic-sized laser systems that operate in the strong-coupling regime. The distinct phase diagrams are drawn according to the features of intracavity photon numbers and emission spectra. Our work extends the PT-symmetric optics from the weak- to the strong-coupling limit, potentially paving the way towards nonclassical PT-symmetric light sources for integrated photonic networks and ultrasensitive sensors.