Related papers: Scalable optical neural network with nonlocally co…
A feed-forward photonic neural network (PNN) is tested for chromatic dispersion compensation in Intensity Modulation/Direct Detection optical links. The PNN is based on a sequence of linear and nonlinear transformations. The linear stage is…
Integrated photonic neural networks (PNNs) have demonstrated significant potential to complement the digital electronic counterparts [1-3]. Nevertheless, robust and repeatable performance of scalable integrated PNNs is directly tied to the…
General-purpose programmable photonic processors are considered a crucial technology because they combine the ultra high-speed, massive bandwidth, and energy efficiency of light-based computing with the flexibility of software-defined…
Convolutional Neural Networks (CNNs) have advanced significantly in visual representation learning and recognition. However, they face notable challenges in performance and computational efficiency when dealing with real-world, multi-scale…
Microwave-to-optical transduction of single photons will play an essential role in interconnecting future superconducting quantum devices, with applications in distributed quantum computing and secure communications. Various transducers…
Optical neural networks (ONNs) are emerging as a promising neuromorphic computing paradigm for object recognition, offering unprecedented advantages in light-speed computation, ultra-low power consumption, and inherent parallelism. However,…
High-speed optical receivers are crucial in modern optical communication systems. While complex photonic integrated circuits (PICs) are widely employed to harness the full degrees of freedom (DOFs) of light for efficient data transmission,…
General-purpose programmable photonic processors offer a flexible foundation for integrating various functionalities within a single chip. A two-dimensional hexagonal waveguide mesh of Mach Zehnder interferometers has been shown to have…
The growing computational demands of classical neural networks have intensified the search for energy-efficient and powerful computational alternatives. Quantum neural networks (QNNs) implemented on integrated photonic platforms offer a…
Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and high energy efficiency. However, current photonic tensor core (PTC) designs based on…
Programmable photonic integrated circuits (PICs) have recently emerged as an important technology for quantum information science and artificial neural networks. In particular, PICs with MEMS-based modulators have the advantages of…
Solid-state quantum emitters are excellent sources of on-demand indistinguishable or entangled photons and can host long-lived spin memories, crucial resources for photonic quantum information applications. However, their scalability…
Photonic integrated circuits (PICs) play a pivotal role in many applications. Particularly powerful are circuits based on meshes of reconfigurable Mach-Zehnder interferometers as they enable active processing of light. Various possibilities…
We demonstrate, for the first time, non-volatile charge-trap flash memory (CTM) co-located with heterogeneous III-V/Si photonics. The wafer-bonded III-V/Si CTM cell facilitates non-volatile optical functionality for a variety of devices…
Quantum states of light play a pivotal role in modern science[1] and future photonic applications[2]. While impressive progress has been made in their generation and manipulation with high fidelities, the common table-top approach is…
Programmable integrated photonics (PIP) has emerged as a powerful on-chip platform for optical signal processing and computing, enabling the implementation of reconfigurable N$\times$N unitary matrix transformations through meshes of…
We report a novel approach to the design and implementation of a tunable cavity on an optical nanofiber (ONF). The key point is to create a composite photonic crystal cavity (CPCC), by combining an ONF and a chirped period defect mode…
Photonic convolutional accelerators have emerged as low-energy alternatives to power-demanding digital convolutional neural networks, though they often face limitations in scalability. In this work, we introduce a convolutional photonic…
Neuromorphic photonic computing represents a paradigm shift for next-generation machine intelligence, yet critical gaps persist in emulating the brain's event-driven, asynchronous dynamics,a fundamental barrier to unlocking its full…
We introduce a novel parameterization of complex unitary matrices, which allows for the efficient photonic implementation of arbitrary linear discrete unitary operators. The proposed architecture is built on factorizing an $N \times N$…