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A scalable and reconfigurable hybrid photonic platform integrates multiple wavelength-tunable quantum emitters with a low-loss lithium niobate circuit, achieving on-chip spectral control and quantum interference, a key step towards fully integrated quantum photonic networking and computation on-chip.

Diamond is of interest for optical and electronic applications owing to its unique mechanical and optical properties. Here, Rath et al. demonstrate the use of small nanometre-sized beams etched from diamond thin films for integrated photonic circuits.

A photonic computing platform using chaotic light for probabilistic arithmetic enables ultrafast, parallel processing. The system predicts classification and uncertainty simultaneously. The optical architecture allows efficient distribution evaluations at each output in a single time step.

Carbon nanotubes in a nanocavity offer a route to narrow-linewidth on-chip light emitters.

Here, the authors report the scalable integration of electroluminescent semiconducting carbon nanotubes between nanographene electrical contacts in photonic crystal cavities, showing high coupling efficiency and dynamic electrical control of the enhanced electroluminescent light emission intensity in the telecom range.

The combination of superconducting nanowire single photon detectors and electro-optically reconfigurable circuits in a cryogenic environment is notoriously difficult to reach. Here, the authors realise this on a Lithium-Niobate-On-Insulator platform, reaching high speed modulation at a frequency up to 1 GHz.

Silicon-ion-implanted yttrium iron garnet technology enables low-loss and dispersion-tunable magnonic waveguides with spin-wave decay lengths of >100 µm, which pave the way for large-scale, energy-efficient magnonic integrated circuits.

Single photons are generated from electrically driven semiconducting single-walled carbon nanotubes embedded in a photonic circuit. Pronounced antibunching is observed when photon correlation is measured at cryogenic temperatures.

Two photonic platforms using a convolutional processing system with partially coherent light sources is shown to boost computing parallelism, demonstrated using the classification of gaits of patients with Parkinson’s disease and the MNIST handwritten digits dataset.

A lithography-free photonic processor through dynamic control of optical gain distributions is demonstrated, allowing reconfigurable photonic neural networks and more efficient signal processing, and showing great promise in easing data traffic as well as accelerating information processing speeds.

The authors develop a method to measure the coupling between a single photon source and any arbitrary photonic structure having constant density of electromagnetic states over the linewidth of the emitter. They demonstrate this method by an experiment on a single molecule coupled to an interrupted nanophotonic waveguide.

Physical computing, particularly photonic computing, offers a promising alternative by directly encoding data in physical quantities, enabling efficient probabilistic computing. This Perspective discusses the challenges and opportunities in photonic probabilistic computing and its applications in artificial intelligence.

Hybrid photonic–electronic systems are essential for high-throughput neuromorphic computing. Here, the authors report an in-memory photonic–electronic dot-product engine with decoupled electronic programming of the phase-change memory cells and parallel photonic computation with high-bit operation, low energy consumption, and high accuracy.

Radio-frequency modulation of optical signals increase the parallelization of photonic processors beyond that afforded by exploiting spatial and wavelength dimensions alone. The approach is then demonstrated on electrocardiogram signals and identifies patients at sudden death risk with 93.5% accuracy.

A nanoscale optomechanical resonator in a double-well potential has been excited into the high-amplitude regime, and then optically cooled to a specific at-rest configuration, which allows it to be operated as a non-volatile memory element.

An integrated photonic processor, based on phase-change-material memory arrays and chip-based optical frequency combs, which can operate at speeds of trillions of multiply-accumulate (MAC) operations per second, is demonstrated.

An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.

Researchers use phase-change materials to demonstrate an integrated optical memory with 13.4 pJ switching energy.

Computing approaches in the optical domain would allow for ultra-fast signaling and ultra-high bandwidth capabilities. Here, Feldmann et al. demonstrate a photonic abacus, which provides multistate compute-and store operation by integrating phase-change materials with nanophotonic chips.

Optical analogues of electronic memristors are desirable for applications including photonic artificial intelligence and computing platforms. Here, recent progress on integrated optical memristors is reviewed.

Inanimate matter is beginning to show some signs of basic intelligence—the ability to sense, actuate and use memory, as controlled by an internal communication network in functional materials.

Photonics offers an attractive platform for implementing neuromorphic computing due to its low latency, multiplexing capabilities and integrated on-chip technology.

The Review summarizes the progress of hybrid quantum photonics integration in terms of its important design considerations and fabrication approaches, and highlights some successful realizations of key physical resources for building integrated quantum devices, such as quantum teleporters, quantum repeaters and quantum simulators.