Optical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical signals, such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed of light. In this work, we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption. The decryptors, designed for operation in the near-infrared region, are nanoprinted on complementary metal-oxide–semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm1,2, achieving a neuron density of >500 million neurons per square centimetre. This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3, sensing4, medical diagnostics5 and computing6,7.

Neuromorphic computing applies concepts extracted from neuroscience to develop devices shaped like neural systems and achieve brain-like capacity and efficiency. In this way, neuromorphic machines, able to learn from the surrounding environment to deduce abstract concepts and to make decisions, promise to start a technological revolution transforming our society and our life. Current electronic implementations of neuromorphic architectures are still far from competing with their biological counterparts in terms of real-time information-processing capabilities, packing density and energy efficiency. A solution to this impasse is represented by the application of photonic principles to the neuromorphic domain creating in this way the field of neuromorphic photonics. This new field combines the advantages of photonics and neuromorphic architectures to build systems with high efficiency, high interconnectivity and high information density, and paves the way to ultrafast, power efficient and low cost and complex signal processing. In this Perspective, we review the rapid development of the neuromorphic computing field both in the electronic and in the photonic domain focusing on the role and the applications of memristors. We discuss the need and the possibility to conceive a photonic memristor and we offer a positive outlook on the challenges and opportunities for the ambitious goal of realising the next generation of full-optical neuromorphic hardware.

Manipulation of momentum space in photonic structures has enabled a range of physical phenomena including negative refraction, slow light, enhanced nonlinearity and three‐dimensional complete bandgaps. Recently, Topology, a property related to the global structure of the frequency dispersion of a photonic system, emerged as a new tool for the control of momentum space and an additional degree of freedom for the discovery of fundamentally new states of light. The Weyl point systems considered in this work are an excellent platform to investigate topological bosonic states. Weyl points act as monopoles or anti‐monopoles Berry flux in momentum space, and carry chirality defined by quantised topological charges. In this work, the experimental realisation of photonic type I Weyl points at optical frequencies is demonstrated in a bio‐inspired three‐dimensional photonic crystal coated uniquely with layered‐composite nanometric materials. More importantly, the chiral nature of the photonic Weyl points is discovered by coupling with spin‐angular momentum carried by circularly polarised light. This Weyl‐point induced mechanism leads to reversed circular dichroism along the directions that intersect the oppositely charged topological photonic states. This discovery provides an entirely new platform for developing topologically protected super‐robust photonic devices in angular‐momentum‐based information processing, circular‐dichroism‐enabled protein sensing, spintronics and quantum optoelectronics.