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For many years, optics has been employed in computing, although the major focus has been and remains to be on connecting parts of computers, for communications, or more fundamentally in systems that have some optical function or element (optical pattern recognition, etc.). Optical digital computers are still evolving; however, a variety of components that can eventually lead to true optical computers, such as optical logic gates, optical switches, neural networks, and spatial light modulators have previously been developed and are discussed in this paper. High-performance off-the-shelf computers can accurately simulate and construct more complicated photonic devices and systems. These advancements have developed under unusual circumstances: photonics is an emerging tool for the next generation of computing hardware, while recent advances in digital computers have empowered the design, modeling, and creation of a new class of photonic devices and systems with unparalleled challenges. Thus, the review of the status and perspectives shows that optical technology offers incredible developments in computational efficiency; however, only separately implemented optical operations are known so far, and the launch of the world’s first commercial optical processing system was only recently announced. Most likely, the optical computer has not been put into mass production because there are still no good solutions for optical transistors, optical memory, and much more that acceptance to break the huge inertia of many proven technologies in electronics.

As photonic linear computations are diverse and easy to realize while photonic nonlinear computations are relatively limited and difficult, we propose a novel way to perform photonic nonlinear computations by linear operations in a high-dimensional space, which can achieve many nonlinear functions different from existing optical methods. As a practical application, the arbitrary binary nonlinear computations between two Boolean signals are demonstrated to implement a programmable logic array. In the experiment, by programming the high-dimensional photonic matrix multiplier, we execute fourteen different logic operations with only one fixed nonlinear operation. Then the combined logic functions of half-adder and comparator are demonstrated at 10 Gbit/s. Compared with current methods, the proposed scheme simplifies the devices and the nonlinear operations for programmable logic computing. More importantly, nonlinear realization assisted by space transformation offers a new solution for optical digital computing and enriches the diversity of photonic nonlinear computing.

Research on electronic devices and materials is currently driven by both the slowing down of transistor scaling and the exponential growth of computing needs, which make present digital computing increasingly capacity-limited and power-limited. A promising alternative approach consists in performing computing based on intrinsic device dynamics, such that each device functionally replaces elaborate digital circuits, leading to adaptive ‘complex computing’. Memristors are a class of devices that naturally embody higher-order dynamics through their internal electrophysical processes. In this Review, we discuss how novel material properties enable complex dynamics and define different orders of complexity in memristor devices and systems. These native complex dynamics at the device level enable new computing architectures, such as brain-inspired neuromorphic systems, which offer both high energy efficiency and high computing capacity. Memristors are devices that possess materials-level complex dynamics that can be used for computing, such that each memristor can functionally replace elaborate digital circuits. This Review surveys novel material properties that enable complex dynamics and new computing architectures that offer dramatically greater computing efficiency than conventional computers.

Noise is the central obstacle to building large-scale quantum computers. Quantum systems with sufficiently uncorrelated and weak noise could be used to solve computational problems that are intractable with current digital computers. There has been substantial progress towards engineering such systems 1 – 8 . However, continued progress depends on the ability to characterize quantum noise reliably and efficiently with high precision 9 . Here, we describe such a protocol and report its experimental implementation on a 14-qubit superconducting quantum architecture. The method returns an estimate of the effective noise and can detect correlations within arbitrary sets of qubits. We show how to construct a quantum noise correlation matrix allowing the easy visualization of correlations between all pairs of qubits, enabling the discovery of long-range two-qubit correlations in the 14-qubit device that had not previously been detected. Our results are the first implementation of a provably rigorous and comprehensive diagnostic protocol capable of being run on state-of-the-art devices and beyond. These results pave the way for noise metrology in next-generation quantum devices, calibration in the presence of crosstalk, bespoke quantum error-correcting codes 10 and customized fault-tolerance protocols 11 that can greatly reduce the overhead in a quantum computation. A protocol for the reliable, efficient and precise characterization of quantum noise is reported and implemented in an architecture consisting of 14 superconducting qubits. Correlated noise within arbitrary sets of qubits can be easily detected.

Quantum many-body systems with a non-Abelian topological order can host anyonic quasiparticles. It has been proposed that anyons could be used to encode and manipulate information in a topologically protected manner that is immune to local noise, with quantum gates performed by braiding and fusing anyons. Unfortunately, realizing non-Abelian topologically ordered states is challenging, and it was not until recently that the signatures of non-Abelian statistics were observed through digital quantum simulation approaches. However, not all forms of topological order can be used to realize universal quantum computation. Here we use a superconducting quantum processor to simulate non-Abelian topologically ordered states of the Fibonacci string-net model and demonstrate braidings of Fibonacci anyons featuring universal computational power. We demonstrate the non-trivial topological nature of the quantum states by measuring the topological entanglement entropy. In addition, we create two pairs of Fibonacci anyons and demonstrate their fusion rule and non-Abelian braiding statistics by applying unitary gates on the underlying physical qubits. Our results establish a digital approach to explore non-Abelian topological states and their associated braiding statistics with current noisy intermediate-scale quantum processors. Superconducting qubits have been used to realize a quantum many-body state that is capable of universal topological quantum computation.

Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large (N≳106) networks and can be operated at high (gigahertz) speeds and very low (subattojoule) energies per multiply and accumulate (MAC), using the massive spatial multiplexing enabled by standard free-space optical components. In contrast to previous approaches, both weights and inputs are optically encoded so that the network can be reprogrammed and trained on the fly. Simulations of the network using models for digit and image classification reveal a “standard quantum limit” for optical neural networks, set by photodetector shot noise. This bound, which can be as low as50zJ/MAC, suggests that performance below the thermodynamic (Landauer) limit for digital irreversible computation is theoretically possible in this device. The proposed accelerator can implement both fully connected and convolutional networks. We also present a scheme for backpropagation and training that can be performed in the same hardware. This architecture will enable a new class of ultralow-energy processors for deep learning.

The transformation of digital computers from bulky machines to portable systems has been enabled by new materials and advanced processing technologies that allow ultrahigh integration of solid-state electronic switching devices. As this conventional scaling pathway has approached atomic-scale dimensions, the constituent nanomaterials (such as SiO 2 gate dielectrics, poly-Si floating gates and Co–Cr–Pt ferromagnetic alloys) increasingly possess properties that are dominated by quantum physics. In parallel, quantum information science has emerged as an alternative to conventional transistor technology, promising new paradigms in computation, communication and sensing. The convergence between quantum materials properties and prototype quantum devices is especially apparent in the field of 2D materials, which offer a broad range of materials properties, high flexibility in fabrication pathways and the ability to form artificial states of quantum matter. In this Review, we discuss the quantum properties and potential of 2D materials as solid-state platforms for quantum-dot qubits, single-photon emitters, superconducting qubits and topological quantum computing elements. By focusing on the interplay between quantum physics and materials science, we identify key opportunities and challenges for the use of 2D materials in the field of quantum information science. 2D materials exhibit diverse properties and can be integrated in heterostructures: this makes them ideal platforms for quantum information science. This Review surveys recent progress and identifies future opportunities for 2D materials as quantum-dot qubits, single-photon emitters, superconducting qubits and topological quantum computing elements.

Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity—a critical component of computation—remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design’s efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing. An optical accelerator is designed to leverage a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a constant low power (~21 mW), providing a new avenue for optical computing.

The combination of integrated optics technologies with nonlinear photonics, which has led to growth of nonlinear integrated photonics, has also opened the way to groundbreaking new devices and applications. In a companion paper also submitted for publication in this journal, we introduce the main physical processes involved in nonlinear photonics applications and discuss the fundaments of this research area. The applications, on the other hand, have been made possible by availability of suitable materials with high nonlinear coefficients and/or by design of guided-wave structures that can enhance a material’s nonlinear properties. A summary of the traditional and innovative nonlinear materials is presented there. Here, we discuss the fabrication processes and integration platforms, referring to semiconductors, glasses, lithium niobate, and two-dimensional materials. Various waveguide structures are presented. In addition, we report several examples of nonlinear photonic integrated devices to be employed in optical communications, all-optical signal processing and computing, or in quantum optics. We aimed at offering a broad overview, even if, certainly, not exhaustive. However, we hope that the overall work will provide guidance for newcomers to this field and some hints to interested researchers for more detailed investigation of the present and future development of this hot and rapidly growing field.

Antiferromagnetic (AF) and ferrimagnetic (FiM) thin films have burgeoning significance in memory and computing applications due to their robustness and ultrafast and energy-efficient switching dynamics. Mn3Ga features a multitude of spin orders that can be meticulously controlled with stoichiometry, temperature, and strain modulations. In this work, we have carefully designed three suitable stacks of Mn3Ga thin films on MgO (111), STO (111) and STO (111)/Ta substrates deposited across varying substrate temperatures up to 500°C. The delicate interplay of strain and temperature tuning is examined by characterizing their magnetic, crystallographic, and morphological properties. The FiM tetragonal τ-Mn3Ga and AF hexagonal ε-Mn3Ga phases display relatively low saturation magnetizations of 10–60 and ≤ 20 kA/m, respectively. No preferential in-plane or out-of-plane magnetic anisotropy is observed for both τ- and ε-Mn3Ga phases. Critically, we observed that the STO strain-regulated τ-phase is stabilized over a wider temperature window and provides more compact, uniformly dispersed grains with average grain size of ~ 100 nm. This work establishes a sturdy methodology in understanding Mn3Ga thin film growth for eventual AF- and FiM-based memory and computing applications.