A combinatorial optimization problem processing device is for associating a combinatorial optimization problem having N elements with an Ising model to process the combinatorial optimization problem. The combinatorial optimization problem processing device includes: a 1×2 Mach-Zehnder optical modulator that receives a polarized clock pulse train; an optical interference circuit that receives polarized clock pulse trains that were modulated by the Mach-Zehnder optical modulator; an optical coupler that couples output of the optical interference circuit with an initialization optical pulse train that creates a neutral state with respect to interactions between the elements; and a modulation signal generator that performs waveform shaping on an electrical signal obtained by photoelectrically converting an output signal of the optical coupler, generates a modulation signal for the Mach-Zehnder optical modulator, and externally outputs a monitor signal that represents a solution to the optimization problem. The optical interference circuit repeatedly allows a predetermined interaction in the Ising model to occur from the neutral state at a period corresponding to the N pulses of the polarized clock pulse train.

An optical computing chip includes a light source array, a first concave mirror, and a modulator array. The light source array is located on an objective focal plane of the first concave mirror. The modulator array is located on an image focal plane of the first concave mirror. The light source array generates a first optical signal based on first data. The first concave mirror outputs a first reflected optical signal based on the first optical signal. The modulator array receives the first reflected optical signal, obtains first spectrum plane distribution data based on the first reflected optical signal, and modulates the first spectrum plane distribution data onto the modulator array.

A method and a system for determining a guided random data sampling are disclosed. The method comprises: converting input data into an optical signal with a variable average intensity through a first conversion module; converting the optical signal into a guided optical signal according to guiding data by a photonic computing module, wherein the average intensity of the guided optical signal varies with the average intensity of the optical signal; and converting the guided optical signal into output data and outputting the output data by a second conversion module; wherein the noise generated by at least one of the first conversion module, the photonic computing module, and the second conversion module is added to the output data as a perturbation. By yielding the perturbation in the optical-analog domain, the output data can be quickly converged to an expected solution in the solving operation of the combinatorial optimization problem.

A quantum information processing system comprises a light source, a detector, at least one spatial light modulator and at least one optical lens. The light source is configured to provide a beam of entangled photons. The at least one optical lens is configured to project the resultant beam onto the spatial light modulator, either by direct imaging or by performing a full or partial optical Fourier transform. Said spatial light modulator includes a plurality of discrete pixels and is configured to select one or more of the plurality of discrete pixels to generate a resultant beam from said beam of entangled photons. The resultant beam from said spatial light modulator is projected onto the detector. For optical computation, such as search algorithms, the configuration and projections are repeated to find the optimal solution.

Provided is an optical signal processing apparatus capable of improving computing accuracy without increasing the number of nodes of a reservoir layer. An optical signal processing apparatus for converting an input one-dimensional signal to an optical signal to perform signal processing includes: an input unit configured to perform linear processing on the input one-dimensional signal to convert the input one-dimensional signal to an optical signal of multi-wavelength; a reservoir unit connected to an output of the input unit and configured to perform linear processing and nonlinear processing on the optical signal; and an output unit connected to an output of the reservoir unit and configured to convert the optical signal to an electrical signal and perform linear processing to output a one-dimensional output.

Systems and methods for distributed training of deep learning models are disclosed. An example local device to train deep learning models includes a reference generator to label input data received at the local device to generate training data, a trainer to train a local deep learning model and to transmit the local deep learning model to a server that is to receive a plurality of local deep learning models from a plurality of local devices, the server to determine a set of weights for a global deep learning model, and an updater to update the local deep learning model based on the set of weights received from the server.

Systems and methods that include: providing input information in an electronic format; converting at least a part of the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. In some examples, a set of multiple input values are encoded on respective optical signals carried by optical waveguides. For each of at least two subsets of one or more optical signals, a corresponding set of one or more copying modules splits the subset of one or more optical signals into two or more copies of the optical signals. For each of at least two copies of a first subset of one or more optical signals, a corresponding multiplication module multiplies the one or more optical signals of the first subset by one or more matrix element values using optical amplitude modulation. For results of two or more of the multiplication modules, a summation module produces an electrical signal that represents a sum of the results of the two or more of the multiplication modules.

Embodiments are provided herein for efficient component communication and resource optimization in a disaggregated computing system. A general purpose link is provided to connect a computing element to a plurality of other computing elements of the disaggregated computing system. The general purpose link is dynamically switched between a plurality of different hardware protocols to communicate with the other computing elements, where respective ones of the other computing elements comprise different types of hardware elements.

Methods, systems and apparatus for simulating quantum systems. In one aspect, a method includes the actions of obtaining a first Hamiltonian describing the quantum system, wherein the Hamiltonian is written in a plane wave basis comprising N plane wave basis vectors; applying a discrete Fourier transform to the first Hamiltonian to generate a second Hamiltonian written in a plane wave dual basis, wherein the second Hamiltonian comprises a number of terms that scales at most quadratically with N; and simulating the quantum system using the second Hamiltonian.

This application discloses an optical apparatus for quantum computing, a system, a method and a storage medium. The apparatus includes: a delay generation module configured to generate different time delays for n photons respectively; an optical fiber collimation module configured to convert light rays of the n photons into n collimated light rays to propagate; a quasi-spatial mode generation module configured to enable the light rays of the n photons to pass through the same active optical element in sequence, the active optical element being configured to modulate the optical signals of the n photons in sequence according to the time sequence in which the n photons arrive; and a feedforward measurement module configured to perform polarimetry on a modulated optical signal of a first photon to obtain a measurement result for performing feedforward compensation or feedforward error correction on a measurement result of a second photon to be measured.