Examples described herein relate to concurrently performing operations on optical signals. In an example, a method includes providing, to an optical circuit, a first plurality of signals having a first optical property and encoding a first vector. A second plurality of signals is provided to the circuit that encodes a second vector and has a second optical property that is different from the first optical property. A first attribute-dependent operation is performed on the first plurality of signals via the circuit to perform a first matrix multiplication operation on the first vector, and concurrently, a second attribute-dependent operation is performed on the second plurality of signals to perform a second matrix multiplication operation on the second vector. The first matrix multiplication operation and the second matrix multiplication operation are different based on the first optical property being different from the second optical property.

A device including an integrated computational element (ICE) positioned to optically interact with electromagnetic radiation from a fluid and to thereby generate optically interacted radiation corresponding to a characteristic of the fluid, and a method for using the system are provided. The device includes a detector positioned to receive the optically interacted radiation and to generate an output signal proportional to an intensity of the optically interacted radiation. And the device further includes a processor positioned to receive the output signal and to determine the characteristic of the fluid. The device is coupled to a controller configured to provide instructions to a transfer system for storage and readout.

An optical computing device and a computing method are provided for an optical Ising machine with high operation efficiency. The optical computing device includes a first spin array, an optical feedback network, and a second spin array, where the optical feedback network is separately connected to the first spin array and the second spin array. The first spin array may receive a first group of signals including N optical pulses or N electrical signals, and generate a first group of spin signals including N spin signals. The optical feedback network may receive the first group of spin signals, and generate, based on the first group of spin signals and specified first data, a first group of feedback signals including N feedback signals. The first spin array and the second spin array may process a plurality of signals in parallel, to improve computation efficiency of the optical computing device.

An optical projection system is described that may be used in an optical computing element. The optical projection system has an optical axis and comprises first, second and third lens arrangements. The first and third lens arrangements are rotationally symmetric about the optical axis and are positioned to capture light from an array of sources on a source plane and image the sources onto an output plane. The second lens arrangement is positioned between the first and third lens arrangements. The second lens arrangement has optical power of a first magnitude in a first orientation and optical power of a second magnitude in a second orientation, wherein the first magnitude is larger than the second magnitude and wherein the first orientation is orthogonal to the second orientation and both the first and second orientations are orthogonal to the optical axis.

Systems and methods for performing matrix operations using a photonic processor are provided. The photonic processor includes encoders configured to encode a numerical value into an optical signal and optical multiplication devices configured to output an electrical signal proportional to a product of one or more encoded values. The optical multiplication devices include a first input waveguide, a second input waveguide, a coupler circuit coupled to the first input waveguide and the second input waveguide, a first detector and a second detector coupled to the coupler circuit, and a circuit coupled to the first detector and second detector and configured to output a current that is proportional to a product of a first input value and a second input value.

Described herein are compact, power efficient photonic processors deigned to handle general matrix-matrix (GEMM) operations. A photonic processor may comprise a controller, an optical interferometer, a plurality of signal drivers, and an optical receiver. The controller is configured to obtain a vector of input values and a matrix of parameters. The optical interferometer comprises an output and a plurality of optical phase shifters. Each signal driver of the plurality of signal drivers is configured to control a respective phase shifter to phase shift light traveling in the optical interferometer based on i) a polarity set by a respective parameter of the matrix, and ii) an amount set by a respective input value of the vector. The optical receiver is coupled to the output of the optical interferometer.

Systems and methods for optical multiplication are disclosed. In one arrangement, a first modulator comprising rows and columns of first modulator elements is configured to spatially modulate light received from a deflector. The first modulator encodes values of a first matrix. The first matrix defines a plurality of input vectors each corresponding to a respective row of the first matrix. A second modulator spatially modulates light received from the first modulator and encodes values of a second matrix in rows and columns of second modulator elements. A light-summing optical arrangement converges light output from each row of second modulator elements to encode a plurality of output vectors representing the results of vector-matrix multiplication between a respective plurality of the input vectors and the second matrix.

The exponential growth in deep learning models is challenging existing computing hardware. Optical neural networks (ONNs) accelerate machine learning tasks with potentially ultrahigh bandwidth and nearly no loss in data movement. Scaling up ONNs involves improving scalability, energy efficiency, compute density, and inline nonlinearity. However, realizing all these criteria remains an unsolved challenge. Here, we demonstrate a three-dimensional spatial time-multiplexed ONN architecture based on dense arrays of microscale vertical cavity surface emitting lasers (VCSELs). The VCSELs, coherently injection-locked to a leader laser, operate at gigahertz data rates with a 7T-phase-shift voltage on the 10-millivolt level. Optical nonlinearity is incorporated into the ONN with no added energy cost using coherent detection of optical interference between VCSELs.

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.

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.