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.

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.

Aspects for forward propagation of a convolutional artificial neural network are described herein. The aspects may include a direct memory access unit configured to receive input data from a storage device and a master computation module configured to select one or more portions of the input data based on a predetermined convolution window. Further, the aspects may include one or more slave computation modules respectively configured to convolute a convolution kernel with one of the one or more portions of the input data to generate a slave output value. Further still, the aspects may include an interconnection unit configured to combine the one or more slave output values into one or more intermediate result vectors, wherein the master computation module is further configured to merge the one or more intermediate result vectors into a merged intermediate vector.

An optical numerical computation device relates light from a plurality of light sources to calculate an arithmetic solution. The optical numerical computation device includes input circuitry, pre-calculation circuitry, calculation circuitry, a light collection cavity, and a plurality of light computation components. The pre-calculation circuitry and calculation circuitry cause light sources to emit light representing the values of input operands, which is subsequently related within the light collection cavity. Sensors then generate resultant outputs at values indicative of the sensed light value. The respective wavelength of light emitted from or sensed by each light computation component may be associated with an operand arithmetic sign.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving a request from a client to process a computational graph; obtaining data representing the computational graph, the computational graph comprising a plurality of nodes and directed edges, wherein each node represents a respective operation, wherein each directed edge connects a respective first node to a respective second node that represents an operation that receives, as input, an output of an operation represented by the respective first node; identifying a plurality of available devices for performing the requested operation; partitioning the computational graph into a plurality of subgraphs, each subgraph comprising one or more nodes in the computational graph; and assigning, for each subgraph, the operations represented by the one or more nodes in the subgraph to a respective available device in the plurality of available devices for operation.

An optical computing device includes: a light diffraction element group including light diffraction elements having an optical computing function, wherein the light diffraction element group passes light through the light diffraction elements in turn, the light passing through the light diffraction elements was either: reflected or scattered from a non-illuminant object located outside the optical computing device, or emitted from an illuminant object located outside the optical computing device, and neither the non-illuminant object nor the illuminant object is a display.

A method includes obtaining a plurality of entangled qubits, with high fault tolerance, represented by a lattice structure. The lattice structure includes a plurality of contiguous lattice cells. A first subset of the plurality of entangled qubits defines a first plane, and a second subset of the plurality of entangled qubits defines a second plane that is parallel to and offset from the first plane. The plurality of entangled qubits includes a defect qubit that is entangled with at least one face qubit on the first plane and at least one edge qubit on the second plane.

An optical operational element which enables a multilayered optical neural network to be constructed without using an optical amplifier is provided. The optical operational element includes: a photothermal conversion unit 30 which converts light energy of input light A into thermal energy; a light intensity variation unit 20 which is in contact with the photothermal conversion unit 30 and which varies, in accordance with a temperature variation accompanying heat generation or heat absorption by the photothermal conversion unit 30, intensity of external light B that is introduced from the outside; and a housing unit 10 which houses the light intensity variation unit 20 and which introduces the external light B from one side and outputs output light C obtained by attenuating intensity of the external light B to the outside on an opposite side to the one side.

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.

An integrated photonics computing system implements a residue number system (RNS) to achieve orders of magnitude improvements in computational speed per watt over the current state-of-the-art. RNS and nanophotonics have a natural affinity where most operations can be achieved as spatial routing using electrically controlled directional coupler switches, thereby giving rise to an innovative processing-in-network (PIN) paradigm. The system provides a path for attojoule-per-bit efficient and fast electro-optic switching devices, and uses them to develop optical compute engines based on residue arithmetic leading to multi-purpose nanophotonic computing.