Method and system for intelligent decision-making photonic signal processing, where the system comprises a multi-functional input unit, an electro-optical conversion module, a signal processing module, a photoelectric conversion module, a multi-functional output unit, and an artificial intelligence chip. The invention combines the advantages of photonic high-speed, wide-band, and electronic flexibility, combined with heterogeneous photoelectron hybrid integration, packaging and other processes, along with deep learning algorithm, is an intelligent electronic information system that may simultaneously realize digital and analog signal processing.

A quantum computing system comprises an input port for receiving a modulated data stream comprising a plurality of bits. Orbital angular momentum processing circuitry receives the modulated data stream and applying at least one of at least three orbital angular momentum function modes to each of the plurality of bits of the modulated data stream to generate a qudit. The qudit comprises a quantum unit of information having any of d states where d has a value of at least 3. Each of the at least three orbital angular momentum function modes comprise separate orbital angular momentum states that are orthogonal to each other. A MicroElectroMechanical system (MEMS) circuitry associated with the orbital angular momentum processing circuitry generates a hologram for applying the at least one of the at least three orbital angular momentum function modes to each of the plurality of bits of the modulated data stream to generate the qudit. At least one quantum gate receives each of the qudits via at least one gate input and generates a quantum circuit output via at least one gate output responsive thereto. An output port for outputs the generated quantum circuit output.

An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.

Methods, devices, and systems for processing information are disclosed. An example device may comprise a metastructure comprising a plurality of physical features configured to transform an analog signal according to a kernel of an integral equation. The device may comprise one or more waveguides coupled to the metastructure and configured to recursively supply a transformed analog output signal of the metastructure to an input of the metastructure to iteratively cause one or more transformed analog signals output from the metastructure to converge to an analog signal representing a solution to the integral equation.

A system may include an optical source and an adjustable spatial light modulator coupled to the optical source. The system may further include a medium coupled to the adjustable spatial light modulator, and an optical detector coupled to the medium. The optical detector may obtain various optical signals that are transmitted through the medium at various predetermined spatial light modulations using the adjustable spatial light modulator. The system may further include a controller coupled to the optical detector and the adjustable spatial light modulator. The controller may train an electronic model using various synthetic gradients based on the optical signals.

The ability to rapidly identify symmetry and anti-symmetry is an essential attribute of intelligence. Symmetry perception is a central process in human vision and may be key to human 3D visualization. While previous work in understanding neuron symmetry perception has concentrated on the neuron as an integrator, the invention provides the coincidence detecting property of the spiking neuron can be used to reveal symmetry density in spatial data. A synchronized symmetry-identifying spiking artificial neural network enables layering and feedback in the network. The network of the invention can identify symmetry density between sets of data and present a digital logic implementation demonstrating an 8×8 leaky-integrate-and-fire symmetry detector in a field-programmable gate array. The efficiency of spiking neural networks can be harnessed to rapidly identify symmetry in spatial data with applications in image processing, 3D computer vision, and robotics.

An optical numerical computation method obtains operands that have respective values, and modulates light sources to output light at amplitudes proportional to the operands. The light output for a given operand depends on whether the operand is positive or negative. The positive operands are output at wavelengths different from the negative operands. For operands that have multiple digits, the digits are separately treated so that the least significant digits are modulated with light sources at one frequency, and the most significant digits in two-digit numbers are modulated at another frequency, with positive and negative operands modulated at different frequencies. The light from the light sources enters a light collection cavity where it is sensed with sensors that generate resultant outputs at values indicative of the sensed light value.

Embodiments of the present disclosure are directed toward techniques and configurations for optical couplers comprising a first optical waveguide and a second optical waveguide coupled to form a 2×2 optical unitary matrix to receive a respective first input optical signal and a second input optical signal. In embodiments the first optical waveguide and second optical waveguide form arms that converge alongside each other to direct the first input optical signal and the second input optical signal along a path that integrates a plurality of tunable phase shifters to transform the first input optical signal or the second input optical signal into a first output optical signal and second output optical signal to be output from the 2×2 optical unitary matrix. Additional embodiments may be described and claimed.

Embodiments described herein are generally related to a method and a system for performing a computation using a hybrid quantum-classical computing system, and, more specifically, to providing an approximate solution to an optimization problem using a hybrid quantum-classical computing system that includes a group of trapped ions. A hybrid quantum-classical computing system that is able to provide a solution to a combinatorial optimization problem may include a classical computer, a system controller, and a quantum processor. The methods and systems described herein include an efficient and noise resilient method for constructing trial states in the quantum processor in solving a problem in a hybrid quantum-classical computing system, which provides improvement over the conventional method for computation in a hybrid quantum-classical computing system.

Embodiments of the present disclosure are directed to a photonic implementation of a processor for keys update and hash generation for digital currency (e.g., bitcoin) transactions. The processor includes a first photonic circuit and a second photonic circuit coupled to the first photonic circuit via a set of optical connections. The first photonic circuit is configured to generate a plurality of new messages based at least in part on a plurality of input messages. During a plurality of operational cycles, the second photonic circuit is configured to receive, from the first photonic circuit via the set of optical connections, the plurality of new messages, and update a plurality of keys based at least in part on the received plurality of new messages. The second photonic circuit is further configured to generate at least one hash value based on the plurality of keys generated after the plurality of operational cycles.