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.

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 for the optical control of qubits and other quantum particles with spatial light modulators (SLM) for quantum computing and quantum simulation are disclosed herein. The system may include a particle system configured to provide an ordered array comprising a multiplicity of quantum particles or a multiplicity of qubits, an optical source, a SLM configured to project a structured illumination pattern capable of individually addressing one or more quantum particles or qubits of the ordered array, and a SLM controller.

Systems and methods for the optical control of qubits and other quantum particles with spatial light modulators (SLM) for quantum computing and quantum simulation are disclosed herein. The system may include a particle system configured to provide an ordered array comprising a multiplicity of quantum particles or a multiplicity of qubits, an optical source, a SLM configured to project a structured illumination pattern capable of individually addressing one or more quantum particles or qubits of the ordered array, and a SLM controller.

Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system and operate in a partially decoupled manner with respect to each other, such as by each decision module operating to synchronize its local solutions and proposed control actions with those of one or more other decision modules, in order to determine a consensus with those other decision modules. Such inter-module synchronizations may occur repeatedly to determine one or more control actions for each decision module at a particular time, as well as to be repeated over multiple times for ongoing control.

A computer-implemented method includes receiving a neural network model that includes a tensor operation, and dividing the tensor operation into sub-operations. The sub-operations includes at least two sub-operations that have no data dependency between the two sub-operations. The computer-implemented method further includes assigning a first sub-operation in the two sub-operations to a first computing engine, assigning a second sub-operation in the two sub-operations to a second computing engine, and generating instructions for performing, in parallel, the first sub-operation by the first computing engine and the second sub-operation by the second computing engine. An inference is then made based on a result of the first sub-operation, a result of the second sub-operation, or both. The first computing engine and the second computing engine are in a same integrated circuit device or in two different integrated circuit devices.

A differential phase modulation Mach-Zehnder optical modulator includes a first phase modulation unit and a second phase modulation unit; an optical interference circuit that receives a polarized clock pulse train that was modulated by the differential phase modulation Mach-Zehnder optical modulator, and allows a predetermined interaction in the Ising model to occur at a period corresponding to the N pulses of the polarized clock pulse train; and a multiplexer/demultiplexer that receives the N initialization optical pulses that create a neutral state with respect to interactions between the elements and receives an output light pulse train from the optical interference circuit, couples the initialization optical pulses with output of the optical interference circuit, demultiplexes the initialization optical pulses and the output light pulse train, outputs a demultiplexed first phase modulation signal to the first phase modulation unit, and outputs a demultiplexed second phase modulation signal to a delay unit.

A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes receiving (i) electricity demand data comprising time series measurements of consumption of electricity by a plurality of consumers, and (ii) electricity supply data comprising time series measurements of availability of electricity by one or more producers. An input matrix is generated that comprises the electricity demand data and the electricity supply data. The CNN is applied to the input matrix to yield a probability of anomaly in the electricity demand data and the electricity supply data. If the probability of anomaly is above a threshold value, an alert message is generated for one or more system operators.

Disclosed is an artificial intelligence (AI) server. The AI server includes a communication unit configured to communicate with an AI device; and an AI unit configured to receive feature data from the AI device, wherein the received feature data is generated by the AI device by obtaining sensing data and compressing the sensing data while preserving a feature of the sensing data; and input the received feature data to a deep learning model to obtain second sensing data for use in a recognition model related to an AI function of the AI device.

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.