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.

An architecture for fault-tolerant universal quantum computation is suited for matter qubits, such as donor qubits in silicon, coupled by a network of photonic interconnects. The basic operational building blocks are local measurements and unitaries, plus an entangling measurement of non-local Pauli operators. 3D graph states created by applying deterministic entangling measurements to pairs of qubits in knitting and fusion processes to yield resource states for one way computing. The deterministic entangling measurements are facilitated by configuring the network with active switches to allow single photons to interact with pairs of matter qubits.

An architecture for fault-tolerant universal quantum computation is suited for matter qubits, such as donor qubits in silicon, coupled by a network of photonic interconnects. The basic operational building blocks are local measurements and unitaries, plus an entangling measurement of non-local Pauli operators. 3D graph states created by applying deterministic entangling measurements to pairs of qubits in knitting and fusion processes to yield resource states for one way computing. The deterministic entangling measurements are facilitated by configuring the network with active switches to allow single photons to interact with pairs of matter qubits.

The disclosure describes various aspects of optical control of atomic quantum bits (qubits) for phase control operations. More specifically, the disclosure describes methods for coherently controlling quantum phases on atomic qubits mediated by optical control fields, applying to quantum logic gates, and generalized interactions between qubits. Various attributes and settings of optical/qubit interactions (e.g., atomic energy structure, laser beam geometry, polarization, spectrum, phase, background magnetic field) are identified for imprinting and storing phase in qubits. The disclosure further describes how these control attributes are best matched in order to control and stabilize qubit interactions and allow extended phase-stable quantum gate sequences.

The disclosure describes various aspects of optical control of atomic quantum bits (qubits) for phase control operations. More specifically, the disclosure describes methods for coherently controlling quantum phases on atomic qubits mediated by optical control fields, applying to quantum logic gates, and generalized interactions between qubits. Various attributes and settings of optical/qubit interactions (e.g., atomic energy structure, laser beam geometry, polarization, spectrum, phase, background magnetic field) are identified for imprinting and storing phase in qubits. The disclosure further describes how these control attributes are best matched in order to control and stabilize qubit interactions and allow extended phase-stable quantum gate sequences.

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.

The present disclosure relates to a field of photonic computing and provides a photonic computing system including: a photonic computing unit configured to receive a first plurality of optical signals, wherein the first plurality of the optical signals represent a first set of values respectively. The photonic computing unit includes a plurality of weight modules, the weight modules represent a plurality of predetermined values respectively, and each of the weight modules corresponds to one of the predetermined values. Each of the weight modules includes: an optical input part configured to receive one optical signal of the first plurality of the optical signals, and at least one directional coupler. The weight module corresponds to one of the predetermined values to achieve the multiplication operation.

The present disclosure relates to a field of photonic computing and provides a photonic computing system including: a photonic computing unit configured to receive a first plurality of optical signals, wherein the first plurality of the optical signals represent a first set of values respectively. The photonic computing unit includes a plurality of weight modules, the weight modules represent a plurality of predetermined values respectively, and each of the weight modules corresponds to one of the predetermined values. Each of the weight modules includes: an optical input part configured to receive one optical signal of the first plurality of the optical signals, and at least one directional coupler. The weight module corresponds to one of the predetermined values to achieve the multiplication operation.

A memory component can include memory cells where a first region of the memory cells is to store a machine learning model and a second region of the memory cells is to store input data and output data of a machine learning operation. A controller can be coupled to the memory component with one more internal buses to perform the machine learning operation by applying the machine learning model to the input data to generate the output data.