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.

An optical logic gate decision-making circuit that combines non-linear materials, such as silicon nitride, on a silicon-on-insulator (SOI) substrate is described. Circuitry includes a ring cavity coupled to an input optical bus waveguide. The input optical bus waveguide receives an optical signal and passes the optical signal to the ring cavity. An electro-optical device, for instance a PN junction, is integrated within the ring cavity to modulate the optical signal such that an optical logic gate function is enabled. An output optical bus waveguide is also coupled to the ring cavity, which outputs the optical signal modified based on the optical logic gate function and based on a wavelength routing function. By using silicon nitride, the optical non-linearity of the materials enables an “all-optical” logic gate. Thus, the optical logic gate decision-making circuit is suitable for all-optical circuits, and support ultrafast optical signal processing and enabling packet switching of data.

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.

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.

Systems and methods are disclosed for making a quantum network node. A plurality of scoring function F values are calculated for an array of at least two photonic crystal cavity unit cells, each having a lattice constant a and a hole having a length Hx and a width Hy. A value of a, a value of Hx, and a value of Hy are selected for which a scoring function value is at a maximum. A waveguide region and the array of at least two photonic crystal cavity unit cells based on the selected values are formed on a substrate. At least one ion between a first photonic crystal cavity unit cell and a second photonic crystal cavity unit cell are implanted and annealed into a quantum defect. A coplanar microwave waveguide is formed on the substrate in proximity to the array of at least two photonic crystal cavity unit cells.

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.

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.

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.

An optical information processing system comprising a nonlinear element selected in relation to input optical pulses to initiate nonlinear optical frequency conversion and a detection unit, the nonlinear element receiving encoded information input in form of pulsed light, pulsed light from the nonlinear element being read-out by the detection unit for spectro-temporal feature extraction, and the readout being used to train the system on a specific target to obtain a task-specific output or re-directed to the nonlinear element to obtain an input-dependent output, yielding processed information comprising selective positions in an output of the system. A method for training an optical system comprises, for each individual optical input information, reading specific optical amplitude or phase features from specific output bins of the system in time or frequency, weighting and evaluating the specific features towards optimizing user-defined fitness function to identify, classify, or fit the input information.

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.