A system for photonic computing, preferably including an input module, computation module, and/or control module, wherein the computation module preferably includes one or more filter banks and/or detectors. A photonic filter bank system, preferably including two waveguides and a plurality of optical filters optically coupled to one or more of the waveguides. A method for photonic computing, preferably including controlling a computation module, controlling an input module, and/or receiving outputs from the computation module.

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.

The disclosure describes an adaptive and optimal imaging of individual quantum emitters within a lattice or optical field of view for quantum computing. Advanced image processing techniques are described to identify individual optically active quantum bits (qubits) with an imager. Images of individual and optically-resolved quantum emitters fluorescing as a lattice are decomposed and recognized based on fluorescence. Expected spatial distributions of the quantum emitters guides the processing, which uses adaptive fitting of peak distribution functions to determine the number of quantum emitters in real time. These techniques can be used for the loading process, where atoms or ions enter the trap one-by-one, for the identification of solid-state emitters, and for internal state-detection of the quantum emitters, where each emitter can be fluorescent or dark depending on its internal state. This latter application is relevant to efficient and fast detection of optically active qubits in quantum simulations and quantum computing.

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.

In one aspect, a computational machine includes an optical device configured to receive energy from an optical energy source and generate a number N1 of optical signals, and a number N2 of coupling devices, each of which controllably couples a plurality of the number N1 optical signals. The coupling devices are individually controlled to simulate a computational problem. In another aspect, a computational machine includes a number N1 of parametric oscillators and a number N2 of coupling devices, each of which controllably couples a plurality of the number N1 of parametric oscillators together. The coupling devices are individually controlled to simulate a computational problem.

In an Ising model calculation device for computing a generalized Ising model expressed by a Hamiltonian having a magnetic field term, the magnetic field term is applied to spins simulated by state monitoring light pulses, and a response of the obtained light pulses is determined by fitting to perform state monitoring during application of a magnetic field term.

In an Ising model calculation device for computing a generalized Ising model expressed by a Hamiltonian having a magnetic field term, the magnetic field term is applied to spins simulated by state monitoring light pulses, and a response of the obtained light pulses is determined by fitting to perform state monitoring during application of a magnetic field term.

An optical computing system includes: a light diffraction element divided into blocks and including cells having respective thicknesses or refractive indices set independently of each other, wherein each of the blocks includes: a first cell of the cells having a thickness or a refractive index such that first optical computing is carried out and, a second cell of the cells having a thickness or a refractive index such that second optical computing is carried out; a light-emitting device including light-emitting cells corresponding to each of the blocks, that generates signal light, and that emits the signal light to the light diffraction element; and a light-receiving device including light-receiving cells corresponding to each of the cells of the light diffraction element, and that detects the signal light from the light diffraction element.

An optical computing system includes: a light diffraction element divided into blocks and including cells having respective thicknesses or refractive indices set independently of each other, wherein each of the blocks includes: a first cell of the cells having a thickness or a refractive index such that first optical computing is carried out and, a second cell of the cells having a thickness or a refractive index such that second optical computing is carried out; a light-emitting device including light-emitting cells corresponding to each of the blocks, that generates signal light, and that emits the signal light to the light diffraction element; and a light-receiving device including light-receiving cells corresponding to each of the cells of the light diffraction element, and that detects the signal light from the light diffraction element.

The disclosure describes an adaptive and optimal imaging of individual quantum emitters within a lattice or optical field of view for quantum computing. Advanced image processing techniques are described to identify individual optically active quantum bits (qubits) with an imager. Images of individual and optically-resolved quantum emitters fluorescing as a lattice are decomposed and recognized based on fluorescence. Expected spatial distributions of the quantum emitters guides the processing, which uses adaptive fitting of peak distribution functions to determine the number of quantum emitters in real time. These techniques can be used for the loading process, where atoms or ions enter the trap one-by-one, for the identification of solid-state emitters, and for internal state-detection of the quantum emitters, where each emitter can be fluorescent or dark depending on its internal state. This latter application is relevant to efficient and fast detection of optically active qubits in quantum simulations and quantum computing.