A method and apparatus used for general purpose problem solving using entanglement properties of holography. Intelligent point-based entities having spatial and other electromagnetic properties called DROPLETS [Data-Representative-Object-Particle(s)-Liking-EnTanglement] are generated as delegate objects—avatars—connected to data sources representing situations, event or other problems. A DROPLET's properties are controlled by changes in input data, self-state, feedback, and/or changes of other DROPLETS. Coherent rays are introduced and interact with DROPLETS, generating an INTELLIGENCE WAVEFRONT. Interference patterns are recorded and converted to binary machine codes of a near-infinite set, instructing where to store human/machine-readable content within a plurality of associative memories. Said content includes waveforms, harmonics, codes, data, and other holograms, which are dispersed and stored wholistically throughout using spread spectrum techniques. Upon future recognition of like-patterns of situations, events and other problems, the appropriate content components are retrieved and presented as full or partial solutions. Hardware, software, and hybrid embodiments are envisioned.

Features are disclosed for identifying and routing items for tagging using a latent feature model, such as a recurrent neural network language model (RNNLM). The model may be trained to identify latent features for catalog items such as movies, books, food items, beverages, and the like. Based on similarities in latent features, tags previous assigned to items may be applied to untagged items. Application may be manual or automatic. In either case, resources need to be balances to ensure efficient tagging of items. The included features help to identify and direct these limited tagging resources.

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.

According to one embodiment, a calculating device includes a nonlinear oscillator. The nonlinear oscillator includes a circuit part including a first Josephson junction and a second Josephson junction, and a conductive member including a first terminal. An electrical signal is input to the first terminal. The electrical signal includes a first signal in a first operation. The first signal includes a first frequency component having a first frequency, and a second frequency component having a second frequency. The first frequency is 2 times an oscillation frequency of the nonlinear oscillator. An absolute value of a difference between the first frequency and the second frequency is not more than 0.3 times the first frequency.

An optical computing system includes: a light diffraction element group including n pieces of light diffraction elements, where n is a natural number of 2 or more. Each of the n pieces includes cells, each of which has a thickness or a refractive index that is independently set. Each of the cells is classified into a C1 cell or a C2 cell. The thickness or the refractive index of each of the C1 cells is set such that optical computing that is carried out by the light diffraction element group becomes an identity operation when the C2 cells are masked.

Hybrid analog-digital processing systems are described. An example of a hybrid analog-digital processing system includes photonic accelerator configured to perform matrix-vector multiplication using light. The photonic accelerator exhibits a frequency response having a first bandwidth (e.g., less than 3 GHz). The hybrid analog-digital processing system further includes a plurality of analog-to-digital converters (ADCs) coupled to the photonic accelerator, and a plurality of digital equalizers coupled to the plurality of ADCs, wherein the digital equalizers are configured to set a frequency response of the hybrid analog-digital processing system to a second bandwidth greater than the first bandwidth.

A photoelectric computing unit, a photoelectric computing array and a photoelectric computing method. The photoelectric computing unit includes a semiconductor multifunctional region structure, which includes at least one carrier control region, at least one coupling region, and at least one photon-generated carrier collection region and readout region.

An optical computing device and a computing method are provided, to provide an optical Ising machine with high operation efficiency. The optical computing device includes a first spin array, an optical feedback network, and a second spin array, where the optical feedback network is separately connected to the first spin array and the second spin array. The first spin array may receive a first group of signals including N optical pulses or N electrical signals, and generate a first group of spin signals including N spin signals. The optical feedback network may receive the first group of spin signals, and generate, based on the first group of spin signals and specified first data, a first group of feedback signals including N feedback signals. The first spin array and the second spin array may process a plurality of signals in parallel, to improve computation efficiency of the optical computing device.

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.