Embodiments of the present disclosure are directed toward techniques and configurations for an optical accelerator including a photonics integrated circuit (PIC) for an optical neural network (ONN). In embodiments, an optical accelerator package includes the PIC and an electronics integrated circuit (EIC) that is heterogeneously integrated into the optical accelerator package to proximally provide pre- and post-processing of optical signal inputs and optical signal outputs provided to and received from an optical matrix multiplier of the PIC. In some embodiments, the EIC is a single EIC or discrete EICs to provide pre- and post-processing of the optical signal inputs and optical signal outputs including optical to electrical and electrical to optical transduction. Other embodiments may be described and/or claimed.

Embodiments of the present disclosure are directed toward techniques and configurations for optical couplers comprising a first optical waveguide and a second optical waveguide coupled to form a 22 optical unitary matrix to receive a respective first input optical signal and a second input optical signal. In embodiments the first optical waveguide and second optical waveguide form arms that converge alongside each other to direct the first input optical signal and the second input optical signal along a path that integrates a plurality of tunable phase shifters to transform the first input optical signal or the second input optical signal into a first output optical signal and second output optical signal to be output from the 22 optical unitary matrix. Additional embodiments may be described and claimed.

Systems and methods performed for generating authentication information for an image using optical computing are provided. When a user takes a photo of an object, an optical authentication system receives light reflected and/or emitted from the object. The system also receives a random key from an authentication server. The system converts the received light to plenoptic data and uploads it to the authentication server. In addition, the system generates an optical hash of the received light using the random key, converts the generated optical hash to a digital optical hash, and uploads the digital optical hash to the authentication server. When the authentication server receives the upload, it verifies whether the time of the upload is within a certain threshold time from the sending of the random key and whether the digital optical hash was generated from the same light as the plenoptic data.

There is provided an optical signal processing device capable of RC in a complex space using optical intensity and phase information. An optical modulator controlled by an electric signal processing circuit modulates laser light, which is emitted from a laser light source, at a modulation period either or both of the intensity and phase values of the optical electric field. On the other hand, an input signal is also modulated by the optical modulator at a modulation period in the time domain so as to be an input signal. The converted input signal passes through an optical transmission path and enters an optical circulation circuit via an optical coupler. Part of the circulating light is branched into two by an optical coupler, and the branched light is converted into a complex intermediate signal at a coherent optical receiver. This complex intermediate signal demodulated at the coherent optical receiver is computed at an electric signal processing circuit, and thereby the operation as RC can be performed.

There is provided an optical signal processing device capable of RC in a complex space using optical intensity and phase information. An optical modulator controlled by an electric signal processing circuit modulates laser light, which is emitted from a laser light source, at a modulation period either or both of the intensity and phase values of the optical electric field. On the other hand, an input signal is also modulated by the optical modulator at a modulation period in the time domain so as to be an input signal. The converted input signal passes through an optical transmission path and enters an optical circulation circuit via an optical coupler. Part of the circulating light is branched into two by an optical coupler, and the branched light is converted into a complex intermediate signal at a coherent optical receiver. This complex intermediate signal demodulated at the coherent optical receiver is computed at an electric signal processing circuit, and thereby the operation as RC can be performed.

A method for forming a wellbore in an earth formation includes positioning a drill string in a wellbore; the drill string including a bottom hole assembly (BHA) that includes a steering unit, one or more sensors responsive to one or more formation properties, and one or more sensors responsive to the current orientation of the BHA in a wellbore. The method also includes receiving information from the BHA related to the formation properties and information related to a current orientation of the BHA in the wellbore and processing the information using computing device that is either a programmable optical computing device or a quantum computing device. The computing device calculates the position of formation features with respect to current wellbore position in real time and compare the current position to a prescribed path.

Systems and methods for performing matrix operations using a photonic processor are provided. The photonic processor includes encoders configured to encode a numerical value into an optical signal and optical multiplication devices configured to output an electrical signal proportional to a product of one or more encoded values. The optical multiplication devices include a first input waveguide, a second input waveguide, a coupler circuit coupled to the first input waveguide and the second input waveguide, a first detector and a second detector coupled to the coupler circuit, and a circuit coupled to the first detector and second detector and configured to output a current that is proportional to a product of a first input value and a second input value.

An optical operational element which enables a multilayered optical neural network to be constructed without using an optical amplifier is provided. The optical operational element includes: a photothermal conversion unit 30 which converts light energy of input light A into thermal energy; a light intensity variation unit 20 which is in contact with the photothermal conversion unit 30 and which varies, in accordance with a temperature variation accompanying heat generation or heat absorption by the photothermal conversion unit 30, intensity of external light B that is introduced from the outside; and a housing unit 10 which houses the light intensity variation unit 20 and which introduces the external light B from one side and outputs output light C obtained by attenuating intensity of the external light B to the outside on an opposite side to the one side.

Among other things, an imaging sensor includes a two-dimensional array of photosensitive elements and a surface to receive a sample within a near-field distance of the photosensitive elements. Electronics classify microbeads in the sample as belonging to different classes based on the effects of different absorption spectra of the different classes of microbeads on light received at the surface. In some examples, the number of different distinguishable classes of microbeads can be very large based on combinations of the effects on light received at the surface of the different absorption spectra together, spatial arrangements of colorants in the microbeads that impart the different absorption spectra, different sizes of microbeads, and different shapes of microbeads, among other things.

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.