Abstract: 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.

Abstract: 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.

Abstract: Typically, quantum systems are very sensitive to environmental fluctuations, and diagnosing errors via measurements causes unavoidable perturbations. Here, an in situ frequency-locking technique monitors and corrects frequency variations in single-photon sources based on resonators. By using the classical laser fields used for photon generation as probes to diagnose variations in the resonator frequency, the system applies feedback control to correct photon frequency errors in parallel to the optical quantum computation without disturbing the physical qubit. Our technique can be implemented on a silicon photonic device and with sub 1 pm frequency stabilization in the presence of applied environmental noise, corresponding to a fractional frequency drift of <1% of a photon linewidth. These methods can be used for feedback-controlled quantum state engineering.

Abstract: A process is provided for the high-yield heterogeneous integration of ‘quantum micro-chiplets’ (QMCs, diamond waveguide arrays containing highly coherent color centers) with an aluminum nitride (AlN) photonic integrated circuit (PIC). As an example, the process is useful for the development of a 72-channel defect-free array of germanium-vacancy (GeV) and silicon-vacancy (SiV) color centers in a PIC. Photoluminescence spectroscopy reveals long-term stable and narrow average optical linewidths of 54 MHz (146 MHz) for GeV (SiV) emitters, close to the lifetime-limited linewidth of 32 MHz (93 MHz). Additionally, inhomogeneities in the individual qubits can be compensated in situ with integrated tuning of the optical frequencies over 100 GHz. The ability to assemble large numbers of nearly indistinguishable artificial atoms into phase-stable PICs is useful for development of multiplexed quantum repeaters and general-purpose quantum computers.

Abstract: A process is provided for the high-yield heterogeneous integration of ‘quantum micro-chiplets’ (QMCs, diamond waveguide arrays containing highly coherent color centers) with an aluminum nitride (AlN) photonic integrated circuit (PIC). As an example, the process is useful for the development of a 72-channel defect-free array of germanium-vacancy (GeV) and silicon-vacancy (SiV) color centers in a PIC. Photoluminescence spectroscopy reveals long-term stable and narrow average optical linewidths of 54 MHz (146 MHz) for GeV (SiV) emitters, close to the lifetime-limited linewidth of 32 MHz (93 MHz). Additionally, inhomogeneities in the individual qubits can be compensated in situ with integrated tuning of the optical frequencies over 100 GHz. The ability to assemble large numbers of nearly indistinguishable artificial atoms into phase-stable PICs is useful for development of multiplexed quantum repeaters and general-purpose quantum computers.

Abstract: 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.

Abstract: Many of the features of neural networks for machine learning can naturally be mapped into the quantum optical domain by introducing the quantum optical neural network (QONN). A QONN can be performed to perform a range of quantum information processing tasks, including newly developed protocols for quantum optical state compression, reinforcement learning, black-box quantum simulation and one way quantum repeaters. A QONN can generalize from only a small set of training data onto previously unseen inputs. Simulations indicate that QONNs are a powerful design tool for quantum optical systems and, leveraging advances in integrated quantum photonics, a promising architecture for next generation quantum processors.

Abstract: 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.

Abstract: Typically, quantum systems are very sensitive to environmental fluctuations, and diagnosing errors via measurements causes unavoidable perturbations. Here, an in situ frequency-locking technique monitors and corrects frequency variations in single-photon sources based on resonators. By using the classical laser fields used for photon generation as probes to diagnose variations in the resonator frequency, the system applies feedback control to correct photon frequency errors in parallel to the optical quantum computation without disturbing the physical qubit. Our technique can be implemented on a silicon photonic device and with sub 1 pm frequency stabilization in the presence of applied environmental noise, corresponding to a fractional frequency drift of <1% of a photon linewidth. These methods can be used for feedback-controlled quantum state engineering.

Abstract: 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.

Abstract: 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.

Abstract: 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.