A photonic quantum memory is provided. The photonic quantum memory includes entanglement basis conversion module configured to receive a first polarization-entangled photon pair and to produce a second entangled photon pair. The second polarization-entangled photon pair can be a time-bin entangled or a propagation direction-entangled photon pair. The photonic quantum memory further includes a photonic storage configured to receive the second entangled photon pair from the basis conversion module and to store the second entangled photon pair.

A photonic quantum memory is provided. The photonic quantum memory includes entanglement basis conversion module configured to receive a first polarization-entangled photon pair and to produce a second entangled photon pair. The second polarization-entangled photon pair can be a time-bin entangled or a propagation direction-entangled photon pair. The photonic quantum memory further includes a photonic storage configured to receive the second entangled photon pair from the basis conversion module and to store the second entangled photon pair.

One embodiment of the invention provides a system for mapping a neural network onto a neurosynaptic substrate. The system comprises a metadata analysis unit for analyzing metadata information associated with one or more portions of an adjacency matrix representation of the neural network, and a mapping unit for mapping the one or more portions of the matrix representation onto the neurosynaptic substrate based on the metadata information.

Model-based neural control uses a model of a portion of a brain and provides feedback control to the model that is based on a received output from the model. A neuromorphic model-based control system includes a neuromorphic model that includes a neuromorphic network to model the brain portion. A synaptic time-multiplexed (STM) neural model-based control system includes an STM neural network to the model the brain portion. The control systems further include a feedback controller to receive an output of the neuromorphic model or STM neural network and to provide a feedback control input to control a model state of the neuromorphic model or the STM neural network.

The disclosure describes various aspects of optical control of atomic quantum bits (qubits) for phase control operations. More specifically, the disclosure describes methods for coherently controlling quantum phases on atomic qubits mediated by optical control fields, applying to quantum logic gates, and generalized interactions between qubits. Various attributes and settings of optical/qubit interactions (e.g., atomic energy structure, laser beam geometry, polarization, spectrum, phase, background magnetic field) are identified for imprinting and storing phase in qubits. The disclosure further describes how these control attributes are best matched in order to control and stabilize qubit interactions and allow extended phase-stable quantum gate sequences.

The disclosure describes various aspects of optical control of atomic quantum bits (qubits) for phase control operations. More specifically, the disclosure describes methods for coherently controlling quantum phases on atomic qubits mediated by optical control fields, applying to quantum logic gates, and generalized interactions between qubits. Various attributes and settings of optical/qubit interactions (e.g., atomic energy structure, laser beam geometry, polarization, spectrum, phase, background magnetic field) are identified for imprinting and storing phase in qubits. The disclosure further describes how these control attributes are best matched in order to control and stabilize qubit interactions and allow extended phase-stable quantum gate sequences.

In some illustrative embodiments, a self-powered system is provided that implements a Quantum Signal Generator where said signal generator is powered by a system with no external electrical connections. Other embodiments are as described above.

In some illustrative embodiments, a self-powered system is provided that implements a Quantum Signal Generator where said signal generator is powered by a system with no external electrical connections. Other embodiments are as described above.

The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject.

Examples described herein relate to concurrently performing operations on optical signals. In an example, a method includes providing, to an optical circuit, a first plurality of signals having a first optical property and encoding a first vector. A second plurality of signals is provided to the circuit that encodes a second vector and has a second optical property that is different from the first optical property. A first attribute-dependent operation is performed on the first plurality of signals via the circuit to perform a first matrix multiplication operation on the first vector, and concurrently, a second attribute-dependent operation is performed on the second plurality of signals to perform a second matrix multiplication operation on the second vector. The first matrix multiplication operation and the second matrix multiplication operation are different based on the first optical property being different from the second optical property.