In one embodiment, a computer program product for optimizing core utilization in a neurosynaptic network includes a computer readable storage medium having program instructions embodied therewith, where the computer readable storage medium is not a transitory signal per se, and where the program instructions are executable by a processor to cause the processor to perform a method including identifying, by the processor, one or more unused portions of a neurosynaptic network, and for each of the one or more unused portions of the neurosynaptic network, disconnecting, by the processor, the unused portion from the neurosynaptic network.

Described is a system for learning, prediction, and recall of spatiotemporal patterns. An input spatiotemporal sequence is learned using a recurrent spiking neural network by first processing the input spatiotemporal sequence using the recurrent spiking neural network. The recurrent spiking neural network comprises neurons having excitatory synaptic connections and inhibitory synaptic connections. Balanced inhibitory connectivity exists between neurons having excitatory synaptic connections. The recurrent spiking neural network uses distinct forms of synaptic plasticity for excitatory synaptic connections and inhibitory synaptic connections, such that excitatory synaptic connections strengthen and inhibitory synaptic connections weaken. In another aspect, the system is able to recall the learned spatiotemporal sequence and predict a future spatiotemporal sequence through activation of the recurrent spiking 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.

Reduction in the number of neurons and axons in a neurosynaptic network while maintaining its functionality is provided. A neural network description describing a neural network is read. One or more functional unit of the neural network is identified. The one or more functional unit of the neural network is optimized. An optimized neural network description is written based on the optimized functional unit.

Reduction in the number of neurons and axons in a neurosynaptic network while maintaining its functionality is provided. A neural network description describing a neural network is read. One or more functional unit of the neural network is identified. The one or more functional unit of the neural network is optimized. An optimized neural network description is written based on the optimized functional unit.

A neural network is implemented as a memristive neuromorphic circuit that includes a neuron circuit and a memristive device connected to the neuron circuit. An input voltage is sensed at a first terminal of a memristive device during a feedforward operation of the neural network. An error voltage is sensed at a second terminal of the memristive device during an error backpropagation operation of the neural network. In accordance with a training rule, a desired conductance change for the memristive device is computed based on the sensed input voltage and the sensed error voltage. Then a training voltage is applied to the memristive device. Here, the training voltage is proportional to a logarithmic value of the desired conductance change.

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.

A governance apparatus and a communication method for communicating within the governance apparatus. The governance apparatus includes a Government. The Government includes Councils such that a macro grid including an artificial intelligence and the Government is configured to respond to an alert pertaining to an event through use of the artificial intelligence and the Government. The governance apparatus also includes an enhanced Transmission Control Protocol/Internet Protocol (TCP/IP) communication stack of layers including a Governance Layer and an Intelligence Layer. The Intelligence Layer includes intelligence software configured to process data pertaining to the event, data pertaining to the alert, and data pertaining to the Government. The Governance Layer includes governance software configured to filter data in a TCP/IP packet header structure through data security and data integrity algorithms, both to and from the intelligence software in the Intelligence Layer, to protect the artificial intelligence from attack.