Abstract: This disclosure relates to methods of constructing efficient quantum circuits for Clifford loaders and variations of these methods following a similar scheme.

Abstract: Orthogonal neural networks impose orthogonality on the weight matrices. They may achieve higher accuracy and avoid evanescent or explosive gradients for deep architectures. Several classical gradient descent methods have been proposed to preserve orthogonality while updating the weight matrices, but these techniques suffer from long running times and provide only approximate orthogonality. In this disclosure, we introduce a new type of neural network layer. The layer allows for gradient descent with perfect orthogonality with the same asymptotic running time as a standard layer. The layer is inspired by quantum computing and can therefore be applied on a classical computing system as well as on a quantum computing system. It may be used as a building block for quantum neural networks and fast orthogonal neural networks.

Abstract: This disclosure relates to methods of constructing efficient quantum circuits for Clifford loaders and variations of these methods following a similar scheme.

Abstract: This disclosure relates generally to the field of quantum algorithms and quantum data loading, and more particularly to constructing quantum circuits for loading classical data into quantum states which reduces the computational resources of the circuit, e.g., number of qubits, depth of quantum circuit, and type of gates in the circuit.

Abstract: This disclosure relates generally to the field of quantum algorithms and quantum data loading, and more particularly to constructing quantum circuits for loading classical data into quantum states which reduces the computational resources of the circuit, e.g., number of qubits, depth of quantum circuit, and type of gates in the circuit.

Abstract: This disclosure relates generally to the field of quantum algorithms and quantum data loading, and more particularly to constructing quantum circuits for loading classical data into quantum states which reduces the computational resources of the circuit, e.g., number of qubits, depth of quantum circuit, and type of gates in the circuit.

Abstract: This disclosure relates to methods of constructing efficient quantum circuits for Clifford loaders and variations of these methods following a similar scheme.

Abstract: Orthogonal neural networks impose orthogonality on the weight matrices. They may achieve higher accuracy and avoid evanescent or explosive gradients for deep architectures. Several classical gradient descent methods have been proposed to preserve orthogonality while updating the weight matrices, but these techniques suffer from long running times and provide only approximate orthogonality. In this disclosure, we introduce a new type of neural network layer. The layer allows for gradient descent with perfect orthogonality with the same asymptotic running time as a standard layer. The layer is inspired by quantum computing and can therefore be applied on a classical computing system as well as on a quantum computing system. It may be used as a building block for quantum neural networks and fast orthogonal neural networks.

Abstract: Embodiments relate to a method for estimating an amplitude of a unitary operator U to within an error ? by using a quantum processor configurable to implement the unitary operator U on a quantum circuit. The quantum circuit has a maximum depth S can implement the unitary operator no more than D times in a single run. A schedule of iterations n=1 to N based on the error ? and number D is determined. Each iteration n characterized by a schedule parameter kn. kn?D for all n and kn increases at a rate that is less than exponential. The iterations n may be sequentially executed. In each iteration, the quantum processor is configured to sequentially apply and execute the unitary operator U kn times on the quantum circuit. A non-quantum processor then estimates the amplitude of the unitary operator U based on the measured resulting states.

Abstract: Orthogonal neural networks impose orthogonality on the weight matrices. They may achieve higher accuracy and avoid evanescent or explosive gradients for deep architectures. Several classical gradient descent methods have been proposed to preserve orthogonality while updating the weight matrices, but these techniques suffer from long running times and provide only approximate orthogonality. In this disclosure, we introduce a new type of neural network layer. The layer allows for gradient descent with perfect orthogonality with the same asymptotic running time as a standard layer. The layer is inspired by quantum computing and can therefore be applied on a classical computing system as well as on a quantum computing system. It may be used as a building block for quantum neural networks and fast orthogonal neural networks.

Abstract: This disclosure relates to methods of constructing efficient quantum circuits for Clifford loaders and variations of these methods following a similar scheme.

Abstract: This disclosure relates generally to the field of quantum algorithms and quantum data loading, and more particularly to constructing quantum circuits for loading classical data into quantum states which reduces the computational resources of the circuit, e.g., number of qubits, depth of quantum circuit, and type of gates in the circuit.

Abstract: This disclosure relates to enhanced methods of operating quantum computing systems to perform amplitude estimation. More than that, the methods may be tuned to accommodate for specific noise levels (e.g., in given a quantum device). Embodiments also enable quantum computing systems to perform amplitude estimation faster than amplitude estimation algorithms performed using a classical (non-quantum) computer.

Abstract: This disclosure relates generally to circuit-model quantum computation, and more particularly, to quantum processing devices that are specialized for efficient loading of classical data into a quantum computer.

Abstract: This disclosure relates generally to circuit-model quantum computation, and more particularly, to quantum processing devices that are specialized for efficient loading of classical data into a quantum computer.

Abstract: This disclosure relates generally to the field of quantum algorithms and quantum data loading, and more particularly to constructing quantum circuits for loading classical data into quantum states which reduces the computational resources of the circuit, e.g., number of qubits, depth of quantum circuit, and type of gates in the circuit.

Abstract: This disclosure relates generally to the field of quantum algorithms and quantum data loading, and more particularly to constructing quantum circuits for loading classical data into quantum states which reduces the computational resources of the circuit, e.g., number of qubits, depth of quantum circuit, and type of gates in the circuit.

Abstract: Embodiments relate to a method for estimating an amplitude of a unitary operator U to within an error ? by using a quantum processor configurable to implement the unitary operator U on a quantum circuit. The quantum circuit has a maximum depth S can implement the unitary operator no more than D times in a single run. A schedule of iterations n=1 to N based on the error ? and number D is determined. Each iteration n characterized by a schedule parameter kn. kn?D for all n and kn increases at a rate that is less than exponential. The iterations n may be sequentially executed. In each iteration, the quantum processor is configured to sequentially apply and execute the unitary operator U kn times on the quantum circuit. A non-quantum processor then estimates the amplitude of the unitary operator U based on the measured resulting states.