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 to methods of constructing efficient quantum circuits for Clifford loaders and variations of these methods following a similar scheme.

Abstract: A computing system receives a stochastic process with a plurality of trajectories over time t. The computing system determines a first quantum circuit that, when executed by a quantum computing system, prepares a mixed quantum state ?? in the quantum computing system, where ? approximates a mixed quantum state of the stochastic process and is defined by: ? = ? traj P ? r [ traj ] ? ? "\[LeftBracketingBar]" ? traj ? ? ? traj ? ? "\[LeftBracketingBar]" , where Pr[traj] is a probability of a trajectory of the stochastic process, |?traj is a quantum state representing a trajectory of the stochastic process and is defined by ? "\[LeftBracketingBar]" ? traj ? = ? t f ? ( t ) ? ? "\[LeftBracketingBar]" t ? , where ƒ(t) is based on a value of the stochastic process at time t.

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: The optimization of circuit parameters of variational quantum algorithms is a challenge for the practical deployment of near-term quantum computing algorithms. Embodiments relate to a hybrid quantum-classical optimization methods. In a first stage, analytical tomography fittings are performed for a local cluster of circuit parameters via sampling of the observable objective function at quadrature points in the circuit parameters. Optimization may be used to determine the optimal circuit parameters within the cluster, with the other circuit parameters frozen. In a second stage, different clusters of circuit parameters are then optimized in “Jacobi sweeps,” leading to a monotonically convergent fixed-point procedure. In a third stage, the iterative history of the fixed-point Jacobi procedure may be used to accelerate the convergence by applying Anderson acceleration/Pulay's direct inversion of the iterative subspace (DIIS).

Abstract: The disclosure is in the technical field of circuit-model quantum computation. Generally, it concerns methods to use quantum computers to perform computations on classical spin models, where the classical spin models involve a number of spins that is exponential in the number of qubits that comprise the quantum computer. Examples of such computations include optimization and calculation of thermal properties, but extend to a wide variety of calculations that can be performed using the configuration of a spin model with an exponential number of spins. Spin models encompass optimization problems, physics simulations, and neural networks (there is a correspondence between a single spin and a single neuron). This disclosure has applications in these three areas as well as any other area in which a spin model can be used.

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: The driver Hamiltonian is modified in such a way that the quantum approximate optimization algorithm (QAOA) running on a circuit-model quantum computing facility (e.g., actual quantum computing device or simulator), may better solve combinatorial optimization problems than with the baseline/default choice of driver Hamiltonian. For example, the driver Hamiltonian may be chosen so that the overall Hamiltonian is non-stoquastic.

Abstract: A variational quantum algorithm is solved using two types of quantum processing units (QPU) with different performance metrics (e.g., speed, size and fidelity). One type of quantum processing unit (QPU) is used to optimize some or all of the circuit parameters in a first stage, and these are then used with a different type QPU in a second stage to solve the target problem. The different performance metrics permit tradeoffs between the two stages.

Abstract: The optimization of circuit parameters of variational quantum algorithms is a challenge for the practical deployment of near-term quantum computing algorithms. Embodiments relate to a hybrid quantum-classical optimization methods. In a first stage, analytical tomography fittings are performed for a local cluster of circuit parameters via sampling of the observable objective function at quadrature points in the circuit parameters. Optimization may be used to determine the optimal circuit parameters within the cluster, with the other circuit parameters frozen. In a second stage, different clusters of circuit parameters are then optimized in “Jacobi sweeps,” leading to a monotonically convergent fixed-point procedure. In a third stage, the iterative history of the fixed-point Jacobi procedure may be used to accelerate the convergence by applying Anderson acceleration/Pulay's direct inversion of the iterative subspace (DIIS).

Abstract: In an embedding caching system, embeddings generated from previous problems are re-used to improve performance on future problems. A data structure stores problems and their corresponding embeddings. When computing future embeddings, this data structure can be queried to determine whether an embedding has already been computed for a problem with the same structure. If it has, the embedding can be retrieved from the data structure, saving the time and computational expense of generating a new embedding. In one variation, the query is not based on exact matches. If a new problem is similar in structure to previous problems, those embeddings may be used to accelerate the generating of an embedding for the new problem, even if they cannot be used directly to embed the new problem.

Abstract: Fault tree analysis is performed using a combination of digital computer systems and quantum processing devices. For example, quantum annealers may be configured to analyze a fault tree for minimal cut sets. The quantum annealer may be particular good at identifying smaller minimal cut sets. Digital computer systems may be used to identify the remaining minimal cut sets. If the quantum annealer identifies one of the minimal cut sets of smallest size (i.e., lowest cardinality), this can be used as a constraint for the digital computer system, thus speeding up its search for other minimal cut sets.

Abstract: Quantum computing is a computational paradigm for solving (exactly or approximately) difficult combinatorial optimization problems. One degree of freedom that is available is the so-called annealing schedule, which defines how the quantum computation evolves from the start of the computation to the end of the computation. This schedule is defined by anneal offsets, which can be different for each quantum bit (qubit) in the quantum computer. The choice of annealing schedule can have a dramatic impact on the performance of the computer. In this disclosure we provide a method for selecting and/or modifying the annealing schedule based on the problem to be solved.

Abstract: A cloud computing architecture and system for interaction with and use of quantum processing devices is presented. In one aspect, the invention comprises a unified platform as a service for interacting with various quantum processing devices. In another aspect, the invention provides an architecture and methodology for accessing and using a variety of quantum processing devices. Other aspects of the invention incorporate various software modules that provide additional functionality for users of quantum processing devices.

Abstract: Quantum processing devices are integrated with conventional distributed computing paradigms. In one aspect, ideas from classical distributed and high-performance computing are brought into the quantum processing domain. Various architectures and methodologies enable the bilateral integration of quantum processing devices and distributed computers. In one aspect, a system is composed of a high-level API and library, a quantum data model, and a set of software processes to prepare this data model for computation on a quantum processing device and to retrieve results from the quantum processing device. This provides a way for distributed computing software frameworks to integrate one or more quantum processing devices into their workflow.