D-wave Systems Inc.

Peter Love

Geordie Rose

Andrew Joseph Berkley

Paul I Bunyk

Mohammad H S Amin

Miles F H Steininger

David Grant

Alexander Maassen Van Den Brink

William G Macready

Marshall Drew-Brook

Thomas Mahon

Thomas F W Mahon

Thomas Fw Mahon

Alec Maassen Van Den Brink

Analog processors for solving various computational problems are provided. Such analog processors comprise a plurality of quantum devices, arranged in a lattice, together with a plurality of coupling devices. The analog processors further comprise bias control systems each configured to apply a local effective bias on a corresponding quantum device. A set of coupling devices in the plurality of coupling devices is configured to couple nearest-neighbor quantum devices in the lattice. Another set of coupling devices is configured to couple next-nearest neighbor quantum devices. The analog processors further comprise a plurality of coupling control systems each configured to tune the coupling value of a corresponding coupling device in the plurality of coupling devices to a coupling. Such quantum processors further comprise a set of readout devices each configured to measure the information from a corresponding quantum device in the plurality of quantum devices.

Analog processor comprising quantum devices

Methods and systems for solving various computational problems with quantum processors are provided. Such quantum processors comprise a plurality of quantum devices together with a plurality of coupling devices. The quantum processor is initialized by setting states of the quantum devices and coupling devices and allowed to evolve to a final state which approximates a natural ground state of the computational problem. The final state can include values of nodes arranged in a lattice in the quantum processor and can represent a solution to the computational processor. The computational problem can have complexity P, NP, NP-Hard, or NP-Complete and may be mapped to a quantum processor with nearest-neighbor and next-nearest-neighbor couplings. The solution to the computational problem can be read out from the quantum processor and transmitted as a data signal embodied in a carrier wave.

Analog processors for solving various computational problems are provided. Such analog processors comprise a plurality of quantum devices, arranged in a lattice, together with a plurality of coupling devices. Such quantum processors further comprise a set of readout devices each configured to measure the information from a corresponding quantum device in the plurality of quantum devices. A method of determining a result of a computational problem using an analog processor includes receiving at a first digital computer, including a digital processor, an instance of the computational problem defined over an input graph, wherein the input graph is non-planar; and determining a mapping of the instance of the computational problem onto the analog processor, by the digital processor.

Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a Boolean logic circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A quantum processor may include multiple sets of qubits, each set coupled to respective annealing signal lines such that dynamic evolution of each set of qubits is controlled independently from the dynamic evolutions of the other sets of qubits.

Systems and methods for solving computational problems

Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a multiplication circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A multiplication circuit may employ binary representations of factors, and these binary representations may be decomposed to reduce the total number of variables required to represent the multiplication circuit.

Systems, methods and apparatus for factoring numbers are provided. The factoring may be accomplished by creating a factor graph, mapping the factor graph onto an analog processor, initializing the analog processor to an initial state, evolving the analog processor to a final state, and receiving an output from the analog processor, the output comprising a set of factors of the number. The factoring may be accomplished by generating a logic circuit representation of the factoring problem, such as a multiplication circuit, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s).

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Haverford, PA, United States of America

Peter Love