Abstract: The systems, devices, articles, and methods described herein generally relate to analog computers, for example quantum processors comprising qubits, couplers, and, or cavities. Analog computers, for example quantum processor based computers, are the subject of various sources of error which can hinder operation, potentially reducing computational accuracy and speed. Sources of error can be broadly characterized, for example as i) a background susceptibility do to inherently characteristics of the circuitry design, ii) as an h/J ratio imbalance, iii) bit flip errors, iv) fidelity, and v) Anderson localization, and various combinations of the aforesaid.

Abstract: A computational method via a hybrid processor comprising an analog processor and a digital processor includes determining a first classical spin configuration via the digital processor, determining preparatory biases toward the first classical spin configuration, programming an Ising problem and the preparatory biases in the analog processor via the digital processor, evolving the analog processor in a first direction, latching the state of the analog processor for a first dwell time, programming the analog processor to remove the preparatory biases via the digital processor, determining a tunneling energy via the digital processor, determining a second dwell time via the digital processor, evolving the analog processor in a second direction until the analog processor reaches the tunneling energy, and evolving the analog processor in the first direction until the analog processor reaches a second classical spin configuration.

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples.

Abstract: The systems, devices, articles, and methods described herein generally relate to analog computers, for example quantum processors comprising qubits, couplers, and, or cavities. Analog computers, for example quantum processor based computers, are the subject of various sources of error which can hinder operation, potentially reducing computational accuracy and speed. Sources of error can be broadly characterized, for example as i) a background susceptibility do to inherently characteristics of the circuitry design, ii) as an h/J ratio imbalance, iii) bit flip errors, iv) fidelity, and v) Anderson localization, and various combinations of the aforesaid.

Abstract: A computational method via a hybrid processor comprising an analog processor and a digital processor includes determining a first classical spin configuration via the digital processor, determining preparatory biases toward the first classical spin configuration, programming an Ising problem and the preparatory biases in the analog processor via the digital processor, evolving the analog processor in a first direction, latching the state of the analog processor for a first dwell time, programming the analog processor to remove the preparatory biases via the digital processor, determining a tunneling energy via the digital processor, determining a second dwell time via the digital processor, evolving the analog processor in a second direction until the analog processor reaches the tunneling energy, and evolving the analog processor in the first direction until the analog processor reaches a second classical spin configuration.

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples.

Abstract: The systems, devices, articles, and methods described herein generally relate to analog computers, for example quantum processors comprising qubits, couplers, and, or cavities. Analog computers, for example quantum processor based computers, are the subject of various sources of error which can hinder operation, potentially reducing computational accuracy and speed. Sources of error can be broadly characterized, for example as i) a background susceptibility do to inherently characteristics of the circuitry design, ii) as an h/J ratio imbalance, iii) bit flip errors, iv) fidelity, and v) Anderson localization, and various combinations of the aforesaid.

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples.

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples.

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples.

Abstract: The systems, devices, articles, and methods described herein generally relate to analog computers, for example quantum processors comprising qubits, couplers, and, or cavities. Analog computers, for example quantum processor based computers, are the subject of various sources of error which can hinder operation, potentially reducing computational accuracy and speed. Sources of error can be broadly characterized, for example as i) a background susceptibility do to inherently characteristics of the circuitry design, ii) as an h/J ratio imbalance, iii) bit flip errors, iv) fidelity, and v) Anderson localization, and various combinations of the aforesaid.

Abstract: Quantum annealers as analog or quantum processors can find paths in problem graphs embedded in a hardware graph of the processor, for example finding valid paths, shortest paths or longest paths. A set of input, for example nucleic acid reads, can be used to set up a graph with edges between nodes denoting overlap (i.e., common base pairs) between the reads with constraints applied to perform sequence alignment or sequencing of a nucleic acid (e.g., DNA) strand or sequence, finding a solution that has a ground state energy. At least a portion of the described approaches can be applied to other problems, for instance resource allocations problems, e.g., job scheduling problems, traveling salesperson problems, and other NP-complete problems.

Abstract: The systems, devices, articles, and methods described herein generally relate to analog computers, for example quantum processors comprising qubits, couplers, and, or cavities. Analog computers, for example quantum processor based computers, are the subject of various sources of error which can hinder operation, potentially reducing computational accuracy and speed. Sources of error can be broadly characterized, for example as i) a background susceptibility do to inherently characteristics of the circuitry design, ii) as an h/J ratio imbalance, iii) bit flip errors, iv) fidelity, and v) Anderson localization, and various combinations of the aforesaid.

Abstract: The invention relates to a data processing system for optimizing a live data-producing process, wherein the data processing system comprises: a process data component for providing the process data representing the live data-producing process, a quantum web service component for generating an optimization problem to be solved by a quantum processing unit, wherein the quantum web service component is linked to the process data component for receiving the process data therefrom, wherein the optimization problem is configured for optimizing the live data-producing process based on the process data received from the process data component, and a feedback component for feeding the solution of the optimization problem solved by the quantum processing unit back to the live data-producing process, wherein the feedback component is linked to the quantum web service component for receiving the solution of the optimization problem solved by the quantum processing unit.

Abstract: A computational method via a hybrid processor comprising an analog processor and a digital processor includes determining a first classical spin configuration via the digital processor, determining preparatory biases toward the first classical spin configuration, programming an Ising problem and the preparatory biases in the analog processor via the digital processor, evolving the analog processor in a first direction, latching the state of the analog processor for a first dwell time, programming the analog processor to remove the preparatory biases via the digital processor, determining a tunneling energy via the digital processor, determining a second dwell time via the digital processor, evolving the analog processor in a second direction until the analog processor reaches the tunneling energy, and evolving the analog processor in the first direction until the analog processor reaches a second classical spin configuration.

Abstract: A computational method via a hybrid processor comprising an analog processor and a digital processor includes determining a first classical spin configuration via the digital processor, determining preparatory biases toward the first classical spin configuration, programming an Ising problem and the preparatory biases in the analog processor via the digital processor, evolving the analog processor in a first direction, latching the state of the analog processor for a first dwell time, programming the analog processor to remove the preparatory biases via the digital processor, determining a tunneling energy via the digital processor, determining a second dwell time via the digital processor, evolving the analog processor in a second direction until the analog processor reaches the tunneling energy, and evolving the analog processor in the first direction until the analog processor reaches a second classical spin configuration.

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples.

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples.

Abstract: The systems, devices, articles, and methods described herein generally relate to analog computers, for example quantum processors comprising qubits, couplers, and, or cavities. Analog computers, for example quantum processor based computers, are the subject of various sources of error which can hinder operation, potentially reducing computational accuracy and speed. Sources of error can be broadly characterized, for example as i) a background susceptibility do to inherently characteristics of the circuitry design, ii) as an h/J ratio imbalance, iii) bit flip errors, iv) fidelity, and v) Anderson localization, and various combinations of the aforesaid.

Abstract: A hybrid computer for generating samples employs a digital computer operable to perform post-processing. An analog computer may be communicatively coupled to the digital computer. The analog computer may be operable to return one or more samples corresponding to low-energy configurations of a Hamiltonian. Methods of generating samples from a quantum Boltzmann distribution to train a Quantum Boltzmann Machine, and from a classical Boltzmann distribution to train a Restricted Boltzmann Machine, are also taught. Computational systems and methods permit processing problems having size and/or connectivity greater than, and/or at least not fully provided by, a working graph of an analog processor.