Abstract: A user interface (UI), data structures and algorithms facilitate programming, analyzing, debugging, embedding, and/or modifying problems that are embedded or to be embedded on an analog processor (e.g., quantum processor), increasing computational efficiency and/or accuracy of problem solutions. The UI provides graph representations (e.g., source graph, target graph and correspondence therebetween) with nodes and edges which may map to hardware components (e.g., qubits, couplers) of the analog processor. Characteristics of solutions are advantageously represented spatially associated (e.g., overlaid or nested) with characteristics of a problem. Characteristics (e.g., bias state) may be represented by color, pattern, values, icons. Issues (e.g., broken chains) may be detected and alerts provided.

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: Systems and methods are described for operating a hybrid computing system using cluster contraction for converting large, dense input to reduced input that can be easily mapped into a quantum processor. The reduced input represents the global structure of the problem. Techniques involve partitioning the input variables into clusters and contracting each cluster. The input variables can be partitioned using an Unweighted Pair Group Method with Arithmetic Mean algorithm. The quantum processor returns samples based on the reduced input and the samples are expanded to correspond to the original input.

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 superconducting integrated circuit has a first superconducting device with a first superconducting loop, where the first superconducting loop has a first superconducting trace in a first layer of the superconducting integrated circuit, and a second superconducting device with a second superconducting loop, where the second superconducting loop has a second superconducting trace in a second layer. The first superconducting loop crosses the second superconducting loop in a crossing region. At least a portion of each of the first and the second superconducting trace inside the crossing region is narrower than at least a portion of each of the traces outside the crossing region, and follows a respective circuitous path which is inductively proximate to at least a portion of the other path.

Abstract: Approaches useful to operation of scalable processors with ever larger numbers of logic devices (e.g., qubits) advantageously take advantage of QFPs, for example to implement shift registers, multiplexers (i.e., MUXs), de-multiplexers (i.e., DEMUXs), and permanent magnetic memories (i.e., PMMs), and the like, and/or employ XY or XYZ addressing schemes, and/or employ control lines that extend in a “braided” pattern across an array of devices. Many of these described approaches are particularly suited for implementing input to and/or output from such processors. Superconducting quantum processors comprising superconducting digital-analog converters (DACs) are provided. The DACs may use kinetic inductance to store energy via thin-film superconducting materials and/or series of Josephson junctions, and may use single-loop or multi-loop designs. Particular constructions of energy storage elements are disclosed, including meandering structures.

Abstract: A user interface (UI), data structures and algorithms facilitate programming, analyzing, debugging, embedding, and/or modifying problems that are embedded or to be embedded on an analog processor (e.g., quantum processor), increasing computational efficiency and/or accuracy of problem solutions. The UI provides graph representations (e.g., source graph, target graph and correspondence therebetween) with nodes and edges which may map to hardware components (e.g., qubits, couplers) of the analog processor. Characteristics of solutions are advantageously represented spatially associated (e.g., overlaid or nested) with characteristics of a problem. Characteristics (e.g., bias state) may be represented by color, pattern, values, icons. Issues (e.g., broken chains) may be detected and alerts provided.

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: Systems and methods are described for operating a hybrid computing system using cluster contraction for converting large, dense input to reduced input that can be easily mapped into a quantum processor. The reduced input represents the global structure of the problem. Techniques involve partitioning the input variables into clusters and contracting each cluster. The input variables can be partitioned using an Unweighted Pair Group Method with Arithmetic Mean algorithm. The quantum processor returns samples based on the reduced input and the samples are expanded to correspond to the original input.

Abstract: Approaches useful to operation of scalable processors with ever larger numbers of logic devices (e.g., qubits) advantageously take advantage of QFPs, for example to implement shift registers, multiplexers (i.e., MUXs), de-multiplexers (i.e., DEMUXs), and permanent magnetic memories (i.e., PMMs), and the like, and/or employ XY or XYZ addressing schemes, and/or employ control lines that extend in a “braided” pattern across an array of devices. Many of these described approaches are particularly suited for implementing input to and/or output from such processors. Superconducting quantum processors comprising superconducting digital-analog converters (DACs) are provided. The DACs may use kinetic inductance to store energy via thin-film superconducting materials and/or series of Josephson junctions, and may use single-loop or multi-loop designs. Particular constructions of energy storage elements are disclosed, including meandering structures.

Abstract: A hybrid computing system for solving a computational problem includes a digital processor, a quantum processor having qubits and coupling devices that together define a working graph of the quantum processor, and at least one nontransitory processor-readable medium communicatively coupleable to the digital processor which stores at least one of processor-executable instructions or data. The digital processor receives a computational problem, and programs the quantum processor with a first set of bias fields and a first set of coupling strengths. The quantum processor generates samples as potential solutions to an approximation of the problem. The digital processor updates the approximation by determining a second set of bias fields based at least in part on the first set of bias fields and a first set of mean fields that are based at least in part on the first set of samples and coupling strengths of one or more virtual coupling devices.

Abstract: Systems and methods are described for operating a hybrid computing system using cluster contraction for converting large, dense input to reduced input that can be easily mapped into a quantum processor. The reduced input represents the global structure of the problem. Techniques involve partitioning the input variables into clusters and contracting each cluster. The input variables can be partitioned using an Unweighted Pair Group Method with Arithmetic Mean algorithm. The quantum processor returns samples based on the reduced input and the samples are expanded to correspond to the original input.

Abstract: An accelerated version of a node-weighted path distance algorithm is implemented on a microprocessor coupled to a digital processor. The algorithm calculates an embedding of a source graph into a target graph (e.g., hardware graph of a quantum processor). The digital processor causes the microprocessor to send seeds to logic blocks with a corresponding node in the target graph contained in a working embedding of a node, compute a minimum distance to neighboring logic blocks from each seeded logic block, set the distance to neighboring logic blocks as the minimum distance plus the weight of the seeded logic block, increment the accumulator value by the weight of the seeded logic block, increment the accumulator value by the distance, determine the minimum distance logic block by computing the minimum accumulated value, compute distances to the minimum distance logic block; and read distances from all logic blocks into local memory.

Abstract: A user interface (UI), data structures and algorithms facilitate programming, analyzing, debugging, embedding, and/or modifying problems that are embedded or to be embedded on an analog processor (e.g., quantum processor), increasing computational efficiency and/or accuracy of problem solutions. The UI provides graph representations (e.g., source graph, target graph and correspondence therebetween) with nodes and edges which may map to hardware components (e.g., qubits, couplers) of the analog processor. Characteristics of solutions are advantageously represented spatially associated (e.g., overlaid or nested) with characteristics of a problem. Characteristics (e.g., bias state) may be represented by color, pattern, values, icons. Issues (e.g., broken chains) may be detected and alerts provided.

Abstract: Topologies for analog computing systems may include cells of qubits which may implement a tripartite graph and cross substantially orthogonally. Qubits may have an H-shape or an l-shape, qubits may change direction within a cell. Topologies may be comprised of two or more different sub-topologies. Qubits may be communicatively coupled to non-adjacent cells by long-range couplers. Long-range couplers may change direction within a cell. A cell may have two or more different type of long-range couplers. A cell may have shifted qubits, more than one type of inter-cell couplers, more than one type of intra-cell couplers and long-range couplers.

Abstract: Josephson junctions (JJ) may replace primary inductance of transformers to realize galvanic coupling between qubits, advantageously reducing size. A long-range symmetric coupler may include a compound JJ (CJJ) positioned at least approximately at a half-way point along the coupler to advantageously provide a higher energy of a first excited state than that of an asymmetric long-range coupler. Quantum processors may include qubits and couplers with a non-stoquastic Hamiltonian to enhance multi-qubit tunneling during annealing. Qubits may include additional shunt capacitances, e.g., to increase overall quality of a total capacitance and improve quantum coherence. A sign and/or magnitude of an effective tunneling amplitude ?eff of a qubit characterized by a double-well potential energy may advantageously be tuned. Sign-tunable electrostatic coupling of qubits may be implemented, e.g., via resonators, and LC-circuits. YY couplings may be incorporated into a quantum anneaier (e.g., quantum processor).

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: A quantum processor performs input and output which may be performed synchronously. The quantum processor executes a problem to generate a classical output state, which is read out at least partially by an I/O system. The I/O system also transmits a classical input state to by the I/O system, which may include the same qubit-proximate devices used for read-out. The classical input state is written to the qubits, and the quantum processor executes based on the classical input state (e.g., by performing reverse annealing to transform the classical input state to quantum state).