D-WAVE SYSTEMS INC.

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.

Machine learning classification models which are robust against label noise are provided. Noise may be modelled explicitly by modelling “label flips”, where incorrect binary labels are “flipped” relative to their ground truth value. Distributions of label flips may be modelled as prior and posterior distributions in a flexible architecture for machine learning systems. An arbitrary classification model may be provided within the system. The classification model is made more robust to label noise by operation of the prior and posterior distributions. Particular prior and approximating posterior distributions are disclosed.

A computational system can include digital circuitry and analog circuitry, for instance a digital processor and a quantum processor. The quantum processor can operate as a sample generator providing samples. Samples can be employed by the digital processing in implementing various machine learning techniques. For example, the computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior.

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.

A system and method of implementing finite element modeling on a quantum processor is discussed. A representation of a computational problem including a boundary value problem and problem grid points is received by one or more processors. The problem grid points are mapped to a Hilbert space of the qubits of the quantum processor. The boundary value problem is transformed into a problem Hamiltonian. Instructions are transmitted to the quantum processor to cause the quantum processor to evolve from an initial state to a final state based on the problem Hamiltonian. The wavefunction amplitudes of the final state are measured, and the wavefunction amplitudes of the final state are mapped onto the problem grid points based on the Hilbert space of the qubits.

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. Galvanic connections between DACs and/or with target devices are disclosed, as well as inductive connections.

A digital processor runs a machine learning algorithm in parallel with a sampling server. The sampling sever may continuously or intermittently draw samples for the machine learning algorithm during execution of the machine learning algorithm, for example on a given problem. The sampling server may run in parallel (e.g., concurrently, overlapping, simultaneously) with a quantum processor to draw samples from the quantum processor.

Systems and methods that employ interactions between quantum computing systems and digital computing systems are described. For an iterative method, a quantum computing system may be designed, operated, and/or adapted to provide a rate of convergence that is greater than the rate of convergence of a digital supercomputer. When the digital supercomputer is iteratively used to evaluate an objective function at a cost incurred of C per iteration, the quantum computing system may be used to provide the input parameter(s) to the objective function and quickly converge on the input parameter(s) that optimize the objective function. Thus, a quantum computing system may be used to minimize the total cost incurred C T for consumption of digital supercomputer resources when a digital supercomputer is iteratively employed to evaluate an objective function.

A device is dynamically isolated via a broadband switch that includes a plurality of cascade elements in series, wherein each cascade element comprises a first set of SQUIDs in series, a matching capacitor, and a second set of SQUIDs in series. The broadband switch is set to a passing state via flux bias lines during programming and readout of the device and set to a suppression state during device's calculation to reduce operation errors at the device. A device is electrically isolated from high-frequencies via an unbiased broadband switch. A device is coupled to a tunable thermal bath that includes a broadband switch.

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. Problem representations may be modified via the UI, and a computer system may autonomously generate new instances of the problem representation, update data structures, embed the new instance and cause the new instance to be executed by the analog processor.