Abstract: One example method includes defining a quantum circuit, orchestrating the quantum circuit to a computing infrastructure for execution, executing the quantum circuit on the infrastructure and, while the quantum circuit is being executed, checkpointing the quantum circuit. One or more of the defining, executing, and checkpointing, includes using a mechanism that improves and/or enhances performance of the checkpointing. Example mechanisms include including a custom gate in the quantum circuit, using quantization when storing state data concerning the quantum circuit, using persistent memory and orchestration for the checkpointing, dynamically determining when checkpointing will be performed, and performing automated flattening of quantum circuit checkpoint images.

Abstract: One example method includes simulating execution of a quantum circuit on a classical computing infrastructure, after one or more times that a gate of the quantum circuit is executed as part of the simulating, creating, after execution of that gate, a hash of a state vector that captures a state of the execution of the quantum circuit, storing the hash, and respective associated data structure, in storage, then as part of a simulated execution process, calculating a hash of each gate across the new quantum circuit, looking up, in the storage, a hash of a state vector associated with execution of one of the gates of the new quantum circuit, and restoring, from storage, the latest hash of the state vector associated with the one gate of the new quantum circuit.

Abstract: A method includes predicting, using a machine learning model, runtime characteristics concerning a quantum computing function, predicting, using the machine learning model, resources needed to perform the quantum computing function, selecting an execution environment for the quantum computing function, and executing the quantum computing function in the execution environment. The quantum computing function may be a quantum circuit cutting operation, or the quantum computing function may be a quantum circuit execution.

Abstract: One example method includes obtaining error information for physical qubits that are candidates for mapping by respective virtual qubits of a quantum circuit, sampling the physical qubits based on their respective error information, mapping the virtual qubits to the physical qubits obtained by the sampling, and performing a shot of the quantum circuit on the sampled physical qubits. These operations may be performed ‘n’ times until an acceptable result is obtained for execution of the quantum circuit.

Abstract: One example method includes deploying, in a production environment, a machine learning model that was trained using metadata created by an intermediate classical computing layer, and the metadata comprises information about one or more aspects of a quantum circuit, generating, with the machine learning model, a prediction as to how one or more computing infrastructures may be expected to perform when executing the quantum circuit, based on the prediction, making an orchestration decision concerning the quantum circuit, and orchestrating the quantum circuit to one of the computing infrastructures.

Abstract: A method for generating one or more quantum circuits to be evaluated by one or more QPUs is disclosed. The method includes obtaining a problem to be solved using one or more QPUs and analyzing the problem for reductions in complexity to obtain a reduced problem. One or more quantum algorithms are generated to implement a potential solution to the reduced problem, and the quantum algorithms are translated into one or more quantum circuits. The method further includes transmitting the one or more quantum circuits to the one or more QPUs.

Abstract: Distributing quantum jobs are disclosed. When a quantum processing unit is underutilized or when wait times are long, quantum jobs may be distributed from the job queue of one vendor to another vendor. This improves utilization and reduces wait times.

Abstract: One example method includes receiving parameter values relating to execution of a simulation of a quantum algorithm, deriving quantum attributes from the parameter values, generating, based on the quantum attributes, a classical computing resource prediction, and translating the classical computing resource prediction into elements of a classical computing infrastructure. The classical computing infrastructure may be sized and configured to support computationally efficient, and cost efficient, execution of the simulation of the quantum algorithm.

Abstract: One example method includes receiving a hybrid/classical algorithm, determining a runtime characteristic of the hybrid/classical algorithm, based on the runtime characteristic, checking a memory availability for execution of the hybrid/classical algorithm, when adequate memory is not available to support execution of the hybrid/classical algorithm, modifying a classical/quantum memory fabric to provide enough memory to support execution of the hybrid/classical algorithm, and orchestrating the hybrid classical/quantum algorithm to an execution environment that includes the classical/quantum memory fabric.

Abstract: Global optimization of quantum jobs in a multi-cloud or multi-edge environment is disclosed. The quantum jobs of multiple vendors are consolidated in a telemetry plane. The quantum jobs are evaluated based on user intents, quantum job characteristics, and quantum processing unit characteristics. The quantum jobs are then assigned to the quantum systems of the vendors based on the evaluation.

Abstract: One example method includes identifying an accelerator service instance associated with a workload, calling a service broker associated with the accelerator service instance to obtain information needed to use the accelerator service instance, receiving an accelerator call, and accelerator job information concerning an accelerator job, from the workload, in response to the accelerator call, spinning up a new process dedicated to the accelerator job, as part of the new process, running the accelerator job using either the accelerator service instance, or a locally available accelerator, and returning data, generated by running the accelerator job, to the workload.

Abstract: One example method includes receiving job configuration information from a user with a quantum computing job to be performed, receiving quantum computing information from a quantum computing service vendor, generating, based on the quantum computing information, a vendor score for the quantum computing service vendor, and transmitting the vendor score to the user. The quantum computing information received from the quantum computing service vendor may include information about an accuracy of results produced by execution of a quantum circuit or other quantum hardware operated by the quantum computing service vendor.

Abstract: One example method includes evaluating a function invoked by a request that is received at a local classical computing execution environment, and the request also implies performance of a quantum computing function in a quantum computing execution environment, based on an outcome of the evaluating, determining whether or not the function should be run in the local classical computing execution environment, or whether the function should be run in a separate classical computing execution environment, and when the determining indicates that the function should be run in the separate classical computing execution environment, forwarding the request to the separate classical computing environment for execution of the function. The local classical computing execution environment, the separate classical computing execution environment, and the quantum computing execution environment, are respective first, second, and third, tiers of a hybrid computing execution environment.

Abstract: A system for accelerator functions as a service is disclosed. The system may receive a job that includes a computer (CPU) portion and an accelerator portion. When the accelerator portion is performed, an execution time associated with a time for the accelerator to return results is determined. Resources or a portion thereof allocated to the job, or the CPU portion are freed or reallocated to another job at least during the execution time. The job is queued to receive resources when the results are received from the accelerator.

Abstract: Quantum processing unit slicing is disclosed. The qubits of a quantum processing unit are sliced or grouped to accommodate multiple independent and separate quantum jobs. The quantum jobs are matched, based on user tolerances related to at least number of shots, and the quantum jobs are then merged and performed. The results for each of the specific quantum jobs concurrently performed are extracted from the overall results of the quantum processing unit.

Abstract: One example method includes identifying a column of a table, and the column includes multiple entries, setting a proposed uniqueness for the column, setting a confidence tolerance for the proposed uniqueness, estimating a sub-sample size for the column based on the proposed uniqueness and the confidence tolerance, based on the sub-sample size, sampling a subset of the entries in the column and, based on the sampling, determining whether or not the column is a primary key for the table.

Abstract: Quality control operations for quantum processing units are disclosed. A broker application may perform quality control jobs to determine baseline characteristics about quantum processing units. The characteristics of user-submitted jobs can be compared to the baseline characteristics associated with the quality control jobs. This allows the broker to generate a confidence score that reflects at least whether the user-submitted job was performed on the quantum processing unit expected by the user.