Abstract: A method, system, and computer program product for qubit sharing across simultaneous quantum job and/or model execution. Qubit groups within quantum jobs and/or trained models that match with respect to a starting state and a gate structure are identified. Furthermore, qubit groups that are considered for dynamic quantum job and/or model reset and reuse for another computation during a simultaneous quantum job and/or model execution are identified. Based on such identified qubit groups, a record of potential quantum job and/or model minimizations is created. A potential quantum job and/or model minimization is removed one at a time from the record until the quantum jobs and/or models can be positioned on the coupling map. Once that occurs, single compressed quantum jobs and/or models are generated that each use two or more quantum jobs and/or models that can share qubits based on the current record of potential quantum job and/or model minimizations.

Abstract: A method, system, and computer program product for handling black swan events on a quantum computing device. Sensor data from an environment of the quantum computing device is captured and compared to historical sensor data of the environment of the quantum computing device. A black swan event is detected if the difference between the captured sensor data and the historical sensor data exceeds a threshold value. Upon detecting a black swan event, such as during the time that the quantum processor is being utilized, a machine learning model is executed to identify the action to be performed to handle the black swan event. The machine learning model identifies such an action based on identifying a neuron of a self-organizing map that most closely matches the captured sensor data, and then identifying which of the clusters of data within the identified neuron is closest to the captured sensor data.

Abstract: Provided are a computer-implemented method, a system, and a computer program product for futureproofing a machine learning model, in which historical data for updates and changes to a baseline machine learning model are received. A futureproofing metric is generated. An enhanced machine learning model comprising a futureproofed version of the baseline machine learning model is generated with the historical data and the baseline machine learning model as inputs.

Abstract: Provided are a computer-implemented method, a system, and a computer program product for futureproofing a machine learning model, in which historical data for updates and changes to a baseline machine learning model are received. A futureproofing metric is generated. An enhanced machine learning model comprising a futureproofed version of the baseline machine learning model is generated with the historical data and the baseline machine learning model as inputs.

Abstract: A processor can control quantum hardware to transform qubit states associated with a plurality of pairs of data points in a training dataset using a circuit parameter representing a rotation angle. Inner products of transformed qubit states associated with the plurality of pairs of data points can be computed. The processor can minimize an objective function based on the inner products, where the minimizing finds a target circuit parameter representing a target rotation angle that minimizes the objective function. A processor can build a kernel matrix based on the inner products computed for a sample dataset and the target circuit parameter passed to the quantum hardware. A classification algorithm can use the kernel matrix to classify the sample dataset.

Abstract: Systems/techniques that facilitate quantum system selection via coupling map comparison are provided. In various embodiments, a system can access a quantum machine learning (QML) model. In various aspects, the system can identify, from a set of quantum computing systems, a quantum computing system for the QML model, based on a comparison between a first coupling map of the quantum computing system and a second coupling map on which the QML model was trained. If the second coupling map topologically matches the first coupling map or topologically matches a subgraph of the first coupling map, the system can execute the QML model on the quantum computing system. Otherwise, the system can adjust the QML model and can accordingly execute the adjusted QML model on the quantum computing system.

Abstract: A method of generating a classical model to simulate a quantum computational model includes 1) inputting into a quantum computational model a dataset, the quantum computational model being implemented on a quantum computer, 2) computing output results with the quantum computational model using the quantum computer, 3) introducing a variation to at least a portion of the dataset into the quantum computer, 4) computing updated output results of the quantum computational model based on the variation of the at least the portion of the dataset using the quantum computer, and 5) generating a classical twin model of the quantum computational model based on a relationship of the output results and updated output results to the dataset from the quantum computational model.

Abstract: A computer-implemented method, system and computer program product for watermarking quantum models. A precision for a parameter of a quantum model on a quantum computer is determined. The parameter is then truncated based on the determined precision. Furthermore, the watermark is appended in one or more positions of truncated portion of the truncated parameter. In this manner, watermarking quantum models, such as quantum machine learning models, is achieved.

Abstract: Techniques regarding generating an ensemble of quantum kernel-based learners for one or more quantum machine learning models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an ensemble component that can generate an ensemble of quantum kernel-based learners by selecting a quantum kernel at multiple iterations of a boosting procedure that analyzes a range of feature maps employable by a quantum machine learning model.