Abstract: Systems and techniques that facilitate Gibbs state-based quantum optimization for combinatorial optimization problems 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 that can execute at least one of the computer executable components that can prepare a Gibbs state of a quantum system that represents a combinatorial optimization problem, wherein the Gibbs state is a quantum state that minimizes free energy of the quantum system. The at least one of the computer executable components can further initialize a quantum optimization algorithm using a set of parameters that define the Gibbs state to solve the combinatorial optimization problem, wherein solving the combinatorial optimization problem comprises determining a ground state of a Hamiltonian of the quantum system.

Abstract: A method, system, and computer program product for enabling quantum machine learning to be used effectively with classical data. Classical data, which may consist of a large sample size and a large number of features, is mapped into quantum state space forming quantum data using a classical machine learning model. Classical data refers to data subject to the laws of classical physics. Quantum state space refers to an abstract space in which different “positions” represent, not literal locations, but rather quantum states of a physical system. The dimensionality of the quantum state space corresponds to 2 raised to the power of the number of qubits. Quantum machine learning may then be performed on a quantum computer using the formed quantum data. As a result, quantum machine learning is enabled to be used effectively with classical data while utilizing a small number of qubits.

Abstract: To obtain meaningful computational results despite limits on the amount of data that can be input to a quantum computer, a data selection system uses an iterative approach to select a suitable subset of data to be input to a quantum device for processing by a quantum algorithm. The system compresses and clusters a data set according to a task-specific distribution criteria and selects a subset of this clustered data corresponding to representative cases of the data. The selected subset is processed by the quantum device and the system generates a metric score based on the degree to which the results satisfy a performance criterion. The selected subset is refined over multiple iterations based on successive metric scores until a termination criterion is reached, and the final selected subset of data is used as input to the quantum computer for execution of the processing task.

Abstract: Systems and methods for quantum machine learning are described. A plurality of qubits can be entangled to create a cluster state. The plurality of qubits can include at least an input qubit, an output qubit, and at least one ancilla qubit. The input qubit can represent data among a training data set of a machine learning model represented by a unitary operation. Sequential local measurements of the cluster state can be performed to generate a plurality of measurement outcomes. At least one of the plurality of qubits can be rotated according to the plurality of measurement outcomes and rotation parameters of the unitary operation. The sequential local measurements and rotation of the plurality of qubits can transform an input state of the input qubit into an output state of the output qubit. The machine learning model can be trained based on the output state of the output qubit.

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: A method and system of generating a route includes receiving information regarding a set of nodes to be serviced. One or more parameters of each node are determined. A capacity of each of the one or more vehicles is determined. A classical computer is used to generate a set of feasible routes based on the one or more parameters of each node and the capacity of each of the one or more vehicles. A number of bags N to divide the set of feasible routes is determined. The feasible routes are distributed into the N bags. The N bags are sent to a quantum computer to calculate a most efficient combination of feasible routes that cover all nodes to be serviced.

Abstract: To obtain meaningful computational results despite limits on the amount of data that can be input to a quantum computer, a data selection system uses an iterative approach to select a suitable subset of data to be input to a quantum device for processing by a quantum algorithm. The system compresses and clusters a data set according to a task-specific distribution criteria and selects a subset of this clustered data corresponding to representative cases of the data. The selected subset is processed by the quantum device and the system generates a metric score based on the degree to which the results satisfy a performance criterion. The selected subset is refined over multiple iterations based on successive metric scores until a termination criterion is reached, and the final selected subset of data is used as input to the quantum computer for execution of the processing task.

Abstract: A method and system of generating a route includes receiving information regarding a set of nodes to be serviced. One or more parameters of each node are determined. A capacity of each of the one or more vehicles is determined. A classical computer is used to generate a set of feasible routes based on the one or more parameters of each node and the capacity of each of the one or more vehicles. A number of bags N to divide the set of feasible routes is determined. The feasible routes are distributed into the N bags. The N bags are sent to a quantum computer to calculate a most efficient combination of feasible routes that cover all nodes to be serviced.