Provided is a technology for creating an optimization function for solving a bandwidth allocation plan problem for determining bandwidths to be allocated for respective paths under various constraints regarding the paths for which bandwidths are to be allocated, the optimization function relating to a variable that represents a quantum state. The technology includes: an input setting unit that sets a set Path of paths, a set Edge of edges, a maximum bandwidth Max, a bandwidth p.bandwidth required by a path p, and a set e.paths of paths that include an edge e, as input to a bandwidth allocation plan problem for creating a bandwidth allocation plan for paths so as to satisfy a condition of minimizing a bandwidth allocated for the paths of the set Path as a whole under a predetermined constraint condition; and an optimization function creation unit that creates the optimization function using the input.

Provided is a technology for creating an optimization function for solving a bandwidth allocation plan problem for determining bandwidths to be allocated for respective paths under various constraints regarding the paths for which bandwidths are to be allocated, the optimization function relating to a variable that represents a quantum state. The technology includes: an input setting unit that sets a set Path of paths, a set Edge of edges, a maximum bandwidth Max, a bandwidth p.bandwidth required by a path p, and a set e.paths of paths that include an edge e, as input to a bandwidth allocation plan problem for creating a bandwidth allocation plan for paths so as to satisfy a condition of minimizing a bandwidth allocated for the paths of the set Path as a whole under a predetermined constraint condition; and an optimization function creation unit that creates the optimization function using the input.

Aspects of the present disclosure relate generally to systems and methods for use in the implementation and/or operation of quantum information processing (QIP) systems, and more particularly, to the computation of joint probability distributions with quantum computers. Improvements in the computation of joint probability distributions are described by designing quantum machine learning algorithms to model copulas. Moreover, any copula is shown to be naturally mapped to a multipartite maximally entangled state. A variational ansatz referred to herein as a “qopula” creates arbitrary correlations between variables while maintaining the copula structure starting from a set of Bell pairs for two variables, or Greenberger-Horne-Zeilinger (GHZ) states for multiple variables. Generative learning algorithms may be demonstrated on quantum computers, and more particularly, in trapped-ion quantum computers. The approach described herein is shown to have advantages over classical models.

Aspects of the present disclosure relate generally to systems and methods for use in the implementation and/or operation of quantum information processing (QIP) systems, and more particularly, to the computation of joint probability distributions with quantum computers. Improvements in the computation of joint probability distributions are described by designing quantum machine learning algorithms to model copulas. Moreover, any copula is shown to be naturally mapped to a multipartite maximally entangled state. A variational ansatz referred to herein as a “qopula” creates arbitrary correlations between variables while maintaining the copula structure starting from a set of Bell pairs for two variables, or Greenberger-Horne-Zeilinger (GHZ) states for multiple variables. Generative learning algorithms may be demonstrated on quantum computers, and more particularly, in trapped-ion quantum computers. The approach described herein is shown to have advantages over classical models.

A method of rating credit risk is provided. The method comprises calculating a number of credit risk factors associated with a financial instrument, wherein each credit risk factor is calculated iteratively at a first timestep as a discrete probabilistic wave function representing a superposition state of scores. The discrete probabilistic wave function of each credit risk factor is measured after each calculation iteration for the first timestep. The probabilistic wave functions of the credit risk factors are then linearly combined to calculate a discrete probabilistic wave function for a final credit rating of the financial instrument for the first timestep, which is displayed in a user interface. The above steps are repeated for a second timestep using the probabilistic wave functions of the credit risk factors at the first timestep as initial states for the second timestep.

The driver Hamiltonian is modified in such a way that the quantum approximate optimization algorithm (QAOA) running on a circuit-model quantum computing facility (e.g., actual quantum computing device or simulator), may better solve combinatorial optimization problems than with the baseline/default choice of driver Hamiltonian. For example, the driver Hamiltonian may be chosen so that the overall Hamiltonian is non-stoquastic.

The driver Hamiltonian is modified in such a way that the quantum approximate optimization algorithm (QAOA) running on a circuit-model quantum computing facility (e.g., actual quantum computing device or simulator), may better solve combinatorial optimization problems than with the baseline/default choice of driver Hamiltonian. For example, the driver Hamiltonian may be chosen so that the overall Hamiltonian is non-stoquastic.

A method for validation and runtime estimation of a quantum algorithm includes receiving a quantum algorithm and simulating the quantum algorithm, the quantum algorithm forming a set of quantum gates. The method further includes analyzing a first set of parameters of the set of quantum gates and analyzing a second set of parameters of a set of qubits performing the set of quantum gates. The method further includes transforming, in response to determining at least one of the first set of parameters or the second set of parameters meets an acceptability criterion, the quantum algorithm into a second set of quantum gates.

A method for validation and runtime estimation of a quantum algorithm includes receiving a quantum algorithm and simulating the quantum algorithm, the quantum algorithm forming a set of quantum gates. The method further includes analyzing a first set of parameters of the set of quantum gates and analyzing a second set of parameters of a set of qubits performing the set of quantum gates. The method further includes transforming, in response to determining at least one of the first set of parameters or the second set of parameters meets an acceptability criterion, the quantum algorithm into a second set of quantum gates.

The present invention relates generally to the design of quantum optical configurations and more specifically to using graph theory mapping and fidelity optimization to design optimal quantum optical configurations that have maximal fidelity between the designed optimal quantum optical configuration and the target quantum state. The target quantum state may include resource-efficient heralded multi-photonic quantum states, heralded high-dimensional entanglement, resource states for quantum gates, and high-dimensional multi-photonic GHZ states without ancilla photons.