Abstract: A technology for generating an optimization function for solving a combinatorial optimization problem, which can be expressed by using fewer quantum bytes is provided. An optimization function generation apparatus includes an input setting unit that sets an input of a combinatorial optimization problem, and an optimization function generation unit that generates an optimization function for solving the combinatorial optimization problem using the input, in which N is an integer equal to or greater than 1, Idx={0, . . . , N?1} is a set of indexes indicating N states, and i satisfies i ? Idx, and the optimization function generation unit generates an optimization function using a quantum byte obtained by performing encoding so that digits when an index i is expressed as a binary number each match a corresponding quantum bit included in a quantum byte that is a sequence of log2N quantum bits.

Abstract: A technique for solving a delivery planning problem for delivering a vehicle to a parking lot short of vehicles to be parked. Included are an input setting unit that sets a set of staff members, a set of vehicles, a set of parking lots, a delivery end time, parking lots with a staff member being present at a delivery start time, a staff cost per unit time, a parking lot with a vehicle being present at the delivery start time, a vehicle cost per unit time, a maximum value of staff members capable of boarding the vehicle, a set of parking lots neighboring to the parking lot, a time period required for moving from the parking lot to a parking lot neighboring to the parking lot, and the number of vehicles to be still accommodated in the parking lot as input.

Abstract: Provided is a technology for creating an optimization function for solving a scheduling problem for creating a process execution plan such that execution of all processes will be completed by a predetermined time point under constraint conditions regarding the processes and resources required by the processes, the optimization function relating to a variable that represents a quantum state. The technology includes: an input setting unit that sets a set P of processes, a set R of resources, a maximum time point End, the time p.time it takes to execute a process p, a set p.precede of processes of which execution needs to be completed before execution of the process p is started, and a set r.

Abstract: 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.

Abstract: A classification unit causes each of a plurality of classifiers trained to classify data of a corresponding class into one of two values through qSVM to classify data for prediction. Further, the calculation unit calculates the energy of the classification result of the data for prediction for each of the plurality of classifiers. Further, the determination unit determines a class of the data for prediction based on the classification result of the classification unit and the energy calculated by the calculation unit.

Abstract: A wearable device attached to a subject includes an accelerometer that measures acceleration information, and a biological sensor that measures biological signal information of the subject. From the measured acceleration information and biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period are extracted. By machine learning based on the first feature data, a dynamic/static activity identification model, a dynamic-activity identification model, and a static-activity identification model, for the subject, are generated. By combination of results of determination based on each of the identification models, a posture and an activity of the subject are identified, and correspondence information, which associates the identified posture and activity with the biological signal information of the subject, is generated.

Abstract: A wearable device attached to a subject includes an accelerometer that measures acceleration information, and a biological sensor that measures biological signal information of the subject. From the measured acceleration information and biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period are extracted. By machine learning based on the first feature data, a dynamic/static activity identification model, a dynamic-activity identification model, and a static-activity identification model, for the subject, are generated. By combination of results of determination based on each of the identification models, a posture and an activity of the subject are identified and correspondence information, which associates the identified posture and activity with the biological signal information of the subject, is generated.

Abstract: A wearable device attached to a subject includes an accelerometer that measures acceleration information, and a biological sensor that measures biological signal information of the subject. From the measured acceleration information and biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period are extracted. By machine learning based on the first feature data, a dynamic/static activity identification model, a dynamic-activity identification model, and a static-activity identification model, for the subject, are generated. By combination of results of determination based on each of the identification models, a posture and an activity of the subject are identified, and correspondence information, which associates the identified posture and activity with the biological signal information of the subject, is generated.