Fujitsu

According to an aspect of an embodiment, operations may include obtaining data corresponding to a completed process flow, the completed process flow being associated with an incident that has been resolved. The operations may further include generating one or more labels that serve as target variables for training a machine learning model based on the data corresponding to the completed process flow. The operations may additionally include extracting a subset of the data corresponding to a predefined time range cutoff. Further, the operations may include generating a plurality of features derived from the data corresponding to the completed process flow for training the machine learning model and training the machine learning model to predict future behavior of ongoing process flows using the plurality of features and the one or more labels associated with the data corresponding to the completed process flow.

An information processing apparatus generates a waveform function that defines a temporal change in amplitude in a first interval of a rise time of a pulse signal using a first polynomial having a first parameter as a coefficient and defines a temporal change in the amplitude in a second interval of the rise time using a second polynomial having a second parameter as a coefficient. The information processing apparatus determines a waveform using the waveform function by assigning a first parameter value group to the parameter group. The information processing apparatus changes a parameter value group assigned to the parameter group, from the first parameter value group to a second parameter value group, based on a result of quantum computation performed by a quantum computer using a pulse signal having the waveform.

A processing unit causes a quantum computer to execute a quantum circuit including a first-polarity first control gate and a second-polarity second control gate, each controlled by an auxiliary qubit. The first control gate applies a first time-evolution operator corresponding to a Hamiltonian H(λ) to a qubit group corresponding to a quantum state |ψ>. The second control gate applies a second time-evolution operator corresponding to H(λ + δλ) to the qubit group. The processing unit acquires, from the quantum computer, a probability amplitude corresponding to an event that a result of applying the two types of time-evolution operators to the quantum state |ψ> matches |ψ>. The processing unit acquires a cumulative distribution function for an energy level difference between H(λ) and H(λ + δλ) by executing discrete Fourier transform using the probability amplitude, and estimates the difference based on the cumulative distribution function.

An information processing apparatus determines, based on target information regarding an imitation target, a first knowledge domain in which the imitation target has knowledge and a first knowledge level of the imitation target regarding the first knowledge domain. Upon receiving a first question, the information processing apparatus instructs an agent, which responds to a question by using a language model, to generate a response to the first question. In instructing the response, the information processing apparatus instructs a response generated by imitating thoughts of the imitation target that are referable to information restricted based on the first knowledge domain and the first knowledge level.

A machine learning device includes a processor that executes a procedure. The procedure including: specifying, among a plurality of points that are present within a predetermined distance from a first point in a latent space of an autoencoder, a second point that satisfies a predetermined relationship with the first point; and updating at least a parameter of a decoder of the autoencoder by optimization of an objective function including a regularization term to reduce a difference between the first point and the second point in the latent space.

A processing unit acquires, for a plurality of terms included in a Hamiltonian corresponding to a problem to be solved by a variational quantum eigensolver and capable of being simultaneously measured by a quantum computer, first information indicating the variance of each term and the covariance of each pair of terms, upon performing a simultaneous measurement, generates, based on the first information, an optimization problem that minimizes a total sum of the variance of each term and the covariance of each pair based on the constraint that the amount of a change with respect to an average value of the sum of the terms is equal to or less than a predetermined value, determines a plurality of first terms to be simultaneously measured by the quantum computer by solving the optimization problem, and computes the number of samples of the simultaneous measurement based on the minimized total sum.

An information processing apparatus performs, a plurality of times, an update process of updating a value of a first parameter, which is a variable included in a cost function applied to a variational quantum circuit used for variational quantum eigenvalue computation. The information processing apparatus determines a value of a second parameter representing a weight for an amount of change in the value of the first parameter in each update process, by adding a constant term and a variable term expressed using a ratio between the values of the cost function calculated through the variational quantum eigenvalue computation using the values of the first parameter obtained in the k-th and (k-1)-th update processes. The information processing apparatus performs the (k+1)-th update process using the amount of change weighted by the determined value of the second parameter.

An information processing apparatus acquires error data indicating an error of a first quantum gate. The information processing apparatus generates, using an approximation function that linearly approximates an influence of an additional quantum gate, which is to be added to the first quantum gate, on an operation of the first quantum gate, a linear equation which includes a variable corresponding to the additional quantum gate and indicates a relationship in which the error is canceled by the additional quantum gate. The information processing apparatus determines a second quantum gate corresponding to the additional quantum gate by solving the linear equation for the variable. The information processing apparatus determines a second quantum gate corresponding to the additional quantum gate by solving the linear equation for the variables.

In an embodiment, a parameterized quantum circuit is initialized on a quantum computer for a Quantum Neural Network (QNN) with an ansatz architecture. Input data, which includes a quantum representation of a multi-dataset and an initial state of the QNN, and labels, is received. The quantum representation of the multi-datasets on the QNN is loaded and a first dataset of the multi-dataset is labelled. A label-controlled circuit associated with label-controlled input qubits of the ansatz architecture for the QNN is determined, based on the labelled first dataset. A universal circuit associated with the ansatz architecture for a prediction circuit associated with the QNN is determined, based on labelled first dataset. The prediction circuit is determined based on the label-controlled circuit and the universal circuit. The prediction circuit is trained and configured to generate predictions associated with the loaded fractionally weighted data portfolios.

A computer implemented method for determining an origin of indication information in a networked connection between a client device and a server, comprising: receiving, at the server, the indication information of the client device, determining a client-server value based on a transmission time of a connection signal transmitted between the client device and the server, transmitting a tracing signal addressed to the indication information via a particular router in the networked connection between the client device and the server, determining a tracing value based on a transmission time of the tracing signal to the particular router, establishing whether the indication information originates from an intermediate connection in the networked connection or from the client device by comparing a difference value with a threshold value, the difference value being based on the client-server value and tracing value.