A system for scalable, fault-tolerant photonic quantum computing includes multiple optical circuits, multiple photon number resolving detectors (PNRs), a multiplexer, and an integrated circuit (IC). During operation, the optical circuits generate output states via Gaussian Boson sampling (GBS), and the PNRs generate qubit clusters based on the output states. The multiplexer multiplexes the qubit clusters and replaces empty modes with squeezed vacuum states, to generate multiple hybrid resource states. The IC stitches together the hybrid resource states into a higher-dimensional cluster state that includes states for fault-tolerant quantum computation.

A distributed processing management apparatus 10 is connected to a plurality of execution servers 20 so as to be able to communicate with the execution servers 20. The distributed processing management apparatus 10 is provided with a conversion instruction unit 11 configured to specify, for each execution server 20, a data format usable by a machine learning engine executed by the execution server 20, and issue an instruction to convert a data format of data held by the execution server 20 to the specified data format.

A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine performance that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. The system may select a reconfiguration plan in response to the performance satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

According to one embodiment, a calculating device includes a nonlinear oscillator. The nonlinear oscillator includes a circuit part including a first Josephson junction and a second Josephson junction, and a conductive member including a first terminal. An electrical signal is input to the first terminal. The electrical signal includes a first signal in a first operation. The first signal includes a first frequency component having a first frequency, and a second frequency component having a second frequency. The first frequency is 2 times an oscillation frequency of the nonlinear oscillator. An absolute value of a difference between the first frequency and the second frequency is not more than 0.3 times the first frequency.

According to one embodiment, a calculating device includes a nonlinear oscillator. The nonlinear oscillator includes a circuit part including a first Josephson junction and a second Josephson junction, and a conductive member including a first terminal. An electrical signal is input to the first terminal. The electrical signal includes a first signal in a first operation. The first signal includes a first frequency component having a first frequency, and a second frequency component having a second frequency. The first frequency is 2 times an oscillation frequency of the nonlinear oscillator. An absolute value of a difference between the first frequency and the second frequency is not more than 0.3 times the first frequency.

A local object classifier using a set of object definitions to perform object classification in image frames. The local object classifier is arranged to detect an object in an image frame and determine whether to transmit image data for the detected object to a remote object classifier. In response to said determining, the local object classifier is arranged to transmit image data, derived from the image data representative of the image frame, to the remote object classifier. The local object classifier is also arranged to receive object data, representative of the detected object, from the remote object classifier.

A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine a performance gain that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. In addition, the system may determine a performance loss that would result from the respective databases of the nodes being inaccessible during reconfiguration with the configuration parameter sets. The system may select a reconfiguration plan in response to the performance gain and the performance loss satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Making failure scenarios using adversarial reinforcement learning is performed by storing, in a first storage, a variety of first experiences of failures of a player agent due to an adversarial agent, and performing a simulation of an environment including the player agent and the adversarial agent. It also includes calculating a similarity of a second experience of a failure of the player agent in the simulation and each of the variety of first experiences in the first storage, and updating the first storage by adding the second experience as a new first experience of the variety of first experiences in response to the similarity being less than a threshold. Additionally, the use of adversarial reinforcement learning can include training the adversarial agent by using at least one of the plurality of first experiences in the first storage to generate an adversarial agent having diverse experiences.

The present disclosure pertains to autonomous control of an imaging system. In some embodiments, training information including at least a plurality of images and action information are received. The plurality of images and action information are provided to a prediction model to train the prediction model. Further, an image capturing device is controlled to capture an image of a portion of a living organism, the image is processed, via the prediction model, to determine an action to be taken with respect to the image, and the determined action is taken with respect to the image.

Various embodiments provide systems and methods for live action gaming.