Abstract: A thermalized state preparation method under a quantum system, a device and a storage medium relate to the field of quantum technologies. The method includes: acquiring a transformation process performed by a parameterized quantum circuit on an input quantum state of combined qubits to obtain n sets of measurement results (210); processing the second measurement results through a neural network to obtain weight parameters (220); computing a correlation function value of a mixed state of the target quantum system based on the weight parameters and the first measurement results (230); and computing an expected value of an objective function based on the correlation function value (240); adjusting variational parameters taking an expected value convergence of the objective function as a goal (250); and acquiring, under a condition that the expected value satisfies a convergence condition, the mixed state to approximately characterize a thermalized state of the target quantum system (260).

Abstract: A method includes generating, by a large language model (LLM), a multitude of match explanations for a multitude of search targets. The multitude of match explanations is clustered to obtain a multitude of explanation clusters. An explanation template is generated for an explanation cluster. The explanation template includes a static portion and a dynamic portion. A multitude of explanation templates is obtained in this manner. A target match for a search target received from a user application is obtained. A corresponding match explanation is generated by the LLM for the target match. An encoding of the corresponding match explanation is matched to a multitude of encoded templates corresponding to the multitude of explanation templates. Responsive to the encoding matching an encoded template a small language model (SLM) populates the corresponding match explanation from the search target.

Abstract: A system is provided for control of an entertainment state system having segregated secure functions and public functions for use by one or more users of the system. First, a public interface portal receives instructions regarding operation of the entertainment state system from the one or more users. The interface portal includes a first interface, a processor, a graphical user interface (GUI) coupled to the processor, a control unit in operative communication with the processor and graphical user interface, and a second interface providing an application program interface (API). Secondly, a secure entity unit is provided, the secure entity unit including a receive interface, the receive interface adapted to receive a call from the application program interface (API) of the interface portal, a send interface, the send interface adapted to provide a response to the interface portal interface, a game engine, and a financial engine.

Abstract: The present application discloses a method, system, and computer system for building a model associated with a dataset. The method includes receiving a data set, the dataset comprising a plurality of keys and a plurality of key-value relationships, determining a plurality of models to build based at least in part on the dataset, wherein determining the plurality of models to build comprises using the dataset format information to identify the plurality of models, building the plurality of models, and optimizing at least one of the plurality of models.

Abstract: A method comprising, by first computer equipment: obtaining an input data point comprising a set of values, each being a value of a different element of an input feature vector; inputting the input data point to a first machine learning model on the first computer equipment to generate at least one associated output label based on the input data point; sending a partial data point to second computer equipment, the partial data point comprising the values of only part of the feature vector; and sending the associated label to the second computer equipment in association with the partial data point, thereby causing the second computer equipment to train a second machine learning model on the second computer equipment based on the sent part and the associated label.

Abstract: A learning apparatus according to one embodiment includes an input unit configured to input a normal data collection for a first system that is a target domain and to input a normal data collection for a second system that is a source domain. The learning apparatus includes a learning unit configured to train a model that includes a first autoencoder configured to input normal data for the target domain, based on the normal data collection for the first system and the normal data collection for the second system. The model includes a second autoencoder configured to input normal data for the source domain, and includes a discriminator configured to output a probability that output data is data representing a feature for any one of the target domain and the source domain, while using, as an input, output data, output data of a first encoder included in the first autoencoder or a second encoder included in the second autoencoder.

Abstract: Techniques for generating personalized content using generative artificial intelligence (AI) are provided. In an example method, a processing device including a personalization module receives an indication that a user interacted with content displayed on a web page. The personalization module receives a set of attributes, comprising information about the user and information about the content, and information about a segment to which the user belongs. The processing module then determines a tuning parameter, wherein the tuning parameter controls the randomness of the output of the generative AI model. The personalization module next inputs to the generative AI model the tuning parameter and a prompt comprising the set of attributes and the information about the segment and subsequently receives personalized content responsive to the tuning parameter and the prompt. The personalization module can then display the personalized content in a dynamic content field associated with the content.

Abstract: Computer implemented methods for training a Student Neural Network, SNN, and for managing an environment of a communication network using a trained SNN are disclosed. The SNN is for generating an action prediction matrix for an environment in a communication network, the action prediction matrix comprising action predictions for a plurality of nodes or resources in the environment. The training method comprises using a Reinforcement Learning process to train a Teacher Neural Network, TNN, to generate an action prediction for a resource or node in the environment, and using the trained TNN to generate a first training data set including action predictions for individual nodes or resources. The training method further comprises generating a second training data set from the first training data set such that the second training data set includes action prediction matrices, and using the second training data set to update values of the parameters of the SNN.

Abstract: Some embodiments provide a method for training parameters of a network. The method receives an MT network having multiple layers of computation nodes that use first sets of weight values. Each computation node of a first layer computes an output value by computing an intermediate value based on a set of input values and a set of weight values and computing a node output value by applying an activation function to the intermediate value. The method replaces the first layer with a second layer of computation nodes followed by a third layer of computation nodes. The replacement involves (i) decomposing the sets of weight values of the first layer into second sets of weight values for the second layer and third sets of weight values for the third layer and (ii) inserting into the network a set of additional activation functions for the second layer.

Abstract: The present disclosure is directed to machine learning model architectures which provide full attention capability in each attention head while maintaining low computation and memory complexity. Specifically, according to one aspect of the present disclosure, example attention models provided herein can treat the self-attention mechanism as a conditional expectation over embeddings at each location and approximate the conditional distribution with a structured factorization. Each location can attend to all other locations, either via direct attention, or through indirect attention to group representations, which are again conditional expectations of embeddings from corresponding local regions.

Abstract: Various systems and methods are presented regarding utilizing a spiral resonator to enhance coupling between a first inductor loop and a second inductor loop to enable coupling between a first qubit and a second qubit. Operation of the first inductor loop can be controlled by a flux-tunable TCQ coupler, wherein flux-tuning can adjust operation from an OFF state (no coupling between the first qubit and the second qubit) to an ON state (the first qubit and second qubit are coupled). The spiral resonator can be located at the center of, and in the same plane as the loop of the first inductor loop. The spiral resonator can enhance inductive coupling between the first loop inductor and the second loop inductor.

Abstract: A system includes a sensor, a plurality of cells, and a processor. The sensor includes a plurality of transducers arranged on a planar surface and includes a first transducer and a second transducer. The first transducer is configured to produce an analog output signal corresponding to a detected input signal. The cells are arranged in a network and include a first and a second cell and are disposed proximate the sensor in a three-dimensional stacking fashion. The first transducer and the second transducer are electrically coupled to the first cell and the second cell in one-to-one relation. The first cell includes a plurality of inputs and a first cell output. Each input is coupled to an output of a corresponding plurality of neighboring cells. The first cell includes a first memristor and a bridge circuit configured to receive the analog output signal and provide a current corresponding to the detected input signal in a pixel-parallel fashion.

Abstract: Methods, systems, and apparatus for performing an entangling operation on a system of qubits. In one aspect, a method includes operating the system of qubits, wherein the system of qubits comprises: a plurality of first qubits, a plurality of second qubits, a plurality of qubit couplers defining nearest neighbor interactions between the first qubits and second qubits, wherein the system of qubits is arranged as a two dimensional grid and each qubit of the multiple first qubits is coupled to multiple second qubits through respective qubit couplers, and wherein operating the system of qubits comprises: pairing multiple first qubits with respective neighboring second qubits; performing an entangling operation on each paired first and second qubit in parallel, comprising detuning each second qubit in the paired first and second qubits in parallel.

Abstract: A computer-implemented method in a model-based reinforcement learning (RL) system with logic states includes switching an agent between a first mode and a second mode, the first mode being a probabilistic planning mode and the second mode being an information gathering mode. In response to the agent being in the probabilistic planning mode, the agent computes a predictive state representation, given a history of observations and actions taken, and the agent scores action candidates based on planning with the predictive state representation so that actions with resolved plans with confidence to achieve a goal state are scored higher than actions without resolved plans. In response to the agent being in the information gathering mode, the agent scores action candidates based on a Q function of a value of expected information to be gathered from a given pair of state and action.

Abstract: Methods and computational devices for the implementation thereof that enable real-time Context Resets in conversational artificial intelligence (AI) systems are provided. The methods and computational devices improve the functioning of conversational AI systems by reducing non-productive computation, preventing propagation of irrelevant Active Context, and optimizing memory use during AI sessions. These operations restore conversational coherence by reducing Misalignment or Confusion between User Inputs and System Outputs. Accordingly, the invention provides a measurable improvement to computer functionality through adaptive, User-guided context management.

Abstract: Provided is a method for classifying data in an electronic apparatus, including obtaining target data, obtaining first classification information by using a classifier set including a plurality of classifiers based on the target data, obtaining second classification information by using a neural network model based on the target data, comparing the first classification information and the second classification information, and verifying the classifier set based on a result of comparing the first classification information and the second classification information.

Abstract: A device, computer program and computer-implemented method for machine learning. The method comprises providing a task comprising an action space of a multi-armed bandit problem or a contextual bandit problem and a distribution over rewards that is conditioned on actions, providing a hyperprior, wherein the hyperprior is a distribution over the action space, determining, depending on the hyperprior, a hyperposterior for that a lower bound for an expected reward on future bandit tasks has as large a value as possible, when using priors sampled from the hyperposterior, and wherein the hyperposterior is a distribution over the action space.

Abstract: The described technology is generally directed towards automated development of machine learning pipelines. An automated framework can extract topics from a data science workspace such as a machine learning notebook, transform and annotate cells of the machine learning notebook to various machine learning pipeline stages, and orchestrate the machine learning pipeline stages in a workflow that can be deployed into production data infrastructures.

Abstract: A computer-implemented method includes receiving an incorrect prediction output by a trained machine learning model, which has been trained using training data items. The method includes identifying a training data item used to train the model that is a cause of the incorrect prediction, by determining an impact on performance of the trained machine learning model associated with removing the training data item from the plurality of training data. The trained model can then be updated to remove the effect of the identified training data item, allowing the model to be automatically corrected in view of poor quality training data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes an attention neural network configured to perform the machine learning task, the attention neural network including one or more attention layers, each attention layer comprising an attention sub-layer and a feed-forward sub-layer that applies an element-wise multiplication between two vectors generated as a result of two different linear transformations performed on the same attended layer input.