Intel Corporation

Other

Anindya Sankar Paul

Michael E Kounavis

Micah James Sheller

Nilesh K Jain

Li Chen

Cory Cornelius

Anne Y Matsuura

Peter Jacobs

Shavindra Premaratne

Sahar Daraeizadeh

Fahim Mohammad

Antonios Papadimitriou

Vui Seng Chua

Eric A Wan

Brandon Edwards

Apparatus and methods for real time calibration of qubits in a quantum processor. For example, one embodiment of an apparatus comprises: a quantum processor comprising a plurality of qubits, each of the qubits having a state; a quantum controller to generate sequences of electromagnetic (EM) pulses to manipulate the states of the plurality of qubits based on a set of control parameters; a qubit measurement unit to measure one or more sensors associated with a corresponding one or more of the qubits of the plurality of qubits to produce one or more corresponding measured values; and a machine-learning engine to evaluate the one or more measured values in accordance with a machine-learning process to generate updated control parameters, wherein the quantum controller is to use the updated control parameters to generate subsequent sequences of EM pulses to manipulate the states of the plurality of qubits.

System and method for automatic real-time calibration of qubit chips

In one example an apparatus comprises a memory and a processor to create, from a first deep neural network (DNN) model, a first plurality of DNN models, generate a first set of adversarial examples that are misclassified by the first plurality of deep neural network (DNN) models, determine a first set of activation path differentials between the first plurality of adversarial examples, generate, from the first set of activation path differentials, at least one composite adversarial example which incorporates at least one intersecting critical path that is shared between at least two adversarial examples in the first set of adversarial examples, and use the at least one composite adversarial example to generate a set of inputs for a subsequent training iteration of the DNN model. Other examples may be described.

Adversarial training of neural networks using information about activation path differentials

Example systems, methods, and apparatus to generate optimized models for Internet of Things device are disclosed. An example apparatus includes a data receiver to collect data from a sensor of an internet of things device based a first sampling frequency and a buffer having a first buffer size; a model trainer to train a model based on the data collected from the sensor; a buffer analyzer to select a second sampling frequency and to reduce the buffer to a second buffer size, the model trainer to update the model based on the second buffer size; and a platform analyzer to: determine a duration of time that that internet of things device will take to analyze sensor data based on the updated model.

Methods and apparatus to generate optimized models for internet of things devices

The invention relates to a system and method for monitoring the location, movement and health of one or more individuals within an environment by a monitoring individual, such as a care giver. The system used includes optional monitoring devices including a wireless transceiver, access point devices including a wireless transceiver, a hub access point device including a wireless transceiver, and a local computing device. The system is programmed such that it has the capability to operate with or without the measure of time of flight value from the optional monitoring devices such that the system has the capability of monitoring the location, movement and health of an individual whether or not the individual is wearing the monitoring device.

Position tracking and mobility assessment system

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Hillsboro, OR, United States of America

Anindya Sankar Paul