Abstract: 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.

Abstract: 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.