Abstract: A processor-implemented method includes generating, by a scheduling model, a group of schedules from a computation graph associated with a task, each node on the computation graph being associated with an operation of an artificial neural network, each schedule of the group of schedules associating each node of the computation graph with a processor of a group of processors of a hardware device. The processor-implemented method also includes testing one or more schedules of the group of schedules on the hardware device or a model of the hardware device. The processor-implemented method further includes selecting a schedule of the one or more schedules based on testing the one or more schedules, the selected schedule satisfying a selection condition.

Abstract: Certain aspects of the present disclosure provide a method for performing quantum convolution, including: receiving input data at a neural network model, wherein the neural network model comprises at least one quantum convolutional layer; performing quantum convolution on the input data using the at least one quantum convolutional layer; generating an output wave function based on the quantum convolution using the at least one quantum convolution layer; generating a marginal probability distribution based on the output wave function; and generating an inference based on the marginal probability distribution.

Abstract: A processor-implemented method for generating a topological order using an artificial neural network (ANN) includes receiving a set of tasks to be performed. The tasks are represented in a graph including multiple nodes connected by edges. Each node corresponds to a task in the set of tasks. A scheduling priority is assigned to each node in the graph. A next node of potential next nodes is selected according to a probability of each of the potential next nodes based on the assigned scheduling priorities and a topology of the graph. A topological order of the tasks is generated by repeating the selection of the next node.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for demapping a signal to a point in a signal constellation. An example method generally includes identifying a seed point in a signal constellation from a received signal. A candidate set of codes for the signal is generated based on a seed point and an additive perturbation applied to the seed point. A point in the signal constellation corresponding to the value of the received signal is identified based on a probability distribution generated over the candidate set of codes. Generally, the identified point corresponds to a code in the candidate set of codes having a highest probability in the probability distribution. The point in the signal constellation is output as the value of the received signal.

Abstract: Certain aspects of the present disclosure provide techniques for performing probabilistic convolution operation with a quantum and non-quantum processing systems.

Abstract: Certain aspects of the present disclosure provide techniques for processing data in a quantum deformed binary neural network, including: determining an input state for a layer of the quantum deformed binary neural network; computing a mean and variance for one or more observables in the layer; and returning an output activation probability based on the mean and variance for the one or more observables in the layer.

Abstract: Certain aspects of the present disclosure provide techniques for performing operations with probabilistic numeric convolutional neural network, including: defining a Gaussian Process based on a mean and a covariance of input data; applying a linear operator to the Gaussian Process to generate pre-activation data; applying a nonlinear operation to the pre-activation data to form activation data; and applying a pooling operation to the activation data to generate an inference.

Abstract: Certain aspects of the present disclosure provide a method for performing quantum convolution, including: receiving input data at a neural network model, wherein the neural network model comprises at least one quantum convolutional layer; performing quantum convolution on the input data using the at least one quantum convolutional layer; generating an output wave function based on the quantum convolution using the at least one quantum convolution layer; generating a marginal probability distribution based on the output wave function; and generating an inference based on the marginal probability distribution.