Qualcomm Incorporated

Roberto Bondesan

Max Welling

Pouriya Sadeghi

Sanaz Barghi

Arash Behboodi

Markus Peschl

Daniel Ernest Worrall

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.

Quantum deformed binary neural networks

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.

Lattice reduction-aided perturbed additive demapper for multiple-input multiple-output signal detection

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.

Growing community of inventors

Amsterdam, Netherlands

Roberto Bondesan