Running quantum algorithms often involves implementing complex quantum circuits with
such a large number of multi-qubit gates that the challenge of tackling practical applications …

We propose a technique for optimizing parameterized circuits in variational quantum
algorithms based on the probabilistic tensor sampling optimization. This method allows one …

Elements of neural networks, both biological and artificial, can be described by their
selectivity for specific cognitive features. Understanding these features is important for …

In optimization, one of the well-known classical algorithms is power iterations. Simply stated,
the algorithm recovers the dominant eigenvector of some diagonalizable matrix. Since …

Tensor networks are a tool first employed in the context of many-body quantum physics that
now have a wide range of uses across the computational sciences, from numerical methods …

This work concerns the zeroth-order global minimization of continuous nonconvex functions
with a unique global minimizer and possibly multiple local minimizers. We formulate a …

Neural networks are deployed widely in natural language processing tasks on the industrial
scale, and perhaps most often they are used as compounds of automatic machine …

We present a novel approach to enhance the performance of sampling-based Model
Predictive Control (MPC) in constrained optimization by leveraging products of experts. Our …

Deep neural networks (DNNs) are widely used today, but they are vulnerable to adversarial
attacks. To develop effective methods of defense, it is important to understand the potential …

Combinatorial optimization problems are pivotal across many fields. Among these,
Quadratic Unconstrained Binary Optimization (QUBO) problems, central to fields like …