SAVING QUBITS BY OPTIMIZING ONE-HOT ENCODING GRANULARITY, RANGE, AND POINT DISTRIBUTION

Abstract: a machine learning model is trained, using historical data, to generate distributions for integer variables of a problem. when a new problem or problem instance is presented, the model is used to predict a distribution for each of the integer variables. a range is determined from each of the distributions. one-hot encoded binary variables are generated from the ranges. this reduces the number of qubits needed to one-hot encode the problem instance.

Inventor(s): Diego Vrague Noble, Miguel Paredes Quiñones, Ítalo Gomes Santana

CPC Classification: G06N5/022 (Knowledge representation; Symbolic representation)

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