Tensor network annealing method and system for optimization of machine learning tasks

Abstract

A computer-implemented method for performing computational optimization in machine learning and data science involves a system equipped with at least one processor and memory. This method encompasses storing vectors, defining vector-scalar multiplication, and configuring the processor to operate as an annealer. Key steps include transforming a multi-variable binary function into a matrix product state (MPS) format, constructing the MPS with a specific bond dimension, and decomposing vectors for optimal efficiency. The method employs a maximal bond dimension for the tensor train, determining its final state, and utilizes tensor network compression techniques to compress the matrix product state. It addresses Quadratic Unconstrained Binary Optimization (QUBO), frequently used in machine learning, to optimize binary functions. Additionally, the system includes a computer program product and a computer-readable data carrier for executing this method, providing a robust framework for annealing tasks and optimization using memory and processor configurations designed for matrix product state representation and compression.

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