Application of a Quantum Annealer for Pattern Recognition
Evgenii V. Burlakov · 2025
Adiabatic quantum computing is successfully used to solve discrete optimization problems. The classical Hopfield neural network can also be applied to such problems but is primarily used for pattern recognition and reconstruction. Since its dynamics are deterministic, this can adversely affect its performance: the network sometimes gets trapped in local energy minima. This paper proposes a novel approach in which the Hopfield neural network learning rule (Hebbian rule) is combined with forward and reverse quantum annealing. This enables the use of adiabatic quantum annealers for pattern recognition, providing an alternative to traditional approaches.