Quantum Pattern Recognition

TL;DR

This paper reviews and extends a quantum associative memory model that stores binary patterns in superposition, enabling high-capacity, probabilistic pattern recognition with tunable accuracy, inspired by brain-like features.

Contribution

The paper introduces an expanded quantum associative memory model that significantly improves capacity and incorporates a tunable accuracy mechanism, bridging quantum computing and brain-inspired memory.

Findings

Exponential increase in memory capacity over classical models

Retrieval probability peaks for patterns close in Hamming distance

Adjustable parameter allows tuning of recall accuracy

Abstract

I review and expand the model of quantum associative memory that I have recently proposed. In this model binary patterns of n bits are stored in the quantum superposition of the appropriate subset of the computational basis of n qbits. Information can be retrieved by performing an input-dependent rotation of the memory quantum state within this subset and measuring the resulting state. The amplitudes of this rotated memory state are peaked on those stored patterns which are closest in Hamming distance to the input, resulting in a high probability of measuring a memory pattern very similar to it. The accuracy of pattern recall can be tuned by adjusting a parameter playing the role of an effective temperature. This model solves the well-known capacity shortage problem of classical associative memories, providing an exponential improvement in capacity. The price to pay is the probabilistic nature of information retrieval, a feature that, however, this model shares with our own brain.