High-Capacity Quantum Associative Memories

TL;DR

This paper reviews quantum associative memory models that use reversible quantum operations to store and retrieve patterns with high capacity, overcoming classical limitations through probabilistic methods and quantum gates.

Contribution

It introduces quantum models of associative memories that significantly improve storage capacity using probabilistic quantum techniques and pattern-dependent quantum gates.

Findings

Probabilistic quantum memories eliminate crosstalk and spurious memories.

The models achieve polynomial capacity improvement over classical memories.

Retrieval accuracy can be tuned by adjusting a quantum temperature parameter.

Abstract

We review our models of quantum associative memories that represent the "quantization" of fully coupled neural networks like the Hopfield model. The idea is to replace the classical irreversible attractor dynamics driven by an Ising model with pattern-dependent weights by the reversible rotation of an input quantum state onto an output quantum state consisting of a linear superposition with probability amplitudes peaked on the stored pattern closest to the input in Hamming distance, resulting in a high probability of measuring a memory pattern very similar to the input. The unitary operator implementing this transformation can be formulated as a sequence of one-qubit and two-qubit elementary quantum gates and is thus the exponential of an ordered quantum Ising model with sequential operations and with pattern-dependent interactions, exactly as in the classical case. Probabilistic quantum memories, that make use of postselection of the measurement result of control qubits, overcome the famed linear storage limitation of their classical counterparts because they permit to completely {\it eliminate crosstalk and spurious memories}. The number of control qubits plays the role of an inverse fictitious temperature, the accuracy of pattern retrieval can be tuned by lowering the fictitious temperature under a critical value for quantum content association while the complexity of the retrieval algorithm remains polynomial for any number of patterns polynomial in the number of qubits. These models solve thus the capacity shortage problem of classical associative memories, providing a {\it polynomial improvement} in capacity. The price to pay is the probabilistic nature of information retrieval.