Source-linked AI summary
Probabilistic Quantum Memories
Carlo A. Trugenberger
TL;DR
Address-oriented memories require precise addresses, while associative memories handle noisy or incomplete inputs but have linear capacity limits. This paper proposes an entanglement-based quantum memory with optimal exponential capacity and probabilistic retrieval that favors stored patterns nearest to the input in Hamming distance.
Problem
Address-oriented memories cannot retrieve incomplete or noisy inputs, whereas associative memories support such retrieval but are limited to linear storage capacity.
Source: p. 1Method
The paper uses quantum entanglement to store binary patterns and a probabilistic, postselected retrieval process for noisy or incomplete inputs.
Source: pp. 1, 4Results
The memory stores up to pmax = 2^n patterns, while retrieval probabilities peak around stored patterns with the smallest Hamming distance to the input.
Source: pp. 1, 3Takeaways & Limitations
Recognition is most efficient for very large, homogeneous pattern sets, whereas identification is best for isolated patterns far from the others.
Source: p. 4Incomplete-input retrieval can avoid random noise by restricting Hamming-distance operations to known qbits, but this requires adjusting the memory operations to each input.
Source: p. 4
Abstract
from arXiv · showhide
Typical address-oriented computer memories cannot recognize incomplete or noisy information. Associative (content-addressable) memories solve this problem but suffer from severe capacity shortages. I propose a model of a quantum memory that solves both problems. The storage capacity is exponential in the number of qbits and thus optimal. The retrieval mechanism for incomplete or noisy inputs is probabilistic, with postselection of the measurement result. The output is determined by a probability distribution on the memory which is peaked around the stored patterns closest in Hamming distance to the input.