dorsal/arxiv
View SchemaQuantum Pattern Recognition
| Authors | Carlo A. Trugenberger |
|---|---|
| Categories | |
| ArXiv ID | quant-ph/0210176 |
| URL | https://arxiv.org/abs/quant-ph/0210176 |
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.
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"abstract": "I review and expand the model of quantum associative memory that I have\nrecently proposed. In this model binary patterns of n bits are stored in the\nquantum superposition of the appropriate subset of the computational basis of n\nqbits. Information can be retrieved by performing an input-dependent rotation\nof the memory quantum state within this subset and measuring the resulting\nstate. The amplitudes of this rotated memory state are peaked on those stored\npatterns which are closest in Hamming distance to the input, resulting in a\nhigh probability of measuring a memory pattern very similar to it. The accuracy\nof pattern recall can be tuned by adjusting a parameter playing the role of an\neffective temperature. This model solves the well-known capacity shortage\nproblem of classical associative memories, providing an exponential improvement\nin capacity. The price to pay is the probabilistic nature of information\nretrieval, a feature that, however, this model shares with our own brain.",
"arxiv_id": "quant-ph/0210176",
"authors": [
"Carlo A. Trugenberger"
],
"categories": [
"quant-ph",
"cond-mat.dis-nn",
"cs.IR",
"nlin.AO",
"q-bio.NC"
],
"title": "Quantum Pattern Recognition",
"url": "https://arxiv.org/abs/quant-ph/0210176"
},
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"source": {
"execution_id": "385999b6-7bde-44cd-ae26-0c1278b3e513",
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"type": "Model",
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