Quantum Pattern Recognition

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.

Invited Talk at the 1st Feynman Festival, Univ. of Maryland, College Park, August 2002

Cited by in corpus (9)

• Quantum perceptron over a field and neural network architecture selection in a quantum computer
• Benchmarking neural networks for quantum computation
• Circuit-based quantum random access memory for classical data with continuous amplitudes
• Pattern recognition on the quantum Bloch sphere
• Weightless neural network parameters and architecture selection in a quantum computer
• Quantum version of the k-NN classifier based on a quantum sorting algorithm
• Quantum Machine Learning and its Supremacy in High Energy Physics
• Quantum-wave pattern recognition: From simulations towards implementation
• Quantum computation and the physical computation level of biological information processing