Quantum Pattern Recognition
Carlo A. Trugenberger
Received November 4, 2002; accepted January 25, 2003
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 a large improvement in capacity.
KEY WORDS: Quantum search algorithms; associative memories.
The power of quantum computation
is mostly associated with the speed-
up in computing time it can provide with respect to its classical counterpart,
the paramount examples being Shor’s factoring algorithm
There is, however, another aspect of quantum
computation which represents a big improvement upon its classical
This leads to a large increase in a particular memory
capacity rather than speed. In this paper I will review and expand the main
aspects of this new application of quantum information theory. Further
aspects of it can be found in Ref. 5.
In traditional computers the storage of information requires setting up
a lookup table (RAM). The main disadvantage of this address-oriented
InfoCodex S.A., av. Louis-Casai 18, CH-1209 Geneva, Switzerland, Theory Division, CERN,
CH-1211 Geneva 23, Switzerland. E-mail: ca.trugenberger@InfoCodex.com
Quantum Information Processing, Vol. 1, No. 6, December 2002 (# 2003)
1570-0755/02/1200–0471/0 # 2003 Plenum Publishing Corporation