145 research outputs found
Macroscopic quantum information processing using spin coherent states
Previously a new scheme of quantum information processing based on spin
coherent states of two component Bose-Einstein condensates was proposed (Byrnes
{\it et al.} Phys. Rev. A 85, 40306(R)). In this paper we give a more detailed
exposition of the scheme, expanding on several aspects that were not discussed
in full previously. The basic concept of the scheme is that spin coherent
states are used instead of qubits to encode qubit information, and manipulated
using collective spin operators. The scheme goes beyond the continuous variable
regime such that the full space of the Bloch sphere is used. We construct a
general framework for quantum algorithms to be executed using multiple spin
coherent states, which are individually controlled. We illustrate the scheme by
applications to quantum information protocols, and discuss possible
experimental implementations. Decoherence effects are analyzed under both
general conditions and for the experimental implementation proposed.Comment: published in Optics Communication August 201
Hybrid quantum-classical unsupervised data clustering based on the Self-Organizing Feature Map
Unsupervised machine learning is one of the main techniques employed in
artificial intelligence. Quantum computers offer opportunities to speed up such
machine learning techniques. Here, we introduce an algorithm for quantum
assisted unsupervised data clustering using the self-organizing feature map, a
type of artificial neural network. We make a proof-of-concept realization of
one of the central components on the IBM Q Experience and show that it allows
us to reduce the number of calculations in a number of clusters. We compare the
results with the classical algorithm on a toy example of unsupervised text
clustering
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