38,630 research outputs found
Singlet state creation and Universal quantum computation in NMR using Genetic Algorithm
Experimental implementation of a quantum algorithm requires unitary operator
decomposition. Here we treat the unitary operator decomposition as an
optimization problem and use Genetic Algorithm, a global optimization method
inspired by nature's evolutionary process for operator decomposition. As an
application, we apply this to NMR Quantum Information Processing and find a
probabilistic way of doing universal quantum computation using global hard
pulses. We also demonstrate efficient creation of singlet state (as a special
case of Bell state) directly from thermal equilibrium using an optimum sequence
of pulses
Quantum Hopfield neural network
Quantum computing allows for the potential of significant advancements in
both the speed and the capacity of widely used machine learning techniques.
Here we employ quantum algorithms for the Hopfield network, which can be used
for pattern recognition, reconstruction, and optimization as a realization of a
content-addressable memory system. We show that an exponentially large network
can be stored in a polynomial number of quantum bits by encoding the network
into the amplitudes of quantum states. By introducing a classical technique for
operating the Hopfield network, we can leverage quantum algorithms to obtain a
quantum computational complexity that is logarithmic in the dimension of the
data. We also present an application of our method as a genetic sequence
recognizer.Comment: 13 pages, 3 figures, final versio
Quantum vs classical genetic algorithms: A numerical comparison shows faster convergence
Genetic algorithms are heuristic optimization techniques inspired by
Darwinian evolution. Quantum computation is a new computational paradigm which
exploits quantum resources to speed up information processing tasks. Therefore,
it is sensible to explore the potential enhancement in the performance of
genetic algorithms by introducing quantum degrees of freedom. Along this line,
a modular quantum genetic algorithm has recently been proposed, with
individuals encoded in independent registers comprising exchangeable quantum
subroutines [arXiv:2203.15039], which leads to different variants. Here, we
perform a numerical comparison among quantum and classical genetic algorithms,
which was missed in previous literature. In order to isolate the effect of the
quantum resources in the performance, the classical variants have been selected
to resemble the fundamental characteristics of the quantum genetic algorithms.
Under these conditions, we encode an optimization problem in a two-qubit
Hamiltonian and face the problem of finding its ground state. A numerical
analysis based on a sample of 200 random cases shows that some quantum variants
outperform all classical ones in convergence speed towards a near-to-optimal
result. Additionally, we have considered a diagonal Hamiltonian and the
Hamiltonian of the hydrogen molecule to complete the analysis with two relevant
use-cases. If this advantage holds for larger systems, quantum genetic
algorithms would provide a new tool to address optimization problems with
quantum computers.Comment: 7 pages, 4 figures, submitted to the IEEE Symposium Series On
Computational Intelligence 202
Evolutionary Approaches to Optimization Problems in Chimera Topologies
Chimera graphs define the topology of one of the first commercially available
quantum computers. A variety of optimization problems have been mapped to this
topology to evaluate the behavior of quantum enhanced optimization heuristics
in relation to other optimizers, being able to efficiently solve problems
classically to use them as benchmarks for quantum machines. In this paper we
investigate for the first time the use of Evolutionary Algorithms (EAs) on
Ising spin glass instances defined on the Chimera topology. Three genetic
algorithms (GAs) and three estimation of distribution algorithms (EDAs) are
evaluated over hard instances of the Ising spin glass constructed from
Sidon sets. We focus on determining whether the information about the topology
of the graph can be used to improve the results of EAs and on identifying the
characteristics of the Ising instances that influence the success rate of GAs
and EDAs.Comment: 8 pages, 5 figures, 3 table
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