Authors:
Wael Korani
and
Malek Mouhoub
Affiliation:
Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada
Keyword(s):
Mother Tree Optimization, Continuous Optimization, Quantum Computing, Artificial Intelligence.
Abstract:
Quantum Control Problem (QCP) for Error Correction (EC) is a significant issue that helps in producing an
efficient quantum computer. The QCP for EC can be tackled using Stochastic Local Search (SLS) methods.
However, these techniques might produce low quality results for large dimensional quantum systems. Lately,
Nature-Inspired (NI) algorithms including different variants of Particle Swarm Optimization (PSO) and Deferential Evolution (DE) were implemented in several studies to tackle the QCP for EC, but the results were
not promising. In this paper, we propose a quantum model that is built on our NI algorithm, called Mother
Tree Optimization for QCP (MTO-QCP), to overcome the stagnation issue that the other methods suffer from.
In order to assess the performance of MTO-QCP, we conducted several preliminary experiments to adjust our
MTO parameters. In this regard, our MTO-QCP achieves high-fidelity (> 99.99%) for a Single-Shot (SS)
three-qubit gate control at gate operation
time of 26 ns. This recommended fidelity is an acceptable threshold
fidelity for fault-tolerant Quantum Computing (QC) problems
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