Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{Rindell_2023,
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author = "Tom Rindell and Berat Yenilen and Niklas Halonen and
Arttu Ponni and Ilkka Tittonen and Matti Raasakka",
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title = "Exploring the optimality of approximate state
preparation quantum circuits with a genetic algorithm",
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journal = "Physics Letters A",
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year = "2023",
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volume = "475",
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pages = "128860",
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month = "5 " # jul,
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keywords = "genetic algorithms, genetic programming, Quantum state
preparation, Genetic algorithm, Quantum circuit
complexity, Noisy intermediate-scale quantum, NISQ",
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publisher = "Elsevier BV",
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ISSN = "0375-9601",
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URL = "https://arxiv.org/abs/2210.06411",
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URL = "https://acris.aalto.fi/ws/portalfiles/portal/107926323/Rindell_Exploring_optimality_PhysLetA.pdf",
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URL = "https://www.sciencedirect.com/science/article/pii/S0375960123002402",
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DOI = "doi:10.1016/j.physleta.2023.128860",
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code_url = "https://github.com/beratyenilen/qc-ga",
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size = "10 pages",
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abstract = "We study the approximate state preparation problem on
noisy intermediate-scale quantum (NISQ) computers by
applying a genetic algorithm to generate quantum
circuits for state preparation. The algorithm can
account for the specific characteristics of the
physical machine in the evaluation of circuits, such as
the native gate set and qubit connectivity. We use our
genetic algorithm to optimize the circuits provided by
the low-rank state preparation algorithm introduced by
Araujo et al., and find substantial improvements to the
fidelity in preparing Haar random states with a limited
number of CNOT gates. Moreover, we observe that already
for a 5-qubit quantum processor with limited qubit
connectivity and significant noise levels (IBM Falcon
5T), the maximal fidelity for Haar random states is
achieved by a short approximate state preparation
circuit instead of the exact preparation circuit. We
also present a theoretical analysis of approximate
state preparation circuit complexity to motivate our
findings. Our genetic algorithm for quantum circuit
discovery is freely available at
https://github.com/beratyenilen/qc-ga",
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notes = "Also known as \cite{RINDELL2023128860}
Micro and Quantum Systems group,Department of
Electronics and Nanoengineering,Aalto University,
Finland",
- }
Genetic Programming entries for
Tom Rindell
Berat Yenilen
Niklas Halonen
Arttu Ponni
Ilkka Tittonen
Matti Raasakka
Citations