Quantum optimization algorithms: Energetic implications Article Swipe
Rolando P. Hong Enriquez
,
Rosa M. Badía
,
Barbara Chapman
,
Kirk Bresniker
,
Scott Pakin
,
Alok Mishra
,
Pedro Bruel
,
Aditya Dhakal
,
Gourav Rattihalli
,
Ninad Hogade
,
Eitan Frachtenberg
,
Dejan Milojičić
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/cpe.8121
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/cpe.8121
Summary Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. In this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least to more energy efficient than their classical counterparts; why feedback‐based quantum optimization is energy‐inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of 20. Finally, we use the NchooseK high‐level programming model to run optimization problems on both gate‐based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate‐based counterparts.
Related Topics
Concepts
Computer science
Qubit
Quantum computer
Quantum algorithm
Quantum
Stochastic gradient descent
Quantum circuit
Quantum gate
Quantum annealing
Algorithm
Quantum machine learning
Mathematical optimization
Computer engineering
Quantum error correction
Artificial intelligence
Mathematics
Physics
Quantum mechanics
Artificial neural network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/cpe.8121
- OA Status
- green
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4395048119
All OpenAlex metadata
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https://openalex.org/W4395048119Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/cpe.8121Digital Object Identifier
- Title
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Quantum optimization algorithms: Energetic implicationsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-04-22Full publication date if available
- Authors
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Rolando P. Hong Enriquez, Rosa M. Badía, Barbara Chapman, Kirk Bresniker, Scott Pakin, Alok Mishra, Pedro Bruel, Aditya Dhakal, Gourav Rattihalli, Ninad Hogade, Eitan Frachtenberg, Dejan MilojičićList of authors in order
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https://doi.org/10.1002/cpe.8121Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.osti.gov/servlets/purl/2367506Direct OA link when available
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Computer science, Qubit, Quantum computer, Quantum algorithm, Quantum, Stochastic gradient descent, Quantum circuit, Quantum gate, Quantum annealing, Algorithm, Quantum machine learning, Mathematical optimization, Computer engineering, Quantum error correction, Artificial intelligence, Mathematics, Physics, Quantum mechanics, Artificial neural networkTop concepts (fields/topics) attached by OpenAlex
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