Quantum optimization algorithms: Energetic implications
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 noise...
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| Published in: | Concurrency and computation Vol. 36; no. 16 |
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25.07.2024
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| Abstract | 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 2×$$ 2\times $$ to 4×$$ 4\times $$ 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 ≥$$ \ge $$20×$$ \times $$. 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. |
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| AbstractList | 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 2×$$ 2\times $$ to 4×$$ 4\times $$ 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 ≥$$ \ge $$20×$$ \times $$. 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. 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 2×$$ 2\times $$ to 4×$$ 4\times $$ 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 ≥$$ \ge $$20×$$ \times $$. 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. 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. 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. |
| Author | Bresniker, Kirk Milojicic, Dejan Pakin, Scott Chapman, Barbara Hogade, Ninad Dhakal, Aditya Rattihalli, Gourav Mishra, Alok Bruel, Pedro Hong Enriquez, Rolando P. Badia, Rosa M. Frachtenberg, Eitan |
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| Cites_doi | 10.1103/PhysRevA.76.042319 10.1080/00107514.2019.1667078 10.1103/PhysRevLett.129.250502 10.22331/q‐2020‐05‐11‐263 10.1145/3592622 10.1109/9.119632 10.1038/d41586‐020‐03237‐w 10.1557/mrs.2013.20 10.1201/9781003358817-7 10.1063/5.0082975 10.22331/q‐2021‐10‐20‐567 10.1063/PT.3.4499 10.22331/q‐2022‐05‐09‐707 10.1103/PhysRevResearch.4.023017 10.1103/PhysRevA.101.032308 10.1155/2013/967529 10.22331/q‐2019‐12‐09‐214 10.1103/PhysRevA.99.032331 10.22331/q‐2020‐05‐25‐269 10.1088/2058‐9565/ab4eb5 10.1088/1751‐8113/47/26/265206 10.1109/QCS54837.2021.00016 10.1007/978-3-540-78800-3_24 10.1109/QCE49297.2020.00045 10.22331/q‐2022‐03‐30‐677 10.22331/q‐2021‐01‐28‐391 10.22331/q‐2018‐08‐06‐79 10.1016/S0301‐0104(01)00318‐4 10.1088/2058‐9565/acae3e 10.22331/q‐2024‐03‐07‐1275 10.1214/aoms/1177729392 10.1162/089976698300017746 10.22331/q‐2021‐02‐04‐392 10.1038/s41563‐022‐01304‐3 10.1088/2058‐9565/aab822 10.1109/TSUSC.2022.3190242 |
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Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional... Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing.... |
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| SubjectTerms | Algorithms Annealing circuit learning Cognitive tasks Energy consumption Energy requirements error mitigation heterogeneous computing Machine learning Optimization Optimization algorithms quantum annealing Quantum computers Quantum computing quantum optimization Qubits (quantum computing) shot optimization sustainability |
| Title | Quantum optimization algorithms: Energetic implications |
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