Circuit depth scaling for quantum approximate optimization Article Swipe
V. Akshay
,
H. Philathong
,
Ernesto Campos
,
Daniil Rabinovich
,
I. Zacharov
,
Xiao‐Ming Zhang
,
Jacob Biamonte
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1103/physreva.106.042438
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1103/physreva.106.042438
Variational quantum algorithms are the centerpiece of modern quantum programming. These algorithms involve training parameterized quantum circuits using a classical co-processor, an approach adapted partly from classical machine learning. An important subclass of these algorithms, designed for combinatorial optimization on currrent quantum hardware, is the quantum approximate optimization algorithm (QAOA). It is known that problem density - a problem constraint to variable ratio - induces under-parametrization in fixed depth QAOA. Density dependent performance has been reported in the literature, yet the circuit depth required to achieve fixed performance (henceforth called critical depth) remained unknown. Here, we propose a predictive model, based on a logistic saturation conjecture for critical depth scaling with respect to density. Focusing on random instances of MAX-2-SAT, we test our predictive model against simulated data with up to 15 qubits. We report the average critical depth, required to attain a success probability of 0.7, saturates at a value of 10 for densities beyond 4. We observe the predictive model to describe the simulated data within a $3\sigma$ confidence interval. Furthermore, based on the model, a linear trend for the critical depth with respect problem size is recovered for the range of 5 to 15 qubits.
Related Topics
Concepts
Qubit
Computer science
Quantum
Scaling
Quantum circuit
Algorithm
Quantum algorithm
Quantum computer
Mathematical optimization
Applied mathematics
Mathematics
Quantum error correction
Quantum mechanics
Physics
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1103/physreva.106.042438
- OA Status
- bronze
- Cited By
- 20
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307381367
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4307381367Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1103/physreva.106.042438Digital Object Identifier
- Title
-
Circuit depth scaling for quantum approximate optimizationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-25Full publication date if available
- Authors
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V. Akshay, H. Philathong, Ernesto Campos, Daniil Rabinovich, I. Zacharov, Xiao‐Ming Zhang, Jacob BiamonteList of authors in order
- Landing page
-
https://doi.org/10.1103/physreva.106.042438Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://scholars.cityu.edu.hk/en/publications/circuit-depth-scaling-for-quantum-approximate-optimizationDirect OA link when available
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Qubit, Computer science, Quantum, Scaling, Quantum circuit, Algorithm, Quantum algorithm, Quantum computer, Mathematical optimization, Applied mathematics, Mathematics, Quantum error correction, Quantum mechanics, Physics, GeometryTop concepts (fields/topics) attached by OpenAlex
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20Total citation count in OpenAlex
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2025: 7, 2024: 5, 2023: 5, 2022: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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