Details
| Original language | English |
|---|---|
| Article number | 062615 |
| Number of pages | 10 |
| Journal | Physical Review A |
| Volume | 108 |
| Issue number | 6 |
| Publication status | Published - 19 Dec 2023 |
Abstract
The building blocks of quantum algorithms and software are quantum gates, with the appropriate combination of quantum gates leading to a desired quantum circuit. Deep expert knowledge is necessary to discover effective combinations of quantum gates to achieve a desired quantum algorithm for solving a specific task. This is especially challenging for quantum machine learning and signal processing. For example, it is not trivial to design a quantum Fourier transform from scratch. This work proposes a quantum architecture search algorithm which is based on a Monte Carlo graph search and measures of importance sampling. It is applicable to the optimization of gate order for both discrete gates and gates containing continuous variables. Several numerical experiments demonstrate the applicability of the proposed method for the automatic discovery of quantum circuits.
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
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In: Physical Review A, Vol. 108, No. 6, 062615, 19.12.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Monte Carlo graph search for quantum circuit optimization
AU - Rosenhahn, Bodo
AU - Osborne, Tobias J.
N1 - Funding Information: This work was supported, in part, by Quantum Valley Lower Saxony and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project No. 274200144, SFB1227, under Germany's Excellence Strategies EXC-2123 QuantumFrontiers and EXC-2122 PhoenixD.
PY - 2023/12/19
Y1 - 2023/12/19
N2 - The building blocks of quantum algorithms and software are quantum gates, with the appropriate combination of quantum gates leading to a desired quantum circuit. Deep expert knowledge is necessary to discover effective combinations of quantum gates to achieve a desired quantum algorithm for solving a specific task. This is especially challenging for quantum machine learning and signal processing. For example, it is not trivial to design a quantum Fourier transform from scratch. This work proposes a quantum architecture search algorithm which is based on a Monte Carlo graph search and measures of importance sampling. It is applicable to the optimization of gate order for both discrete gates and gates containing continuous variables. Several numerical experiments demonstrate the applicability of the proposed method for the automatic discovery of quantum circuits.
AB - The building blocks of quantum algorithms and software are quantum gates, with the appropriate combination of quantum gates leading to a desired quantum circuit. Deep expert knowledge is necessary to discover effective combinations of quantum gates to achieve a desired quantum algorithm for solving a specific task. This is especially challenging for quantum machine learning and signal processing. For example, it is not trivial to design a quantum Fourier transform from scratch. This work proposes a quantum architecture search algorithm which is based on a Monte Carlo graph search and measures of importance sampling. It is applicable to the optimization of gate order for both discrete gates and gates containing continuous variables. Several numerical experiments demonstrate the applicability of the proposed method for the automatic discovery of quantum circuits.
UR - http://www.scopus.com/inward/record.url?scp=85181084902&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2307.07353
DO - 10.48550/arXiv.2307.07353
M3 - Article
AN - SCOPUS:85181084902
VL - 108
JO - Physical Review A
JF - Physical Review A
SN - 2469-9926
IS - 6
M1 - 062615
ER -