Multi-target quantum compilation algorithm

Abstract Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum state preparation, and quantum dynamic simulation. Traditional quantum compilation usually optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets, such as thermal state preparation, time-dependent dynamic simulation, and others. To address this, we develop a multi-target quantum compilation algorithm to improve the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the effectiveness of the algorithm, highlighting the importance of multi-target optimization in advancing quantum computing. This work lays the groundwork for further development and evaluation of multi-target quantum compilation algorithms.

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Publication Details

Journal
Machine Learning Science and Technology
Published
2024-11-25
DOI
https://doi.org/10.1088/2632-2153/ad9705
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.00

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article

Multi-target quantum compilation algorithm

Thu-Uyen Nguyen, Van-Nhan Nguyen, Lan Nguyen Tran, Vu Tuan Hai et al.
Machine Learning Science and Technology
Quantum Computing Algorithms and Architecture
article

Multi-target quantum compilation algorithm

Thu-Uyen Nguyen, Van-Nhan Nguyen, Lan Nguyen Tran, Vu Tuan Hai, Le Bin Ho, Jesus Urbaneja
article en

Abstract

Abstract Quantum compilation is the process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. It has a wide range of applications, including gate optimization, quantum-assisted compiling, quantum state preparation, and quantum dynamic simulation. Traditional quantum compilation usually optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets, such as thermal state preparation, time-dependent dynamic simulation, and others. To address this, we develop a multi-target quantum compilation algorithm to improve the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the effectiveness of the algorithm, highlighting the importance of multi-target optimization in advancing quantum computing. This work lays the groundwork for further development and evaluation of multi-target quantum compilation algorithms.

Machine Learning Science and TechnologyVol. 5(4)
FPT University (VN), Vietnam National University Ho Chi Minh City (VN), Tohoku University (JP), Nara Institute of Science and Technology (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 81%
Quantum Computing Algorithms and Architecture
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