<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si26.svg" display="inline" id="d1e1792"><mml:mrow><mml:mo>〈</mml:mo><mml:mi mathvariant="normal">qo</mml:mi><mml:mo>|</mml:mo><mml:mi mathvariant="normal">op</mml:mi><mml:mo>〉</mml:mo></mml:mrow></mml:math>: A quantum object optimizer

Hai, Vu Tuan; Viet, Nguyen Tan; Ho, Le Bin

doi:10.1016/j.softx.2024.101726

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Variational quantum algorithms (VQAs) like quantum approximate optimization algorithm (QAOA), variational quantum eigensolver (VQE), quantum neural networks (QNN), and quantum compilation (QC) are promising for solving practical tasks on noisy intermediate scale quantum (NISQ) devices beyond classical computers 1 . Recent achievements demonstrate effectiveness in quantum state preparation [2][3][4][5][6] , quantum dynamic simulation 2, 7-9 , and quantum metrology [10][11][12][13][14] . QC, in particular, has gained significant interest.…”

Section: Introductionmentioning

confidence: 99%

“…This method has various applications, including gates optimization 15 , quantum-assisted compiling 16 , continuous-variable quantum learning 17 , quantum state tomography 18 , and quantum objects simulation 2 . For instance, a quantum object such as a quantum state can be prepared and its evolution can be simulated in a quantum circuit by using QC 2 .…”

Section: Introductionmentioning

confidence: 99%

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Multi-target quantum compilation algorithm

Binho,

Hai,

Viet

et al. 2024

Preprint

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In quantum computing, quantum compilation transforms a target unitary into a trainable unitary represented by a quantum circuit. Traditional quantum compilation optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets. To address this, we developed a multi-target quantum compilation algorithm to enhance the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the algorithm’s effectiveness, highlighting the significance of multi-target optimization in advancing quantum computing. This work establishes the foundation for further development and evaluation of multi-target quantum compilation algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-target quantum compilation algorithm

Binho,

Hai,

Viet

et al. 2024

Preprint

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Multi-target quantum compilation algorithm

Tuan Hai,

Tan Viet,

Urbaneja

et al. 2024

Mach. Learn.: Sci. Technol.

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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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