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<p>The process of translating a quantum algorithm into a form suitable for implementation on a quantum computing platform is crucial but yet challenging. This entails specifying quantum operations with precision, a typically intricate task. In this paper, we present an alternative approach: an automated method for synthesising the functionality of a quantum algorithm into a quantum circuit model representation. Our methodology involves training a neural network model using diverse input–output mappings of the quantum algorithm. We demonstrate that this trained model can effectively generate a quantum circuit model equivalent to the original algorithm. Remarkably, our observations indicate that the trained model achieves an almost perfect mapping of unseen inputs to their corresponding outputs, with test accuracies ranging from 98.27% in the worst case to 99.99% in the best case.</p>

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Optimal quantum circuit design via unitary neural networks

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • M. Zomorodi,
  • H. Amini,
  • M. Abbaszadeh,
  • J. Sohrabi,
  • V. Salari,
  • P. Plawiak

摘要

The process of translating a quantum algorithm into a form suitable for implementation on a quantum computing platform is crucial but yet challenging. This entails specifying quantum operations with precision, a typically intricate task. In this paper, we present an alternative approach: an automated method for synthesising the functionality of a quantum algorithm into a quantum circuit model representation. Our methodology involves training a neural network model using diverse input–output mappings of the quantum algorithm. We demonstrate that this trained model can effectively generate a quantum circuit model equivalent to the original algorithm. Remarkably, our observations indicate that the trained model achieves an almost perfect mapping of unseen inputs to their corresponding outputs, with test accuracies ranging from 98.27% in the worst case to 99.99% in the best case.