Parametric Synthesis of Computational Circuits for Complex Quantum Algorithms in the Transport and Road Sphere

C. B. Pronin, A. Ostroukh
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引用次数: 2

Abstract

Since quantum computing technologies are still in their early development phase, quantum circuits are created mainly by manual placement of logic elements. This development approach has the drawback of becoming inefficient due to lack of human comprehension when analyzing large circuits that correspond to complex algorithms. Because, even a slight increase in the number of operations in a quantum algorithm, could lead to the significant increase in size of its corresponding quantum circuit. Therefore, the purpose of creating Quantum Circuit Synthesizer "Naginata" is to improve the development and debugging processes of quantum circuits by introducing dynamically scalable compositions for common operations such as: the adder, multiplier, digital comparator (comparison operator), etc., turning them into building blocks for quantum programs, as well as providing a stable platform for creating more of these compositions. This way, our quantum synthesizer is opening an opportunity to implement quantum algorithms using higher-level commands. The programmer could implement a quantum algorithm with these generic blocks, and the quantum synthesizer would create a suitable circuit for this algorithm, in a format that is supported by the chosen quantum computation environment. With the help of simple command logging and using coding for building quantum circuits, this approach has the potential to significantly simplify and enhance the development and debugging processes of quantum circuits. The proposed approach for implementing quantum algorithms could have a potential application in the field of machine learning, in this regard, we provided an example of creating a circuit for training a simple neural network. Neural networks have a significant impact on the technological development of the transport and road complex, and there is a potential for improving the reliability and efficiency of their learning process by utilizing quantum computation.
交通和道路领域复杂量子算法计算电路的参数综合
由于量子计算技术仍处于早期发展阶段,量子电路主要是通过人工放置逻辑元件来创建的。这种开发方法的缺点是,在分析与复杂算法相对应的大型电路时,由于缺乏人类的理解而变得效率低下。因为,即使量子算法中运算次数的轻微增加,也可能导致其相应量子电路尺寸的显着增加。因此,创建量子电路合成器“Naginata”的目的是通过引入加法器、乘法器、数字比较器(比较算子)等常见操作的动态可扩展组合,改善量子电路的开发和调试过程,将其变成量子程序的构建块,并为创建更多这些组合提供稳定的平台。这样,我们的量子合成器就有机会使用更高级别的命令来实现量子算法。程序员可以用这些通用块实现量子算法,量子合成器将以所选量子计算环境支持的格式为该算法创建合适的电路。借助简单的命令记录和使用编码来构建量子电路,这种方法有可能大大简化和增强量子电路的开发和调试过程。所提出的实现量子算法的方法可能在机器学习领域有潜在的应用,在这方面,我们提供了一个创建用于训练简单神经网络的电路的示例。神经网络对交通和道路综合体的技术发展产生了重大影响,并且利用量子计算有可能提高其学习过程的可靠性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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