Data search method, device and equipment based on simulated quantum algorithm

Abstract

The embodiment of the present application discloses a data search method, device and equipment based on an analog quantum algorithm, belonging to the field of computer technology. The method includes: creating a first search path according to multiple candidate data to be searched, obtaining a first processing function corresponding to the first search path, traversing at least two sets of numerical values ​​of the first search path, and obtaining The output value of the first processing function, when the output value of the first processing function converges, using a plurality of values ​​in the value set corresponding to the output value of the convergence as the values ​​of the plurality of coefficients to obtain the second search path, The target data is searched for using the second search path. By traversing at least two sets of value sets, the output value corresponding to the traversal value set is obtained, and the number of traversals is small, and the accurate values ​​of multiple coefficients can be quickly obtained, thereby improving the efficiency of obtaining the second search path, using the second search path The target data can be quickly searched.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technology, and in particular to a data search method, device and equipment based on an analog quantum algorithm. Background technique [0002] With the development of quantum computing technology, there are more and more applications of quantum computing. Compared with classical computing, quantum computing has greatly improved the efficiency of data processing. Adiabatic quantum computing in quantum computing uses a search path to process data, and an accurate search path can improve the efficiency of adiabatic quantum computing. [0003] In related technologies, the first search path includes a plurality of coefficients with undetermined values, and the values ​​of the plurality of coefficients are respectively determined through a reinforcement learning model to obtain the second search path. However, the reinforcement learning model finally determines an optional value by ...

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Problems solved by technology

However, the reinforcement learning model finally determines an optional value by trying multiple optional values ​​for each coefficient. In the process of obtaining the second search path, if each coefficient whose value is not determined corresponds to a large number of Optional value, the reinforcement learning model needs to try multiple times, resulting in low efficiency in determining the value, resulting in low efficiency in obtaining the second search path

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