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

Abstract

The embodiment of the invention discloses a data search method, device and equipment based on an analog quantum algorithm, and belongs to the technical field of computers. The method comprises the following steps: obtaining a plurality of candidate data to be searched, creating a first search path, obtaining a first processing function corresponding to the first search path, traversing at least two groups of numerical value sets of the first search path, and based on the analog quantum algorithm, obtaining an output value of the first processing function, under the condition that the output value of the first processing function is converged, taking a plurality of numerical values in the numerical value set corresponding to the converged output value as values of a plurality of coefficients respectively to obtain a second search path, and searching the target data by adopting the second search path. By traversing the at least two groups of numerical value sets and obtaining the outputvalues corresponding to the traversed numerical value sets, the frequency of traversing is low, accurate values of multiple coefficients can be quickly obtained, so that the efficiency of obtaining the second search path is improved, and the target data can be quickly searched by adopting the second search path.

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 a second search path. However, the reinforcement learning model finally determines an optional value by tr...

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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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