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The variational quantum algorithm (VQA) is a hybrid classical–quantum algorithm. It can actually run in an intermediate-scale quantum device where the number of available qubits is too limited to perform quantum error correction, so it is one of the most promising quantum algorithms in the noisy intermediate-scale quantum era. In this paper, two ideas for solving the learning with errors problem (LWE) using VQA are proposed. First, after reducing the LWE problem into the bounded distance decoding problem, the quantum approximation optimization algorithm (QAOA) is introduced to improve classical methods. Second, after the LWE problem is reduced into the unique shortest vector problem, the variational quantum eigensolver (VQE) is used to solve it, and the number of qubits required is calculated in detail. Small-scale experiments are carried out for the two LWE variational quantum algorithms, and the experiments show that VQA improves the quality of the classical solutions. [ABSTRACT FROM AUTHOR]

Solving algebraic equations over GF(2) is a problem which has a wide range of applications, including NP-Hard problems and problems related to cryptography. The existing mature algorithms are difficult to solve large-scale problems. Inspired by Schöning's algorithm and its quantum version, we apply related methods to solve algebraic equations over GF (2). The new algorithm we proposed has a significant improvement of solving efficiency in large-scale and sparse algebraic equations. As a hybrid algorithm, the new algorithm can not only run on a classic computer alone, but also use small-scale quantum devices to assist acceleration. And the new algorithm can be seen as an example of solving a large-scale problem on a small-scale quantum device. [ABSTRACT FROM AUTHOR]