Machine Learning Approaches to Building Quantum Circuits for Sets of Matrices
TLDR
This paper uses interpretable machine learning to create a universal, shortest analytic quantum algorithm for arbitrary diagonal matrices of any size.
Key contributions
- Applies interpretable machine learning to design quantum algorithms.
- Constructs a universal, shortest analytic quantum algorithm.
- Specifically targets arbitrary diagonal matrices of any size.
Why it matters
This paper introduces a novel method using interpretable machine learning to automatically design quantum algorithms. It's significant for providing a universal and shortest analytic quantum algorithm for arbitrary diagonal matrices, a key step towards more efficient quantum circuit development. This approach could accelerate the creation of practical quantum algorithms.
Original Abstract
Machine learning nowadays becomes a useful instrument in many subjects. In this paper we use interpretable machine learning to build quantum algorithm. By studying the parameters of the machine learning algorithm we were able to construct universal shortest analytic quantum algorithm for arbitrary diagonal matrix of any size.
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