Comprehensive Library of Variational LSE Solvers
This work addresses a gap in practical tools for researchers in quantum machine learning, but it is incremental as it builds on existing theoretical methods.
The authors tackled the lack of comprehensive implementations for variational linear systems of equations solvers by developing a framework that realizes existing approaches and introduces enhancements, resulting in a user-friendly interface for researchers working on end-to-end applications.
Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate finding solutions for large systems. Although there is a wealth of theoretical research on these algorithms, only fragmentary implementations exist. To fill this gap, we have developed the variational-lse-solver framework, which realizes existing approaches in literature, and introduces several enhancements. The user-friendly interface is designed for researchers that work at the abstraction level of identifying and developing end-to-end applications.