Quantum compression with classically simulatable circuits
This addresses the need for efficient quantum resource usage in noisy quantum devices, though it appears incremental as it builds on the existing notion of quantum autoencoders.
The paper tackles the problem of compressing quantum information to reduce resource requirements by proposing a strategy to design quantum autoencoders using evolutionary algorithms. They successfully demonstrate initial applications for compressing different families of quantum states, noting that using a restricted gate set allows for efficient simulation of the generated circuits.
As we continue to find applications where the currently available noisy devices exhibit an advantage over their classical counterparts, the efficient use of quantum resources is highly desirable. The notion of quantum autoencoders was proposed as a way for the compression of quantum information to reduce resource requirements. Here, we present a strategy to design quantum autoencoders using evolutionary algorithms for transforming quantum information into lower-dimensional representations. We successfully demonstrate the initial applications of the algorithm for compressing different families of quantum states. In particular, we point out that using a restricted gate set in the algorithm allows for efficient simulation of the generated circuits. This approach opens the possibility of using classical logic to find low representations of quantum data, using fewer computational resources.