Quantum compression with classically simulatable circuits
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
Randomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback
Benchmarking MOEAs for solving continuous multi-objective RL problems
Mergenetic: a Simple Evolutionary Model Merging Library
Evolutionary thoughts: integration of large language models and evolutionary algorithms
RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search
토론
이 논문과 연결된 포럼 글이 아직 없습니다.