Entanglement-Based Computation

Konstantin Wernli

In arXiv:1208.0365 entanglement polytopes where introduced as a coarsening of the SLOCC classification of multipartite entanglement. The advantages of classifying entanglement by entanglement polytopes are a finite hierarchy for all dimensions and a number of parameters linear in system size. In arXiv:1208.0365 a method to compute entanglement polytopes using geometric invariant theory is presented. In this thesis we consider alternative methods to compute them. Some geometrical and algebraical tools are presented that can be used to compute inequalities giving an outer approximation of the entanglement polytopes. Furthermore we present a numerical method which, in theory, can compute the entanglement polytope of any given SLOCC class given a representative. Using it we classify the entanglement polytopes of $2 \times 3 \times N$ systems.

Marcel Patulacci, Mona Lisa

Giuliano Benenti

Jincheng Zhang

This paper proposes a novel distributed computation architecture, Quantum Entanglement-Based Distributed Computation (QEB), leveraging quantum entanglement as the foundational mechanism for information transfer and computation. Traditional distributed computing systems are constrained by the limitations of classical communication, resulting in bottlenecks and reduced scalability. QEB overcomes these challenges by directly utilizing the inherent parallelism and resilience of quantum entanglement. The architecture decomposes computational tasks into a series of entangled sub-tasks, orchestrated across a network of quantum nodes. Synchronization and execution of these sub-tasks occur through controlled manipulation of the entangled states, eliminating the need for classical data transmission. Furthermore, the architecture incorporates fault tolerance through the dynamic reconstruction of entangled states, providing robustness against node failures. This approach promises significant advancements in computational performance and reliability, representing a paradigm shift in the field of distributed computing. The core claim of QEB is to build a highly parallel and fault-tolerant architecture by exploiting quantum entanglement as the primary communication channel. ---

Asher Peres

Jincheng Zhang

Quantum entanglement is a cornerstone of quantum computing, offering the potential for unparalleled computational power. However, harnessing its capabilities has been hampered by the inherent complexity of managing and optimizing entangled states. This paper presents an innovative approach to quantum entanglement computation, utilizing an adaptive learning framework to dynamically adjust quantum entanglement parameters. Our work demonstrates a significant improvement in computational efficiency through automated parameter tuning, significantly reducing the time required for entanglement calculations. The proposed mechanism leverages a self-learning algorithm to optimize the entanglement distribution and entanglement duration, thereby unlocking the potential of quantum computing. This research addresses a critical limitation in current quantum computing architectures – the reliance on manual parameter adjustment – and paves the way for more efficient and scalable quantum algorithms.