Speaker
Description
Quantum cellular automata (QCA) provide a compelling framework for distributed quantum computation and the simulation of many-body quantum dynamics through the application of local, translation-invariant update rules. These update rules can also be used in the context of machine learning. In this work, we implement a QCA framework designed to solve the Maximum Independent Set graph problem on a real neutral atom quantum processing unit. To achieve this, we adapt the QCA update rules to the constraints and native operations of the neutral-atom device's architecture. The objective of this work is to evaluate the performance of current neutral-atom quantum hardware for QCA-based computation, identify the practical limitations imposed by existing device constraints, and assess the feasibility of executing quantum cellular automata on near-term quantum processors
| Topic | Quantum algorithms for machine learning applications |
|---|---|
| Additional Topic | Encoding and processing of data in quantum systems |