Practical Use Cases of Neutral Atoms Quantum Computers

TL;DR

This review assesses the practical use cases of neutral-atom quantum computers, focusing on Rydberg-atom platforms that combine scalable 2D connectivity with long coherence and high-fidelity operations. It surveys hardware principles, benchmarking methods, and three major application areas: combinatorial optimization (notably MIS via Ising mappings and QAOA/QAA variants), physics simulations (Ising/Heisenberg models and lattice gauge theories), and chemistry-related tasks (molecular docking and quantum chemistry). A central theme is the development of register-mapping techniques, quantum-wiring strategies, and parity-encoding methods (e.g., LHZ) to address connectivity limits and higher-order terms, enabling robust tackling of QUBO/SAT problems and beyond. The article also highlights hybrid approaches that integrate machine learning and classical optimization to enhance parameter tuning, error mitigation, and problem-solving performance, while acknowledging hardware-imposed constraints (coherence times, need for local addressing) that must be overcome to realize practical quantum advantage.

Abstract

Quantum computing has quickly emerged as a revolutionary paradigm that holds the potential for greatly enhanced computational capability and algorithmic efficiency, in a wide range of areas. Among the various hardware platforms, neutral atom quantum processors based on Rydberg interactions are gaining increasing interest because of their scalability, qubit-connection flexibility, and intrinsic appropriateness for solving combinatorial optimization challenges. This paper provides an overview of the present capabilities, standards, and applications of neutral atom quantum computers. We first discuss recent hardware advancements and register mapping optimization techniques that enhance circuit fidelity and performance. We next review their uses as quantum simulators, in both classical and quantum hard problems, such as MIS and QUBO problems, quantum many-body models and molecules in chemistry and pharmacology. Applications for enhancing machine learning are also covered.