Speaker
Description
Quantum computing has the potential to deliver exponential speedups for computational tasks in specific problem domains. While digital quantum computing remains a long-term goal due to challenges in scalability and error correction, analog quantum computing offers a promising near-term alternative. In this work, we investigate the energy consumption and runtime of analog neutral-atom quantum processing units (QPUs) in applications involving the simulation of quantum many-body dynamics. We also compare their performance against state-of-the-art classical approaches. In particular, we examine the scaling behavior of classical methods—such as Matrix Product States (MPS) and time-dependent variational Monte Carlo (VMC)—and compare their energy footprints with those of neutral-atom QPUs. Our results highlight a potential advantage of quantum computing in terms of both energy efficiency and runtime for near-term devices.
