Dimensional Quantum Memory Advantage in the Simulation of Stochastic Processes

• Main Authors: Farzad Ghafari, Nora Tischler, Jayne Thompson, Mile Gu, Lynden K. Shalm, Varun B. Verma, Sae Woo Nam, Raj B. Patel, Howard M. Wiseman, Geoff J. Pryde
• Format: Article
• Language: English
• Published: American Physical Society 2019-10-01
• Series: Physical Review X
• Online Access: http://doi.org/10.1103/PhysRevX.9.041013

Stochastic processes underlie a vast range of natural and social phenomena. Some processes such as atomic decay feature intrinsic randomness, whereas other complex processes, e.g., traffic congestion, are effectively probabilistic because we cannot track all relevant variables. To simulate a stochastic system’s future behavior, information about its past must be stored, and thus memory is a key resource. Quantum information processing promises a memory advantage for stochastic simulation. Here, we report the first experimental demonstration that a quantum stochastic simulator can encode the required information in fewer dimensions than any classical simulator, thereby achieving a quantum advantage in minimal memory requirements using an individual simulator. This advantage is in contrast to recent proof-of-concept experiments, where the memory saving would only become accessible in the limit of a large number of parallel simulations. In those examples, the minimal memory registers of individual quantum simulators had the same dimensionality as their classical counterparts. Our photonic experiment thus establishes the potential of new, practical resource savings in the simulation of complex systems.