Accuracy vs Memory Advantage in the Quantum Simulation of Stochastic Processes
This addresses the trade-off between accuracy and memory in quantum simulation for inference tasks, with potential implications for learning, though it appears incremental as it extends prior work on quantum advantage under ideal conditions.
The paper tackled the problem of whether quantum simulators maintain a memory advantage over classical ones when modeling stochastic processes with imperfect accuracy, showing that quantum models achieve equal accuracy with less memory or better accuracy with the same memory.
Many inference scenarios rely on extracting relevant information from known data in order to make future predictions. When the underlying stochastic process satisfies certain assumptions, there is a direct mapping between its exact classical and quantum simulators, with the latter asymptotically using less memory. Here we focus on studying whether such quantum advantage persists when those assumptions are not satisfied, and the model is doomed to have imperfect accuracy. By studying the trade-off between accuracy and memory requirements, we show that quantum models can reach the same accuracy with less memory, or alternatively, better accuracy with the same memory. Finally, we discuss the implications of this result for learning tasks.