Causal Asymmetry in a Quantum World

TL;DR

This paper demonstrates that quantum models can eliminate the memory overhead associated with causal asymmetry in predictive modeling, surpassing classical models in both natural and reverse temporal directions.

Contribution

It introduces the concept that quantum models can remove the memory cost asymmetry in causal modeling, outperforming classical approaches in all temporal directions.

Findings

Quantum models eliminate causal asymmetry memory overhead.

Quantum models outperform classical models in reverse time.

Quantum advantage persists even with unbounded memory requirements.

Abstract

Causal asymmetry is one of the great surprises in predictive modelling: the memory required to predict the future differs from the memory required to retrodict the past. There is a privileged temporal direction for modelling a stochastic process where memory costs are minimal. Models operating in the other direction incur an unavoidable memory overhead. Here we show that this overhead can vanish when quantum models are allowed. Quantum models forced to run in the less natural temporal direction not only surpass their optimal classical counterparts, but also any classical model running in reverse time. This holds even when the memory overhead is unbounded, resulting in quantum models with unbounded memory advantage.