The field of classical stochastic processes forms a major branch of mathematics. Stochastic
processes are, of course, also very well studied in biology, chemistry, ecology, geology …

Stochastic processes are as ubiquitous throughout the quantitative sciences as they are
notorious for being difficult to simulate and predict. In this Letter, we propose a unitary …

Effective and efficient forecasting relies on identification of the relevant information
contained in past observations—the predictive features—and isolating it from the rest. When …

Causal asymmetry is one of the great surprises in predictive modeling: The memory required
to predict the future differs from the memory required to retrodict the past. There is a …

By exploiting the complexity intrinsic to quantum dynamics, quantum technologies promise a
host of computational advantages. One such advantage lies in the field of stochastic …

Isolating past information relevant for future prediction is central to quantitative science.
Quantum models offer a promising approach, enabling statistically faithful modeling while …

We introduce a quantum algorithm for memory-efficient biased sampling of rare events
generated by classical memoryful stochastic processes. Two efficiency metrics are used to …

Stochastic modeling is an essential component of the quantitative sciences, with hidden
Markov models (HMMs) often playing a central role. Concurrently, the rise of quantum …

A possible notion of nonclassicality for single systems can be defined on the basis of the
notion of memory cost of classically simulating probabilities observed in a temporal …

In stochastic modeling, there has been a significant effort towards finding predictive models
that predict a stochastic process' future using minimal information from its past. Meanwhile …