Interfering trajectories in experimental quantum-enhanced stochastic simulation

Implementing quantum dimensionality reduction for non-Markovian stochastic simulation

Complex systems are embedded in our everyday experience. Stochastic modelling enables us to understand and predict the behaviour of such systems, cementing its utility across the quantitative sciences. Accurate models of highly non-Markovian processes - where the future behaviour depends on events that happened far in the past - must track copious amounts of information about past observations, requiring high-dimensional memories. Quantum technologies can ameliorate this cost, allowing models of the same processes with lower memory dimension than corresponding classical models. Here we implement such memory-efficient quantum models for a family of non-Markovian processes using a photonic setup. We show that with a single qubit of memory our implemented quantum models can attain higher precision than possible with any classical model of the same memory dimension. This heralds a key step towards applying quantum technologies in complex systems modelling.

Effects of correlation in an information ratchet with finite tape

With the finite-tape autonomous information ratchet modeled by He et al. [Phys. Rev. E 105, 054131 (2022)2470-004510.1103/PhysRevE.105.054131], we recast the information processing second law, giving a tighter bound on the work extracted, in terms of the marginal bit-ratchet distribution defined from the joint tape-ratchet distribution. The marginal distribution is further utilized to probe and elucidate the conditions that lead to the presence of equilibrium and nonequilibrium stationary states in general, which are related to the effects of correlation. Applying our analysis to two designs of this information ratchet, where correlations within manifest differently, we uncover the mathematical condition for equilibrium stationary states for information ratchets that harness correlation, to identify them for engine operation during the transient phase.

Information processing second law for an information ratchet with finite tape

We model a class of discrete-time information ratchet with a finite tape and explore its thermodynamic consequence as a Maxwell demon. We found that, although it supports the operational regime of an engine or eraser, it cannot typically sustain these thermodynamic functionalities due to eventual equilibration as a result of the finite information capacity of the tape. Nonetheless, cumulative work can be accrued or expended through successive tape scans and we prove that at all time the ratchet obeys the information processing second law (IPSL). Unlike the IPSL for the infinite-tape ratchet which operates only at the stationary state, the IPSL here is applicable also at the transient phase of the ratchet operation. We explore two ratchet designs with the single-state perturbed coin (PC) ratchet being the simplest ratchet without memory, while the double-state modified Boyd's (MB) ratchet is the simplest ratchet with memory. Our analysis shows that the MB ratchet can harness correlation to accumulate more work by having a larger time constant to reach steady state relative to the PC ratchet.

Extending Quantum Probability from Real Axis to Complex Plane

Probability is an important question in the ontological interpretation of quantum mechanics. It has been discussed in some trajectory interpretations such as Bohmian mechanics and stochastic mechanics. New questions arise when the probability domain extends to the complex space, including the generation of complex trajectory, the definition of the complex probability, and the relation of the complex probability to the quantum probability. The complex treatment proposed in this article applies the optimal quantum guidance law to derive the stochastic differential equation governing a particle’s random motion in the complex plane. The probability distribution ρc(t,x,y) of the particle’s position over the complex plane z=x+iy is formed by an ensemble of the complex quantum random trajectories, which are solved from the complex stochastic differential equation. Meanwhile, the probability distribution ρc(t,x,y) is verified by the solution of the complex Fokker–Planck equation. It is shown that quantum probability |Ψ|2 and classical probability can be integrated under the framework of complex probability ρc(t,x,y), such that they can both be derived from ρc(t,x,y) by different statistical ways of collecting spatial points.

Extreme Dimensionality Reduction with Quantum Modeling

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 the future of a process bears a strong dependence on its behavior far into the past, there are many such features to store, necessitating complex models with extensive memories. Here, we highlight a family of stochastic processes whose minimal classical models must devote unboundedly many bits to tracking the past. For this family, we identify quantum models of equal accuracy that can store all relevant information within a single two-dimensional quantum system (qubit). This represents the ultimate limit of quantum compression and highlights an immense practical advantage of quantum technologies for the forecasting and simulation of complex systems.