Unifying methods for optimal control in non-Markovian quantum systems via process tensors.

The large dimensionality of environments is the limiting factor in applying optimal control to open quantum systems beyond the Markovian approximation. Various methods exist to simulate non-Markovian systems, which effectively reduce the environment to a number of active degrees of freedom. Here, we show that several of these methods can be expressed in terms of a process tensor in the form of a matrix-product-operator, which serves as a unifying framework to show how they can be used in optimal control and to compare their performance.

OQuPy: A Python package to efficiently simulate non-Markovian open quantum systems with process tensors.

Non-Markovian dynamics arising from the strong coupling of a system to a structured environment is essential in many applications of quantum mechanics and emerging technologies. Deriving an accurate description of general quantum dynamics including memory effects is, however, a demanding task, prohibitive to standard analytical or direct numerical approaches. We present a major release of our open source software package, OQuPy (Open Quantum System in Python), which provides several recently developed numerical methods that address this challenging task.

Reservoir-Free Decoherence in Flying Qubits.

An effective time-dependent Hamiltonian can be implemented by making a quantum system fly through an inhomogeneous potential, realizing, for example, a quantum gate on its internal degrees of freedom. However, flying systems have a spatial spread that will generically entangle the internal and spatial degrees of freedom, leading to decoherence in the internal state dynamics, even in the absence of any external reservoir. We provide formulas valid at all times for the dynamics, fidelity, and change of entropy for ballistic particles with small spatial spreads, quantified by Δx.

Beyond Local Structures in Critical Supercooled Water through Unsupervised Learning.

The presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often requires designing parameters that are subject to human intervention.

Seeking and sharing information in collective olfactory search.

Searching for a target is a task of fundamental importance for many living organisms. Long-distance search guided by olfactory cues is a prototypical example of such a process. The searcher receives signals that are sparse and very noisy, making the task extremely difficult.

Stochastic Entropy Production: Fluctuation Relation and Irreversibility Mitigation in Non-unital Quantum Dynamics.

In this work, we study the stochastic entropy production in open quantum systems whose time evolution is described by a class of non-unital quantum maps. In particular, as in Phys Rev E 92:032129 (2015), we consider Kraus operators that can be related to a nonequilibrium potential. This class accounts for both thermalization and equilibration to a non-thermal state.

Do Machine-Learning Atomic Descriptors and Order Parameters Tell the Same Story? The Case of Liquid Water.

Machine-learning (ML) has become a key workhorse in molecular simulations. Building an ML model in this context involves encoding the information on chemical environments using local atomic descriptors. In this work, we focus on the Smooth Overlap of Atomic Positions (SOAP) and their application in studying the properties of liquid water both in the bulk and at the hydrophobic air-water interface.

Optimizing airborne wind energy with reinforcement learning.

Airborne wind energy is a lightweight technology that allows power extraction from the wind using airborne devices such as kites and gliders, where the airfoil orientation can be dynamically controlled in order to maximize performance. The dynamical complexity of turbulent aerodynamics makes this optimization problem unapproachable by conventional methods such as classical control theory, which rely on accurate and tractable analytical models of the dynamical system at hand. Here we propose to attack this problem through reinforcement learning, a technique that-by repeated trial-and-error interactions with the environment-learns to associate observations with profitable actions without requiring prior knowledge of the system.

Seeding Crystallization in Time.

We introduce the concept of seeding of crystallization in time by studying the dynamics of an ensemble of coupled continuous time crystals. We demonstrate that a single subsystem in a broken-symmetry phase acting as a nucleation center may induce time-translation symmetry breaking across the entire ensemble. Seeding is observed for both coherent and dissipative coupling, as well as for a broad range of parameter regimes.

Betweenness centrality for temporal multiplexes.

Betweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes.

Transcriptome resilience predicts thermotolerance in Caenorhabditis elegans.

Background: The detrimental effects of a short bout of stress can persist and potentially turn lethal, long after the return to normal conditions. Thermotolerance, which is the capacity of an organism to withstand relatively extreme temperatures, is influenced by the response during stress exposure, as well as the recovery process afterwards. While heat-shock response mechanisms have been studied intensively, predicting thermal tolerance remains a challenge.

Novel guanosine derivatives against Zika virus polymerase in silico.

The Zika virus (ZIKV) outbreak, which started in the year 2015, is considered the fastest and most widely spread outbreak reported for this flavivirus. The polymerase domain of the NS5 protein has been targeted in other viral infections and is recognized as a suitable target in ZIKV infection. Different novel modified compounds against ZIKV NS5 have been tested in silico.

Molecular dynamics simulation revealed binding of nucleotide inhibitors to ZIKV polymerase over 444 nanoseconds.

In the year 2015, new Zika virus (ZIKV) broke out in Brazil and spread away in more than 80 countries. Scientists directed their efforts toward viral polymerase in attempt to find inhibitors that might interfere with its function. In this study, molecular dynamics simulation (MDS) was performed over 444 ns for a ZIKV polymerase model.

Hydrogen divacancy diffusion: a new perspective on H migration in MgH materials for energy storage.

The formation and diffusion of pairs of hydrogen vacancies (divacancies) in magnesium hydride is modeled using density functional theory. Compared to the commonly studied case of single hydrogen vacancies, it is found that divacancies are energetically favored over two isolated vacancies. Also, as a function of the diffusion axis considered, the calculated diffusion barriers of divacancies are either smaller or of comparable magnitude with respect to the diffusion barriers of a single vacancy.

Quantitative structure-activity relationship and molecular docking revealed a potency of anti-hepatitis C virus drugs against human corona viruses.

A number of human coronaviruses (HCoVs) were reported in the last and present centuries. Some outbreaks of which (eg, SARS and MERS CoVs) caused the mortality of hundreds of people worldwide. The problem of finding a potent drug against HCoV strains lies in the inability of finding a drug that stops the viral replication through inhibiting its important proteins.

Assembly of protein tertiary structures from secondary structures using optimized potentials.

We present a simulated annealing-based method for the prediction of the tertiary structures of proteins given knowledge of the secondary structure associated with each amino acid in the sequence. The backbone is represented in a detailed fashion whereas the sidechains and pairwise interactions are modeled in a simplified way, following the LINUS model of Srinivasan and Rose. A perceptron-based technique is used to optimize the interaction potentials for a training set of three proteins.