## About

27

Publications

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1,227

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Citations since 2017

## Publications

Publications (27)

Many real-world tasks include some kind of parameter estimation, i.e., determination of a parameter encoded in a probability distribution. Often, such probability distributions arise from stochastic processes. For a stationary stochastic process with temporal correlations, the random variables that constitute it are identically distributed but not...

Thermodynamic resources, beyond their well-known usefulness in work extraction and other thermodynamic tasks, are often important also in tasks that are not evidently thermodynamic. Here we develop a framework for identifying such resources in diverse applications of bosonic continuous-variable systems. Introducing the class of bosonic linear therm...

In the setting of regression, the standard formulation of gradient boosting generates a sequence of improvements to a constant model. In this paper, we reformulate gradient boosting such that it is able to generate a sequence of improvements to a nonconstant model, which may contain prior knowledge or physical insight about the data generating proc...

Thermodynamics connects our knowledge of the world to our capability to manipulate and thus to control it. This crucial role of control is exemplified by the third law of thermodynamics, Nernst's unattainability principle, stating that infinite resources are required to cool a system to absolute zero temperature. But what are these resources? And h...

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 m...

Constraints on work extraction are fundamental to our operational understanding of the thermodynamics of both classical and quantum systems. In the quantum setting, finite-time control operations typically generate coherence in the instantaneous energy eigenbasis of the dynamical system. Thermodynamic cycles can, in principle, be designed to extrac...

Quantifying how distinguishable two stochastic processes are is at the heart of many fields, such as machine learning and quantitative finance. While several measures have been proposed for this task, none have universal applicability and ease of use. In this article, we suggest a set of requirements for a well-behaved measure of process distinguis...

Constraints on work extraction are fundamental to our operational understanding of the thermodynamics of both classical and quantum systems. In the quantum setting, finite-time control operations typically generate coherence in the instantaneous energy eigenbasis of the dynamical system. Thermodynamic cycles can, in principle, be designed to extrac...

A hallmark of machine learning is the inductive discovery of hypotheses, whereby machine learning algorithms employ a mechanism, known as an inductive bias, to constrain the hypotheses under consideration. In this work, we present a learning framework for the discovery of vector fields, given predictions from a first principles approach and the cor...

A quantum battery is a work reservoir that stores energy in quantum degrees of freedom. When immersed in an environment, an open quantum battery needs to be stabilized against free-energy leakage due to decoherence, unavoidably entailing entropy production. For this purpose we here propose a stabilization protocol given by a nonunitary open-loop co...

A quantum battery is a work reservoir that stores energy in quantum degrees of freedom. When immersed in an environment an open quantum battery needs to be stabilized against free energy leakage into the environment. For this purpose we here propose a simple protocol that relies on projective measurement and obeys a second-law like inequality for t...

Quantifying how distinguishable two stochastic processes are lies at the heart of many fields, such as machine learning and quantitative finance. While several measures have been proposed for this task, none have universal applicability and ease of use. In this Letter, we suggest a set of requirements for a well-behaved measure of process distingui...

A system's deviation from its ambient temperature has long been known to be a resource---a consequence of the second law of thermodynamics, which constrains all systems to drift towards thermal equilibrium. Here we consider how such constraints generalize to continuous-variable quantum systems comprising interacting identical bosonic modes. Introdu...

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 behaviour far into the past, there are many such features to store, necessitating complex models with exte...

Isolating past information relevant for future prediction is central to quantitative science. Quantum models offer a promising approach, enabling statistically faithful modeling while using less past information than any classical counterpart. Here we introduce a class of phase-enhanced quantum models, representing the most general means of simulat...

Genuinely quantum states of a harmonic oscillator may be distinguished from their classical counterparts by the Glauber-Sudarshan P representation—a state lacking a positive P function is said to be nonclassical. In this paper, we propose a general operational framework for studying nonclassicality as a resource in networks of passive linear elemen...

Identifying and extracting the past information relevant to the future behaviour of stochastic processes is a central task in the quantitative sciences. Quantum models offer a promising approach to this, allowing for accurate simulation of future trajectories whilst using less past information than any classical counterpart. Here we introduce a cla...

Genuinely quantum states of a harmonic oscillator may be distinguished from their classical counterparts by the Glauber-Sudarshan P-representation -- a state lacking a positive P-function is said to be nonclassical. In this paper, we propose a general operational framework for studying nonclassicality as a resource in networks of passive linear ele...

The pursuit of simplicity underlies most of quantitative science. In stochastic modeling, there has been significant effort towards finding models that predict a process' future using minimal information from its past. Meanwhile, in condensed matter physics, finding efficient representations for large quantum many-body systems is a topic of critica...

Conventional quantum speed limits perform poorly for mixed quantum states: They are generally not tight and often significantly underestimate the fastest possible evolution speed. To remedy this, for unitary driving, we derive two quantum speed limits that outperform the traditional bounds for almost all quantum states. Moreover, our bounds are sig...

Optimizing open quantum system evolution is an important step on the way to achieving quantum computing and quantum thermodynamic tasks. In this article, we approach optimisation via variational principles and derive an open quantum system variational algorithm explicitly for Lindblad evolution in Liouville space. As an example of such control over...

We show that conventional quantum speed limits, for unitary driving, perform poorly for mixed quantum states. To remedy this, we derive two quantum speed limits that outperform the traditional bounds for almost all quantum states. Moreover, our bounds are significantly simpler to compute as well as experimentally more accessible.

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 quantum simulator for discrete-time stochastic processes which requires less internal memory than any classical analogue throughout the simulation. The simulator's interna...

Can collective quantum effects make a difference in a meaningful thermodynamic operation? Focusing on energy storage and batteries, we demonstrate that quantum mechanics can lead to an enhancement in the amount of work deposited per unit time, \textit{i.e.}, the charging power, when $N$ batteries are charged collectively. We first derive analytic u...

We study the problem of charging a quantum battery in finite time. We
demonstrate an analytical optimal protocol for the case of a single qubit.
Extending this analysis to an array of N qubits, we demonstrate that an N-fold
advantage in power per qubit can be achieved when global operations are
permitted. The exemplary analytic argument for this qu...

Accurately describing work extraction from a quantum system is a central objective for the extension of thermodynamics to individual quantum systems. The concepts of work and heat are surprisingly subtle when generalizations are made to arbitrary quantum states. We formulate an operational thermodynamics suitable for application to an open quantum...

Accurately describing work extraction from a quantum system is a central
objective for the extension of thermodynamics to individual quantum systems.
The concepts of work and heat are surprisingly subtle when generalizations are
made to arbitrary quantum states. We formulate an operational thermodynamics
suitable for application to an open quantum...

## Projects

Projects (2)