Related papers: Quantum targeted energy transfer through machine l…
We investigate multichromophoric energy transfer allowing for bath-induced fluctuations at different sites to be correlated. As a prototype system we consider a light-harvesting antenna surrounding a reaction center. We show that the…
We propose an efficient scheme for the implementation of quantum information transfer in a onedimensional coupled resonator waveguide. We show that, based on the effective long-range dipole-dipole interactions between the atoms mediated by…
We identify and explore the intriguing property of resource resonance arising within resource theories of entanglement, coherence and thermodynamics. While the theories considered are reversible asymptotically, the same is generally not…
Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics.…
We introduce transfer learning for nonlinear dynamics, which enables efficient predictions of chaotic dynamics by utilizing a small amount of data. For the Lorenz chaos, by optimizing the transfer rate, we accomplish more accurate inference…
Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning…
We analyse a protocol of quantum energy teleportation that transports energy from the left edge of a linear ion crystal to the right edge by local operations and classical communication at a speed considerably greater than the speed of a…
We study the problem of joint communication and sensing for data transmission systems using optimal quantum instruments in order to transmit data and, at the same time, estimate environmental parameters. In particular we consider the…
Machine learning has recently been applied and deployed at several light source facilities in the domain of Accelerator Physics. We introduce an approach based on machine learning to produce a fast-executing model that predicts the…
Bayesian methods which utilize Bayes' theorem to update the knowledge of desired parameters after each measurement, are used in a wide range of quantum science. For various applications in quantum science, efficiently and accurately…
The generation of equilibrium samples of molecular systems has been a long-standing problem in statistical physics. Boltzmann Generators are a generative machine learning method that addresses this issue by learning a transformation via a…
Quantum state transfer (QST) via homogeneous spin chains plays a crucial role in building scalable quantum hardware. A basic quantum state transmission protocol prepares a state in one qubit and transfers it to another through a channel,…
In this paper, we present a new idea for Transfer Learning (TL) based on Gibbs Sampling. Gibbs sampling is an algorithm in which instances are likely to transfer to a new state with a higher possibility with respect to a probability…
The quantum internet holds transformative potential for global communication by harnessing the principles of quantum information processing. Despite significant advancements in quantum communication technologies, the efficient distribution…
Quantum energy teleportation (QET) exploits the existence of correlations to enable remote energy transfer without the need for physical energy carriers between emitter and receiver. This paper presents a review of the thermodynamic…
Resonant transfer of energy between excited states in a system of two semiconductor quantum dots is studied theoretically. The model Hamiltonian has been formulated, which allowed describe the impact on the dynamics of the resonant laser…
We develop a quantum scattering model to describe the exciton transport through the Fenna-Matthews-Olson(FMO) complex. It is found that the exciton transport involved the optimal quantum coherence is more efficient than that involved…
Here we will give a perspective on new possible interplays between Machine Learning and Quantum Physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum…
Under physiological conditions, ballistic long-range transfer of electronic excitations in molecular aggregates is generally expected to be suppressed by noise and dissipative processes. Hence, quantum phenomena are not considered to be…
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in…