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We describe and analyze a quantum thermometer based on a multi-layered collisional model. The proposed architecture provides significant sensitivity even for short interaction times between the ancillae comprised in the thermometer and the system to be probed, and a small number of information-acquiring collisions. The assessment of the flow of information taking place within the layered thermometer and between system and thermometer reveals that the tuning of the mutual backflow of information has a positive influence on the precision of thermometry, and helps unveiling the information-theoretic mechanisms behind the working principles of the proposed architecture.

The return of the information from the environment to the system is a phenomenon can be related to existence of non-Markovian mechanisms in the environment and such transformation of resources can be useful for quantum information applications. Thus, understanding the details of the system-environment information dynamics, i.e., the transference of quantum resources is of key importance to design noise-resilient quantum technologies. In this work, we show how a quantum resource propagates from the main system to an environment, using as model a single qubit coupled to two linear chains of qubits, and also the information dynamics among the environment qubits. In this way, we characterize the propagation of information leaving the main qubit and going through the environment. Finally, we connect the conditions for the emergence of this dynamics to the existence of quantum Darwinism.

Previously only considered a frontier area of Physics, nowadays quantum computing is one of the fastest growing research field, precisely because of its technological applications in optimization problems, machine learning, information security and simulations. The goal of this article is to introduce the fundamentals of quantum computing, focusing on a promising quantum algorithm and its application to a financial market problem. More specifically, we discuss the portfolio optimization problem using the \textit{Quantum Approximate Optimization Algorithm} (QAOA). We not only describe the main concepts involved but also consider simple practical examples, involving financial assets available on the Brazilian stock exchange, with codes, both classic and quantum, freely available as a Jupyter Notebook. We also analyze in details the quality of the combinatorial portfolio optimization solutions through QAOA using SENAI/CIMATEC's ATOS QLM quantum simulator., Comment: In PORTUGUESE

Abstract Reservoir engineering is an important tool for quantum information science and quantum thermodynamics since it allows for preparing and/or protecting special quantum states of single or multipartite systems or to investigate fundamental questions of the thermodynamics as quantum thermal machines and their efficiencies. Here we employ this technique to engineer reservoirs with arbitrary (effective) negative and positive temperatures for a single spin system. To this end, we firstly engineer an appropriate interaction between a qubit system, a carbon nuclear spin, to a fermionic reservoir, in our case a large number of hydrogen nuclear spins that acts as the spins bath. This carbon-hydrogen structure is present in a polycrystalline adamantane, which was used in our experimental setup. The required interaction engineering is achieved by applying a specific sequence of radio-frequency pulses using Nuclear Magnetic Resonance (NMR), while the temperature of the bath can be controlled by appropriate preparation of the initial hydrogen nuclear spin state, being the predicted results in very good agreement with the experimental data. As an application we implemented a single qubit quantum thermal machine which operates at a single reservoir at effective negative temperature whose efficiency is always 100%, independent of the unitary transformation performed on the qubit system, as long as it changes the qubit state., Comment: 7 pages, 6 figures

Abstract We perform an experiment in which a quantum heat engine works under two reservoirs, one at a positive spin temperature and the other at an effective negative spin temperature i.e., when the spin system presents population inversion. We show that the efficiency of this engine can be greater than that when both reservoirs are at positive temperatures. We also demonstrate the counter-intuitive result that the Otto efficiency can be beaten only when the quantum engine is operating in the finite-time mode., Comment: 13 pages, 6 figures, Accepted in Physical Review Letters