Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNon-Markovian Quantum Probes
172
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We review the most recent developments in the theory of open quantum systems focusing on situations in which the reservoir memory effects, due to long-lasting and non-negligible correlations between system and environment, play a crucial role. These systems are often referred to as non-Markovian systems. After a brief summary of different measures of non-Markovianity that have been introduced over the last few years we restrict our analysis to the investigation of information flow between system and environment. Within this framework we introduce an important application of non-Markovianity, namely its use as a quantum probe of complex quantum systems. To illustrate this point we consider quantum probes of ultracold gases, spin chains, and trapped ion crystals and show how properties of these systems can be extracted by means of non-Markovianity measures.
rate research
Read More
We present a detailed investigation of the dynamics of two physically different qubit models, dephasing under the effect of an ultracold atomic gas in a Bose-Einstein condensed (BEC) state. We study the robustness of each qubit probe against environmental noise; even though the two models appear very similar at a first glance, we demonstrate that they decohere in a strikingly different way. This result holds significance for studies of reservoir engineering as well as for using the qubits as quantum probes of the ultracold gas. For each model we study whether and when, upon suitable manipulation of the BEC, the dynamics of the qubit can be described by a (non-)Markovian process and consider the the effect of thermal fluctuations on the qubit dynamics. Finally, we provide an intuitive explanation for the phenomena we observe in terms of the spectral density function of the environment.
The rapidly developing quantum technologies have put forward a requirement to precisely control and measure temperature of microscopic matters at quantum level. Many quantum thermometry schemes have been proposed. However, precisely measuring low temperature is still extremely challenging because the sensing errors obtained in these schemes tend to divergence with decreasing temperature. Using a continuous-variable system as a thermometer, we propose a non-Markovian quantum thermometry to measure the temperature of a quantum reservoir. A mechanism to make the sensing error $delta T$ scale with the temperature $T$ as $delta Tsimeq T$ in the full-temperature regime is discovered. Our analysis reveals that it is the quantum criticality of the total thermometer-reservoir system that causes this enhanced sensitivity. Solving the long-standing and challenging error-divergence problem, our result gives an efficient way to precisely measure the low temperature of quantum systems.
Characterisation protocols have so far played a central role in the development of noisy intermediate-scale quantum (NISQ) computers capable of impressive quantum feats. This trajectory is expected to continue in building the next generation of devices: ones that can surpass classical computers for particular tasks -- but progress in characterisation must keep up with the complexities of intricate device noise. A missing piece in the zoo of characterisation procedures is tomography which can completely describe non-Markovian dynamics. Here, we formally introduce a generalisation of quantum process tomography, which we call process tensor tomography. We detail the experimental requirements, construct the necessary post-processing algorithms for maximum-likelihood estimation, outline the best-practice aspects for accurate results, and make the procedure efficient for low-memory processes. The characterisation is the pathway to diagnostics and informed control of correlated noise. As an example application of the technique, we improve multi-time circuit fidelities on IBM Quantum devices for both standalone qubits and in the presence of crosstalk to a level comparable with the fault-tolerant noise threshold in a variety of different noise conditions. Our methods could form the core for carefully developed software that may help hardware consistently pass the fault-tolerant noise threshold.
Non-Markovian quantum effects are typically observed in systems interacting with structured reservoirs. Discrete-time quantum walks are prime example of such systems in which, quantum memory arises due to the controlled interaction between the coin and position degrees of freedom. Here we show that the information backflow that quantifies memory effects can be enhanced when the particle is subjected to uncorrelated static or dynamic disorder. The presence of disorder in the system leads to localization effects in 1-dimensional quantum walks. We shown that it is possible to infer about the nature of localization in position space by monitoring the information backflow in the reduced system. Further, we study other useful properties of quantum walk such as entanglement, interference and its connection to quantum non-Markovianity.
Machine learning methods have proved to be useful for the recognition of patterns in statistical data. The measurement outcomes are intrinsically random in quantum physics, however, they do have a pattern when the measurements are performed successively on an open quantum system. This pattern is due to the system-environment interaction and contains information about the relaxation rates as well as non-Markovian memory effects. Here we develop a method to extract the information about the unknown environment from a series of projective single-shot measurements on the system (without resorting to the process tomography). The method is based on embedding the non-Markovian system dynamics into a Markovian dynamics of the system and the effective reservoir of finite dimension. The generator of Markovian embedding is learned by the maximum likelihood estimation. We verify the method by comparing its prediction with an exactly solvable non-Markovian dynamics. The developed algorithm to learn unknown quantum environments enables one to efficiently control and manipulate quantum systems.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
