Unraveling the topology of dissipative quantum systems

Gneiting, Clemens; Koottandavida, Akshay; Rozhkov, A. V.; Nori, Franco

doi:10.1103/physrevresearch.4.023036

Cited by 15 publications

(4 citation statements)

References 56 publications

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“…This number is chosen to meet a compromise; it is sufficiently large to ensure that the system is in a steady state, while also allowing for inference with a limited number of photons in a relatively short timescale spanning just a few tens of emitter lifetimes, which is typically within the range of nanoseconds for solid-state quantum emitters [66]. It is important to note that fixing N requires that each trajectory will have a different evolution time T [80,81]. While the choice of fixing N allows us to work straightforwardly with inputs of uniform dimension, there may be experimental scenarios where T is a more relevant metrological resource.…”

Section: System and Data Generationmentioning

confidence: 99%

Parameter estimation from quantum-jump data using neural networks

Rinaldi,

González Lastre,

García Herreros

et al. 2024

Quantum Sci. Technol.

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We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps.We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settings.

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Section: System and Data Generationmentioning

confidence: 99%

Parameter estimation from quantum-jump data using neural networks

Rinaldi,

González Lastre,

García Herreros

et al. 2024

Quantum Sci. Technol.

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“…Therefore, unraveling the extensions of topological phases in open quantum systems will become an urgent task. Despite being in its early stage, this research area has garnered increasing interest and ignited in-depth discussions [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Topological extension including quantum jump

Niu,

Wang

2024

J. Phys. A: Math. Theor.

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Non-Hermitian systems and the Lindblad form master equation have always been regarded as reliable tools in dissipative modeling. Intriguingly, existing literature often obtains an equivalent non-Hermitian Hamiltonian by neglecting the quantum jumping terms in the master equation. However, there lacks investigation into the effects of discarded terms as well as the unified connection between these two approaches. In this study, we study the Su-Schrieffer-Heeger model with collective loss and gain from a topological perspective. When the system undergoes no quantum jump events, the corresponding shape matrix exhibits the same topological properties in contrast to the traditional non-Hermitian theory. Conversely, the occurrence of quantum jumps can result in a shift in the positions of the phase transition. Our study provides a qualitative analysis of the impact of quantum jumping terms and reveals their unique role in quantum systems.

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“…Dissipation in these NH systems would give rise to many interesting effect that do not have Hermitian counterpart, such as NH skin effects [8][9][10][11], PT -symmetric physics [12,13], and the breakdown of bulk-boundary correspondence [14][15][16][17]. Moreover, the question that how the dissipation influences the topology of a system attracts much attention, which has been explored in many papers [18][19][20][21][22][23][24]. The fundamental interests in studying the topological properties of NH systems are to expand the symmetry classes, which had been settled by Bernard and LeClair based on four fundamental symmetries, resulting in a total of 43 symmetry classes, that is known as Bernard-LeClair class [25].…”

Section: Introductionmentioning

confidence: 99%