Pushing the Limits in Real-Time Measurements of Quantum Dynamics

Kleinherbers, Eric; Stegmann, Philipp; Kurzmann, Annika; Geller, M.; Lorke, Axel; König, Jürgen

doi:10.1103/physrevlett.128.087701

Cited by 18 publications

(13 citation statements)

References 45 publications

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“…Similar RTS phenomena are discerned in superconducting qubits [14], single photon avalanche diodes [15], ultrasensitive biosensors [16], and single-cell activities in ion channels [17]. Real-time dynamics in quantum electrical devices also reveal rapid jumping sequences [18,19]. Furthermore these bursting events can represent biological and biochemical processes like spontaneous transitions in gene-regulation network [20,21], where RTS models are adopted to explain stochastic individual gene activation and deactivation processes well.…”

mentioning

confidence: 74%

Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals

Robitaille¹,

Yang²,

Wang³

et al. 2022

Preprint

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Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. Reliable RTS analyses are crucial prerequisite to identify underlying mechanisms related to performance sensitivity. When numerous levels partake, complex patterns of multilevel RTSs occur, making their quantitative analysis exponentially difficult, hereby systematic approaches are found elusive. Here, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we build three deep neural network architectures that can process temporal data well and demonstrate the model accuracy extensively with a large dataset of different RTS types affected by controlling background noise size. Our protocol offers structured schemes to quantify complex RTSs from which meaningful interpretation and inference can ensue.

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mentioning

confidence: 74%

Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals

Robitaille¹,

Yang²,

Wang³

et al. 2022

Preprint

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“…For example, for the Fermi-Hubbard trimer with a built-in charge detector realized in Ref. 27 , extraction of the waiting time distribution requires time-resolved current measurements that have with very high accuracy been performed for simpler systems [67][68][69][70][71] . For larger systems, the relaxation time scales could be obtained by pumpprobe schemes, e.g., in the 1T-TaS 2 system.…”

Section: Discussionmentioning

confidence: 99%

Environment-induced decay dynamics of anti-ferromagnetic order in Mott-Hubbard systems

Schaller,

Queisser,

Szpak

et al. 2021

Preprint

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We study the dissipative Fermi-Hubbard model in the limit of weak tunneling and strong repulsive interactions, where each lattice site is tunnel-coupled to a Markovian fermionic bath. For cold baths at intermediate chemical potentials, the Mott insulator property remains stable and we find a fast relaxation of the particle number towards half filling. On longer time scales, we find that the anti-ferromagnetic order of the Mott-Néel ground state on bi-partite lattices decays, even at zero temperature. For zero and non-zero temperatures, we quantify the different relaxation time scales by means of waiting time distributions which can be derived from an effective (non-Hermitian) Hamiltonian and obtain fully analytic expressions for the Fermi-Hubbard model on a tetramer ring.

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“…As we will see in the following, the factorial moments and cumulants provide a convenient description of the single-electron micromaser. In particular, while ordinary cumulants are useful to describe continuous random variables, factorial cumulants are in some cases better suited to characterize discrete random variables, such as the number of counted electrons or photons [60][61][62][63][64][65][66][67][68][69][70][71].…”

Section: Full Counting Statisticsmentioning

confidence: 99%

“…Having characterized each of the two dynamical phases, we now go on to investigate the transition between them. To this end, we make use of Lee-Yang theory [30][31][32][33][34][35][36] by considering the zeros of the factorial moment generating function [60][61][62][63][64][65][66][67][68][69][70][71], which for our purposes plays the role of the partition function in equilibrium statistical mechanics [40][41][42][43][44][45][46][47][48]. However, in contrast to thermal phase transitions, we are not considering transitions between different equilibrium phases such as spin lattices with a vanishing or a finite average magnetization.…”

Section: Nonequilibrium Phase Transitionmentioning

confidence: 99%

“…Here, by contrast, we show how the phase transitions can be directly observed from accurate measurements of the full counting statistics. In particular, we will see that factorial moments and factorial cumulants [60][61][62][63][64][65][66][67][68][69][70][71] are particularly useful to reveal the nonequilibrium phase transition, which may be observed in future experiments.…”

Section: Introductionmentioning

confidence: 99%

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