62 research outputs found
State Transfer in Permanently Coupled Quantum Chains
No full textin Quantum Information Processing From Theory to Experiment Vol. 199, NATO Science Series: Computer & Systems Sciences; Edited by: D.G. Angelakis, M. Christandl, A. Ekert, A. Kay and S. Kulik (Proceedings of the NATO Advanced Research Workshop, Crete.
Out-of-equilibrium physics in driven dissipative coupled resonator arrays
No full textCoupled resonator arrays have been shown to exhibit interesting many- body physics including Mott and Fractional Hall states of photons. One of the main differences between these photonic quantum simulators and their cold atoms coun- terparts is in the dissipative nature of their photonic excitations. The natural equi- librium state is where there are no photons left in the cavity. Pumping the system with external drives is therefore necessary to compensate for the losses and realise non-trivial states. The external driving here can easily be tuned to be incoherent, coherent or fully quantum, opening the road for exploration of many body regimes beyond the reach of other approaches. In this chapter, we review some of the physics arising in driven dissipative coupled resonator arrays including photon fermionisa- tion, crystallisation, as well as photonic quantum Hall physics out of equilibrium. We start by briefly describing possible experimental candidates to realise coupled resonator arrays along with the two theoretical models that capture their physics, the Jaynes-Cummings-Hubbard and Bose-Hubbard Hamiltonians. A brief review of the analytical and sophisticated numerical methods required to tackle these systems is included
Topological data analysis and machine learning
No full textTopological data analysis refers to approaches for systematically and
reliably computing abstract ``shapes'' of complex data sets. There are various
applications of topological data analysis in life and data sciences, with
growing interest among physicists. We present a concise yet (we hope)
comprehensive review of applications of topological data analysis to physics
and machine learning problems in physics including the detection of phase
transitions. We finish with a preview of anticipated directions for future
research.Comment: Invited review, 15 pages, 7 figures, 117 reference
Photonic band structure design using persistent homology
No full text10.1063/5.0041084APL Photonics6330802
Topological data analysis and machine learning
No full textTopological data analysis refers to approaches for systematically and reliably computing abstract ``shapes'' of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise yet (we hope) comprehensive review of applications of topological data analysis to physics and machine learning problems in physics including the detection of phase transitions. We finish with a preview of anticipated directions for future research
Unsupervised learning of quantum many-body scars using intrinsic dimension
No full textQuantum many-body scarred systems contain both thermal and non-thermal scar
eigenstates in their spectra. When these systems are quenched from special
initial states which share high overlap with scar eigenstates, the system
undergoes dynamics with atypically slow relaxation and periodic revival. This
scarring phenomenon poses a potential avenue for circumventing decoherence in
various quantum engineering applications. Given access to an unknown scar
system, current approaches for identification of special states leading to
non-thermal dynamics rely on costly measures such as entanglement entropy. In
this work, we show how two dimensionality reduction techniques,
multidimensional scaling and intrinsic dimension estimation, can be used to
learn structural properties of dynamics in the PXP model and distinguish
between thermal and scar initial states. The latter method is shown to be
robust against limited sample sizes and experimental measurement errors.Comment: 16 pages, 5 figures; added reference
Photonic band structure design using persistent homology
No full textThe machine learning technique of persistent homology classifies complex systems or datasets by computing their topological features over a range of characteristic scales. There is growing interest in applying persistent homology to characterize physical systems such as spin models and multiqubit entangled states. Here we propose persistent homology as a tool for characterizing and optimizing band structures of periodic photonic media. Using the honeycomb photonic lattice Haldane model as an example, we show how persistent homology is able to reliably classify a variety of band structures falling outside the usual paradigms of topological band theory, including "moat band" and multi-valley dispersion relations, and thereby control the properties of quantum emitters embedded in the lattice. The method is promising for the automated design of more complex systems such as photonic crystals and Moire superlattices
Sestrin 2 levels are associated with emphysematous phenotype of COPD
No full textSestrins (Sesns) are a family of highly conserved stress-inducible proteins and various stresses have been shown to strongly up-regulate them. Sestrin 2 (Sesn2) deficiency has been shown to partially suppress pulmonary emphysema. The aim of this study was to evaluate Sesn2 levels in COPD patients and its possible associations with the presence of emphysema and blood eosinophils. All patients underwent lung function testing and highresolution computed tomography (HRCT) of the chest. The presence of emphysematous lesions in >15% of the pulmonary parenchyma was considered as significant emphysema. Sixty-seven patients were included in the study. 40/67 patients were characterized as having significant emphysema. Patients with significant emphysema had higher levels of Sesn2 (ng/ml) [median (IQR) 6.7 (2.7,10.3 vs 1.09 (0.9,1.9), p<0.001)] and significantly lower % and absolute blood eosinophil counts (cells/μL) compared to patients without emphysema [1 (0, 2) vs 4 (2, 4) p<0.001 and 62 (0, 110) vs 248 (180, 300), p<0.001 respectively]. Sesn2 presented a significant positive correlation to the score of emphysema in HRCT (rs = 0.87, p<0.001) and similar positive but weaker correlation to FRC (rs = 0.27, p = 0.024). Negative correlations were observed between Sesn2 and either the %of blood eosinophils and/or the absolute blood eosinophil count (rs = -0.79, p<0.001, and rs = -0.78, p<0.001 respectively). Sesn2 levels above 1.87 ng/ml showed a high diagnostic performance for the presence of significant emphysema in HRCT with an AUC 0.93, 95% CI (0.85,0.98), p<0.001. Sesn2 could serve as a potential biomarker of emphysema. © 2022 Angelakis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
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