Spread Complexity in free fermion models

We study spread complexity and the statistics of work done for quenches in the three-spin interacting Ising model, the XY spin chain, and the Su-Schrieffer-Heeger model. We study these models without quench and for different schemes of quenches, such as sudden quench and multiple sudden quenches. We employ the Floquet operator technique to investigate all three models in the presence of time-dependent periodic driving of parameters. In contrast to the sudden quenched cases, the periodically varying parameter case clearly shows non-analytical behaviour near the critical point. We also elucidate the relation between work done and the Lanczos coefficient and how the statistics of work done behave near critical points.Comment: 23 pages, 18 figure

Passenger Screening using Deep Learning and Artificial Neural Networks

In this research, we have to detect the contrabands hidden in the human body’s scanned images at airport security machines using segmentation and classification. Present algorithm of security scanning machines at the airports of USA are producing high rate of false negatives which in cases lead to engage in a secondary, manual screening process that slows everything down. So to resolve this problem and to improve the detection of contrabands, new and efficient algorithm need to be made

Complexity and quenches in models with three and four spin interactions

We study information theoretic quantities in models with three and four spin interactions. These models show distinctive characteristics compared to their nearest neighbour counterparts. Here, we quantify these in terms of the Nielsen complexity in static and quench scenarios, the Fubini-Study complexity, and the entanglement entropy. The models that we study have a rich phase structure, and we show how the difference in the nature of phase transitions in these, compared to ones with nearest neighbour interactions, result in different behaviour of information theoretic quantities, from ones known in the literature. For example, the derivative of the Nielsen complexity does not diverge but shows a discontinuity near continuous phase transitions, and the Fubini-Study complexity may be regular and continuous across such transitions. The entanglement entropy shows a novel discontinuity both at first and second order quantum phase transitions. We also study multiple quench scenarios in these models and contrast these with quenches in the transverse XY model.Comment: 12 Pages, 11 Figure

FOTOC complexity in an extended Lipkin-Meshkov-Glick model

We study fidelity out-of-time-order correlators (FOTOCs) in an extended Lipkin-Meshkov-Glick model and demonstrate that these exhibit distinctive behaviour at quantum phase transitions in both the ground and the excited states. We show that the dynamics of the FOTOC have different behaviour in the symmetric and broken-symmetry phases, and as one approaches phase transition. If we rescale the FOTOC operator with time, then for small times, we establish that it is identical to the Loschmidt echo. We also compute the Nielsen complexity of the FOTOC operator in both phases, and apply this operator on the ground and excited states to obtain the quasi-scrambled state of the model. The FOTOC operator introduces a small perturbation on the original ground and excited states. For this perturbed state, we compute the quantum information metric to first order in perturbation, in the thermodynamic limit. We find that the associated Ricci scalar diverges at the phase transition on the broken-symmetry phase side, in contrast to the zeroth order result. Finally, we comment upon the Fubini-Study complexity in this model.Comment: Minor corrections. 10 Pages, 5 Figure

Machine Learning Decoder for 5G NR PUCCH Format 0

5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is necessary to set up and sustain high throughput radio links. This paper makes the first attempt at using Machine Learning techniques to improve the decoding performance of the Physical Uplink Control Channel Format 0. We use fully connected neural networks to classify the received samples based on the uplink control information content embedded within them. The trained neural network, tested on real-time wireless captures, shows significant improvement in accuracy over conventional DFT-based decoders, even at low SNR. The obtained accuracy results also demonstrate conformance with 3GPP requirements.Comment: Submitted to NCC conferenc

A STUDY ON USE OF RICE HUSK ASH IN CONCRETE

The rice husk is an agricultural waste which is obtained from milling process of paddy and approximately 22% of the weight of paddy is rice husk. The waste is used as fuel in producing stream in parboiling process. The 25 % the weight of husk is converted into ash which is known as rice husk ash (RHA) and is again a waste which is disposed. This ash consists of amorphous silica which can be used as pozzolana in making concrete and cement instead of disposing it without compromising on the properties of cement or concrete if replaced in specific proportion with other constituents of cement or concrete. In this study the ordinary Portland cement is replaced in different proportion with RHA to obtain concrete with comparable and satisfactory strength and properties to that of normal concrete. The proportions of replacement chosen are at 2.5% interval starting from 5 % to 15 % and the casted concrete were tested under compression at different ages and results obtained are compared with normal concrete of same grade and it is concluded that the results are comparable