arXiv subjects

T. Jin

Publications and source records attributed to T. Jin.

5 recordsLinked to original sources

arXiv2022

Cascading of Nanomechanical Resonator Logic

Nanomechanical systems have been proposed as an alternative computing platform for high radiation environments, where semiconductor electronics traditionally fail, as well as to allow improved gate densities and energy consumption. While there have been numerous demonstrations of individual nanomechanical logic gates leveraging the Duffing nonlinearity, the development of useful nanomechanical logic circuits depends strongly on the ability to cascade multiple logic gates. Here we show theoretically that cascading nanomechanical logic gates, where the output of one gate is fed into the input of another, is a complex problem due to the transient dynamics of the collective system. These transient behaviours can lead to undesired bit flips, which precludes cascading altogether. We then show that this issue can be circumvented by carefully initialising the system prior to computation. We illustrate these salient features through the modelled dynamics of two cascaded nanomechanical NAND gates.

physics.app-ph↗

arXiv2019

Equilibrium Fluctuations in Maximally Noisy Extended Quantum Systems

We introduce and study a class of models of free fermions hopping between neighbouring sites with random Brownian amplitudes. These simple models describe stochastic, diffusive, quantum, unitary dynamics. We focus on periodic boundary conditions and derive the complete stationary distribution of the system. It is proven that the generating function of the latter is provided by the Harish-Chandra-Itzykson-Zuber integral which allows us to access all fluctuations of the system state. The steady state is characterized by non trivial correlations which have a topological nature. Diagrammatic tools appropriate for the study of these correlations are presented. In the thermodynamic large system size limit, the system approaches a non random equilibrium state plus occupancy and coherence fluctuations of magnitude scaling proportionally with the inverse of the square root of the volume. The large deviation function for those fluctuations is determined. Although decoherence is effective on the mean steady state, we observe that sub-leading fluctuating coherences are dynamically produced from the inhomogeneities of the initial occupancy profile.

cond-mat.stat-mech↗

arXiv2018

Monitoring continuous spectrum observables: the strong measurement limit

We revisit aspects of monitoring observables with continuous spectrum in a quantum system subject to dissipative (Lindbladian) or conservative (Hamiltonian) evolutions. After recalling some of the salient features of the case of pure monitoring, we deal with the case when monitoring is in competition with a Lindbladian evolution. We show that the strong measurement limit leads to a diffusion on the spectrum of the observable. For the case with competition between observation and Hamiltonian dynamics, we exhibit a scaling limit in which the crossover between the classical regime and a diffusive regime can be analyzed in details.

math-ph↗

arXiv2018

Transport in quantum chains under strong monitoring

We study the transport properties of quantum 1D systems under strong monitoring. The quantum Zeno effect inhibits transport and induces localization. Beyond the Zeno freezing and on long time scales, a new dynamics emerges in the form of a Markov process. Studying fermionic and bosonic chains under strong monitoring, we are able to identify the quantum origin of the classical exclusion process, inclusion process and a sub-class of the misanthrope process. Moreover, we show that passive monitoring cannot break time-reversal symmetry and that the transport generally loses its ballistic nature existing for weak measurements.

cond-mat.stat-mech↗

arXiv2012

Spectral Clustering: An empirical study of Approximation Algorithms and its Application to the Attrition Problem

Clustering is the problem of separating a set of objects into groups (called clusters) so that objects within the same cluster are more similar to each other than to those in different clusters. Spectral clustering is a now well-known method for clustering which utilizes the spectrum of the data similarity matrix to perform this separation. Since the method relies on solving an eigenvector problem, it is computationally expensive for large datasets. To overcome this constraint, approximation methods have been developed which aim to reduce running time while maintaining accurate classification. In this article, we summarize and experimentally evaluate several approximation methods for spectral clustering. From an applications standpoint, we employ spectral clustering to solve the so-called attrition problem, where one aims to identify from a set of employees those who are likely to voluntarily leave the company from those who are not. Our study sheds light on the empirical performance of existing approximate spectral clustering methods and shows the applicability of these methods in an important business optimization related problem.

cs.LG↗