High-performance dynamic quantum clustering on graphics processors

Wittek, Peter

doi:10.1016/j.jcp.2012.08.048

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…the minimization of the potential V (x) leads to the desired clustering. An extension to dynamical quantum systems has been introduced in [100]. In this case the expectation values of the position operator evolves in time toward the closer minimum of the potential.…”

Section: Towards Unified Analysis Of Network Complexitymentioning

confidence: 99%

“…The cross-pollination of community detection with quantum mechanics is in two levels. On the one hand, chronologically, the first attempt was to borrow tools from quantum mechanics for applications to classical systems [89,[98][99][100][101]. On the other hand an algorithm to find communities in complex quantum systems was proposed in [13].…”

Section: Comparing Classical Networkmentioning

confidence: 99%

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Complex networks from classical to quantum

2019

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NETWORKS IN QUANTUM PHYSICS VS COMPLEXITYNetwork and graph theory fundamentally arises in nearly all aspects of quantum information and computation. As is the case with traditional network science, not arXiv:1702.08459v4 [quant-ph] 16 Apr 2019Box 1 Cross-pollination between the fields of complex networks and quantum information science.In recent years, seminal work has been carried out at the intersection of quantum information and computation and complex network theory. We attempt to catalog the scope of this work in Fig. 1. complex networks and quantum physics (current status) quantum algorithms for network analysis algorithms and descriptors quantum transport on complex networks quantum communication networks graphity and emergent models of space-time related types of quantum networks quantum inspired network tools network analysis adapted to quantum physical resources tools for understanding network information theory quantum centrality measures random network models detecting community structure in quantum systems random quantum circuits random tensor networks and geometry quantum generalizations of random graph models entanglement percolation on complex networks quantum inspired network measures polynomial and superpolynomial reductions for certain network problems such as flow and effective resistance polynomial and superpolynomial reductions for certain machine learning and data analysis problems quantum walks solving graph recognition and search engine ranking problems walk models of exciton transport in photosynthetic complexes

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Section: Towards Unified Analysis Of Network Complexitymentioning

confidence: 99%

Section: Comparing Classical Networkmentioning

confidence: 99%

Complex networks from classical to quantum

2019

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“…Each simulation was executed from t = 0 to t = 1 with a time-step of dt = 0.01. The red stars indicate the starting location of each test particle (with zero momentum), and the black lines demonstrate their time-trajectories according to Equations ( 21) and (22).…”

Section: Langevin Annealing and Data Clusteringmentioning

confidence: 99%

“…such that clustering is achieved by temporally propagating the system based on H, and tracking the wave-packets on their oscillatory trajectory about potential minima [22].…”

Section: Introductionmentioning

confidence: 99%

Data clustering based on Langevin annealing with a self-consistent potential

Lafata

Zhou

Liu

et al. 2018

Preprint

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This paper introduces a novel data clustering algorithm based on Langevin dynamics, where the associated potential is constructed directly from the data. To introduce a self-consistent potential, we adopt the potential model from the established Quantum Clustering method. The first step is to use a radial basis function to construct a density distribution from the data. A potential function is then constructed such that this density distribution is the ground state solution to the time-independent Schrödinger equation. The second step is to use this potential function with the Langevin dynamics at sub-critical temperature to avoid ergodicity. The Langevin equations take a classical Gibbs distribution as the invariant measure, where the peaks of the distribution coincide with minima of the potential surface. The time dynamics of individual data points lead to different metastable states, which are interpreted as cluster centers. Clustering is therefore achieved when subsets of the data aggregate -as a result of the Langevin dynamics for a moderate period of time -in the neighborhood of a particular potential minimum. While the data points are pushed towards potential minima by the potential gradient, Brownian motion allows them to effectively tunnel through local potential barriers and escape saddle points into locations of the potential surface otherwise forbidden. The algorithms feasibility is first established based on

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“…We will also investigate the possibilities of expediting DQC performance. For example, it has been shown recently that DQC can be parallelized and efficiently executed using graphics processors [8]. We will investigate if faster algorithm execution can be achieved by clustering of spectral data in time or clustering of temporal data in the energy domain by introducing appropriate simplifications in either approach.…”

Section: Future Workmentioning

confidence: 99%

Detection of nuclear sources in search survey using dynamic quantum clustering of gamma-ray spectral data

2014

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In a search scenario, nuclear background spectra are continuously measured in short acquisition intervals with a mobile detector-spectrometer. Detecting sources from measured data is difficult because of low signal to noise ratio (S/N) of spectra, large and highly varying background due to naturally occurring radioactive material (NORM), and line broadening due to limited spectral resolution of nuclear detector. We have invented a method for detection of sources using clustering of spectral data. Our method takes advantage of the physical fact that a source not only produces counts in the region of its spectral emission, but also has the effect on the entire detector spectrum via Compton continuum. This allows characterizing the low S/N spectrum without distinct isotopic lines using multiple data features. We have shown that noisy spectra with low S/N can be grouped by overall spectral shape similarity using a data clustering technique called Dynamic Quantum Clustering (DQC). The spectra in the same cluster can then be averaged to enhance S/N of the isotopic spectral line. This would allow for increased accuracy of isotopic identification and lower false alarm rate. Our method was validated in a proof-ofprinciple study using a data set of spectra measured in one-second intervals with Sodium Iodide detector. The data set consisted of over 7000 spectra obtained in urban background measurements, and approximately 70 measurements of Cs-137 and Co-60 sources. Using DQC analysis, we have observed that all spectra containing Cs-137 and Co-60 signal cluster away from the background.

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High-performance dynamic quantum clustering on graphics processors

Cited by 15 publications

References 26 publications

Complex networks from classical to quantum

Complex networks from classical to quantum

Data clustering based on Langevin annealing with a self-consistent potential

Detection of nuclear sources in search survey using dynamic quantum clustering of gamma-ray spectral data

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