46 results about "Quantum clustering" patented technology

Techniques for grouping related objects such as documents and files using quantum clustering are disclosed. A method may include constructing a feature-object database of multiple objects. The feature-object database may have quantized selected features as keys. A connected objects database maybe built. Clusters of connected objects may be identified in the connected objects database. The clusters of identified objects may be evaluated to determine groups of related objects. The method may be implemented on a computing device.

A method of determining clusters of data within a dataset, the dataset is represented by a plurality of multidimensional data entries, the method comprises (a) spanning a space, represented by a plurality of points; (b) determining a density function over the space;(c) associating a potential to the density function; (d) locating a plurality of local minima of the potential; and (e) for each of the plurality of local minima, attributing at least one of the points; thereby determining clusters of data within the dataset.

In the present work, quantum clustering is extended to provide a dynamical approach for data clustering using a time-dependent Schrödinger equation. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering. Additionally, the parameters of the analysis can be modified in order to improve the efficiency of the dynamic quantum clustering processes.

Stable atomic quantum clusters, AQCs, characterized by being composed of at least 500 metal atoms, its production process characterized by having a kinetic control and by maintaining a low concentration of reagents in the reaction medium, as well as the uses of these clusters as sensors (fluorescent, magnetic or chemical), electrocatalysts and as cytostatics and/or cytotoxics.

The invention relates to the technical field of computational intelligence, in particular to an interval type II intuitionistic fuzzy random vector function connection neural network design method. The interval type II intuitionistic fuzzy random vector function connection neural network design method comprises the following steps: constructing an interval type II intuitionistic fuzzy random vector function connection neural network; adopting a hybrid learning method combining quantum clustering algorithm, swarm intelligence algorithm and least square method to adjust the structure and parameters of the neural network. The neural network adopts the membership function and the non-membership function based on the beta function as the forepart of the fuzzy rule, and adopts the random vectorfunction to connect the neural network as the afterpart of the interval fuzzy rule. The IT2IF-RVFLNN designed by the invention can achieve global approximation.

The preparation of silver quantum clusters embedded in organic-templated-boehmite-nanoarchitecture (OTBN) and its use as a sensor for quantity of water flow measured by change of color in visible light upon flow of contaminated water have been provided. Silver quantum clusters-embedded OTBN are highly luminescent. Since the quantum clusters are embedded in the matrix, they are highly stable over a long period of time. The composition described here is utilized in the form of a device for ‘visible/ultraviolet light color change-based detection’ upon passage of water through a water purification device. Upon interaction with ions present in water, luminescent silver clusters undergo chemical transformation to Ag₂S nanoparticles. The transformation is reflected in the form of visible color change (from pink to black) and luminescence quenching (from red emission to negligible luminescence).

The invention discloses a stealth target detecting device and method. The method comprises the steps that a pure-state entangled quantum cluster is prepared; a known quantum state is modulated, bell-state measurement is conducted on the known quantum state and one quantum in the pure-state entangled quantum cluster, at this point, other quanta change into collapsed quantum states, and a classicalinformation piece is obtained; an arbitrary quantum of the collapsed quantum states is emitted into a target airspace, decoherence happens to the detection quantum, U-transformation is conducted on one quantum of other collapsed quantum states and the classical information piece, an obtained state is not known quantum states, and according to emission time of the probe quantum and U-transformationtime, the speed and orientation of a stealth target are calculated. According to the device and the method, based on the characteristic that decoherence is caused to the entangled-state quantum due to environmental interference, detection of the stealth target is achieved; the better the performance of a target microwave absorbing material and a target plasma shielding material is, the higher thedetection precision is; the problem is solved that backward signals of traditional quantum radars get weaker when the performance of the microwave absorbing material and the plasma shielding materialis improved continuously, so that the detection distance keeps getting shorter.

A method for making a solid state light emitting device includes: (a) forming a first cladding layer on a substrate; (b) forming a matrix layer above the first cladding layer, the matrix layer having a top surface and being formed with a plurality of isolated spaces; (c) epitaxially forming a quantum cluster in each of the spaces such that the top surface of the matrix layer and top surfaces of the quantum clusters cooperatively define a coplanar surface, the quantum clusters cooperating with the matrix layer to form a light emitting layer; (d) forming a second cladding layer on the light emitting layer; and (e) forming an electrode unit electrically connected to the first and second cladding layers.

The present invention relates to a new method for preparing anisotropic metal nanoparticles with high aspect ratios and different types of structures by means of catalysis by Atomic Quantum Clusters (AQCs).

The invention discloses a quantum clustering method based on a machine learning framework and a related device, and the method comprises the steps: building a quantum machine learning clustering model based on an obtained sample data set and k initial clustering centers through calling a quantum module, and operating the quantum machine learning clustering model to obtain output data; and then, a data structure module is called to determine the similarity between each piece of sample data in a sample data set and the k initial clustering centers based on the output data, so that comparison of distances between data in a classical clustering algorithm is converted into comparison of similarity between quantum states of quantum bits, and the similarity between the quantum states of the quantum bits is obtained by utilizing the property of quantum superposition. The occupancy rate of a classical k-means algorithm on computing resources is reduced; and finally, determining a target cluster to which each piece of sample data belongs based on the similarity, so that the speed of a clustering algorithm is improved.

The invention provides a quantum clustering method based on a nearest neighbor KNN and an improved wave function, and the method comprises the steps: obtaining original data of a group of to-be-classified sample points, carrying out the normalization of the original data, determining the input parameters of a quantum clustering model based on the nearest neighbor KNN, calculating the wave function parameters of all sample points, and carrying out the calculation of the wave function parameters of all sample points; the wave function parameters comprise the steps of calculating scale parameters and shape parameters of distribution obeyed by wave functions, calculating potential energy surfaces of quantum clustering, and determining the classification number and classification boundaries according to the calculated potential energy surfaces. The method provided by the invention inherits all advantages of a quantum clustering method, is more suitable for classifying data obeying Weibull distribution, provides a new choice for data classification, does not need to manually give any input parameter and does not need to give a classification label of sample data at the same time, and can be applied to the field of data classification. The input parameters of the quantum clustering model can be calculated, the practicability is high, and the accuracy is high.