Abstract: This invention relates to a method of building a hybrid quantum-classical computing network, comprising: a first step of transformation of an application composed of services into a Petri net including both Petri places (8, 9) and Petri transitions (81, 82, 91-94) between said Petri places (8, 9), any said Petri place (8, 9) corresponding to: either a first type building block corresponding to any quantum processing unit (8) which processes a job into a result, or a second type building block corresponding to any plugin unit (9), which converts a job into another job and/or a result into another result, any Petri transition (81, 82, 91-94) corresponding to any link between two building blocks (8, 9), all said links (81, 82, 91-94) being formatted so as to make any building block (8, 9) interchangeable, a second step of transformation of said Petri net into a hybrid quantum-classical computing network, replacing any building block by its corresponding unit (8, 9), interconnecting all said corresponding units (

Abstract: The present disclosure relates to a computing system for executing hybrid programs, said computing system comprising: hardware resources comprising quantum computing resources and classical computing resources, said quantum computing resources comprising one or more quantum computers; software resources to be executed on the hardware resources; wherein the software resources comprise a plurality of processing modules comprising interfaces of two possible types referred to as upstream interface and downstream interface, wherein said plurality of processing modules comprises: at least one quantum processing module for each quantum computer, wherein each quantum processing module comprises an upstream interface; a plurality of plugin modules, wherein each plugin module comprises both an upstream interface and a downstream interface; wherein a hybrid program is built by connecting at least one plugin module and one quantum processing module.

Abstract: The present disclosure relates to a computing system comprising a classical computer, an analog quantum computer and a digital quantum computer, said computing system comprising: a digital quantum processing, DQP, module comprising an input interface for receiving a quantum circuit to be executed by the digital quantum computer; an analog quantum processing, AQP, module comprising an input interface for receiving a temporal schedule to be executed by the analog quantum computer; a digital to analog converting, DAC, module comprising an input interface for receiving a quantum circuit and an output interface for outputting a temporal schedule; wherein a same format is used on the input interfaces of both the DQP module and the DAC module, and a same format is used on both the output interface of the DAC module and the input interface of the AQP module.

Abstract: This invention relates to a method of building a hybrid quantum-classical computing network, comprising: a first step of transformation of an application composed of services into a Petri net including both Petri places (8, 9) and Petri transitions (81, 82, 91-94) between said Petri places (8, 9), any said Petri place (8, 9) corresponding to: either a first type building block corresponding to any quantum processing unit (8) which processes a job into a result, or a second type building block corresponding to any plugin unit (9), which converts a job into another job and/or a result into another result, any Petri transition (81, 82, 91-94) corresponding to any link between two building blocks (8, 9), all said links (81, 82, 91-94) being formatted so as to make any building block (8, 9) interchangeable, a second step of transformation of said Petri net into a hybrid quantum-classical computing network, replacing any building block by its corresponding unit (8, 9), interconnecting all said corresponding units (

Abstract: A Method for simulating, on a computer processing unit including a semiconductor integrated circuit, the operation of a quantum circuit model, which includes the operations of: dividing the quantum circuit into d adjacent layers Lk intended to be successively traversed by the n qubits taken together, each layer including a single quantum gate Gk; and assigning a type to each quantum gate Gk of the circuit, among three predefined types of quantum gates. The three types are: Diagonal type gate, for which the transfer matrix is diagonal; Conventional type gate, for which the transfer matrix is non-diagonal and includes operators having a value of 0 or 1, with only one operator per row and per column; and Dense type gate, which is neither conventional nor diagonal in type.

Abstract: A Method for simulating, on a computer processing unit including a semiconductor integrated circuit, the operation of a quantum circuit model, which includes the operations of: dividing the quantum circuit into d adjacent layers Lk intended to be successively traversed by the n qubits taken together, each layer including a single quantum gate Gk; and assigning a type to each quantum gate Gk of the circuit, among three predefined types of quantum gates. The three types are: Diagonal type gate, for which the transfer matrix is diagonal; Conventional type gate, for which the transfer matrix is non-diagonal and includes operators having a value of 0 or 1, with only one operator per row and per column; and Dense type gate, which is neither conventional nor diagonal in type.

Abstract: The present invention relates to a method of automatically recognizing fingerprints, consisting in establishing a database by digitizing images of fingerprints of individuals, by detecting the corresponding minutiae, by selecting the most discriminating minutiae, by storing the characteristic parameters of these minutiae, then, in the step for recognizing prints of a given individual, in digitizing the fingerprints of this individual, in detecting the minutiae of these fingerprints, in storing their characteristic parameters, in comparing these parameters with those stored in the database, and it is characterized in that, on establishing the database and on taking prints of said given individual, for each print in the database and of the individual concerned, at least the spectra of the selected minutiae are stored as characteristic parameters of the minutiae, and in that, after comparison of the characteristic parameters of the prints of the individual with the corresponding parameters of the prints in the d

Abstract: The present invention relates to a method of automatically recognizing fingerprints, consisting in establishing a database by digitizing images of fingerprints of individuals, by detecting the corresponding minutiae, by selecting the most discriminating minutiae, by storing the characteristic parameters of these minutiae, then, in the step for recognizing prints of a given individual, in digitizing the fingerprints of this individual, in detecting the minutiae of these fingerprints, in storing their characteristic parameters, in comparing these parameters with those stored in the database, and it is characterized in that, on establishing the database and on taking prints of said given individual, for each print in the database and of the individual concerned, at least the spectra of the selected minutiae are stored as characteristic parameters of the minutiae, and in that, after comparison of the characteristic parameters of the prints of the individual with the corresponding parameters of the prints in the d

Abstract: To reduce the fluoroscopic noise in an image I acquired at a date t, the pixels of this image are paired with the pixels of an image I? acquired at a date t?1. For a pixel with coordinates (x,y) of the image I, a convolution is done with a core U equivalent to a low-pass filter whose coefficients have been modified as a function of the neighborhood of the pixel with coordinates (x,y) in the image I. For the pixel paired in the image I?, a convolution is done with the core U whose coefficients have been modified as a function of the neighborhood of the pixel with coordinates (x,y) in the image I?. The result of the two convolutions is associated linearly in order to obtain a filtered value for the pixel with coordinates (x,y). These operations are repeated for each pixel of the image I.

Abstract: In order to model a fluoroscopic noise present in a radiography operation, two successive images of a same zone are used so that it is possible to pair the dots of the two images as a function of the zone of the space that they represent. The pairs of dots are grouped in sub-groups according to their gray level. For each sub-group, the mean standard deviation ? of the Pi(x, y)?Pi?1(x, y) values is computed. A sub-group is discriminated by eliminating the dots for which Pi(x, y)?Pi?1(x, y) is greater than the mean of the values Pi(x, y)?Pi?1(x, y) plus k times the mean standard deviation. These computations are repeated a certain number of times. Once the sub-group is discriminated, its centering is assessed. A sub-group is non-centered if its mean is greater than 1.5 times its mean standard deviation. Pairs of dots (v, ?) are then obtained. From these dots, an iterative regression is performed to obtain a model of noise according to ?(v)=?.?v+?.

Abstract: The invention includes processing an image belonging to a sequence of at least two images displaying a surface representing an organ or part of an organ which is deformable over time and called the organ surface. The organ surface includes characteristic points (marking points) which correspond to each other from one image to another in the sequence. The method includes defining a structure per unit length whose deformation is followed on an image, calculating the positions of the marking points and determining the parameters of a mathematical expression of the deformation of the organ observed between the two images. The determination is carried out from positions of a set of marking points on the two images. The expression is applied to the structure per unit length to define the form of the structure per unit length after deformation of the organ between the two images.

Abstract: A method of obtaining a three-dimensional deformation of an organ which is deformable over time and extends in a three-dimensional space from at least two sets of data (DS(t1), DS(t2)) representing points of said organ and corresponding to distinct times (t1, t2) in the deformation of the organ. A correspondence (SEL(t1,t2)) between the points in the sets of data being determined (COR), said method uses a definition (DEF) of notional planes (Pi) on which there are defined explicit equations (EQi) of the deformation of the organ including unknown parameters (PARi(t1, t2)). The parameters (PARi(t1, t2)) are calculated (CAL) for each equation (EQi) and the explicit equations (Esp.) obtained are then utilized (EXT) in order to define the deformation in the three-dimensional space (DEF3D(t1,t2)) by weighting functions defined for the points in the space.

Abstract: An image processing method of accurately fully automatically detecting Tag Points (16) of a tagged image (10) of a sequence of MRI tagged images (for example, SPAMM protocol) comprises the steps of estimating (13) optimum value points of the intensity profile; labeling said points as Candidate Points (14) of a tag; automatically constructing (18) a Predicted Image (17) from determined Tags equations (19) of at least a preceding image of the sequence and from spatial and temporal parameters; detecting (15) Tag Points (16) among Candidate Points (14), using characteristics of the constructed Predicted Image (17); determining (20) Tag equations (21) from detected Tag Points (16), said Tag equations (21) intended to be used in the construction (18) of at least another Predicted Image for a next image of the sequence. The method further allows assigning a Tag Point to a specific Tag whatever the temporal resolution, this feature allowing the tracking of Tags from one image to the next of the sequence.

Abstract: To make the settings for an X-ray installation, so that the images that it reveals have the greatest possible contrast, a measurement is made of a mean equivalent thickness of the body of a patient being examined from a test image. However, as a preliminary, the test image is rid of those pixels for which it is known, a priori, that their significance does not comprise any interesting gray levels. The dynamic range of the image can be set objectively by choosing the thickness threshold and the equivalent mean thickness as a given proportion of the dynamic range. Preferably, the computation and the setting are done on the fly, in real time after the acquisition of the test image.

Abstract: To reduce the fluoroscopic noise in an image I acquired at a date t, the pixels of this image are paired with the pixels of an image I′ acquired at a date t−1. For a pixel with coordinates (x,y) of the image I, a convolution is done with a core U equivalent to a low-pass filter whose coefficients have been modified as a function of the neighborhood of the pixel with coordinates (x,y) in the image I. For the pixel paired in the image I′, a convolution is done with the core U whose coefficients have been modified as a function of the neighborhood of the pixel with coordinates (x,y) in the image I′. The result of the two convolutions is associated linearly in order to obtain a filtered value for the pixel with coordinates (x,y). These operations are repeated for each pixel of the image I.

Abstract: In order to model a fluoroscopic noise present in a radiography operation, two successive images of a same zone are used so that it is possible to pair the dots of the two images as a function of the zone of the space that they represent. The pairs of dots are grouped in sub-groups according to their gray level. For each sub-group, the mean standard deviation &sgr; of the Pi(x, y)−Pi−1(x, y) values is computed. A sub-group is discriminated by eliminating the dots for which Pi(x, y)−Pi−1(x, y) is greater than the mean of the values Pi(x, y)−Pi−1(x, y) plus k times the mean standard deviation. These computations are repeated a certain number of times. Once the sub-group is discriminated, its centering is assessed. A sub-group is non-centered if its mean is greater than 1.5 times its mean standard deviation. Pairs of dots (v, &sgr;) are then obtained. From these dots, an iterative regression is performed to obtain a model of noise according to &sgr;(v)=&agr;.

Abstract: The invention relates to a method of obtaining a three-dimensional deformation of an organ which is deformable over time and extends in a three-dimensional space from at least two sets of data [DS(t1), DS(t2)] representing points of said organ and corresponding to distinct times [t1, t2] in the deformation of the organ. A correspondence [SEL(t1,t2)] between the points in the sets of data being determined [COR], said method uses a definition [DEF] of notional planes [Pi] on which there are defined explicit equations [EQi] of the deformation of the organ including unknown parameters [PARi(t1,t2)].

Abstract: The invention relates to a processing method for images of a sequence of at least two images IM(t1) and IM(t2) having a surface which is representative of an organ or a part of an organ which is deformable over time and which is referred to as the organ surface, said surface including characteristic points, denoted marked points MP, which correspond to each other from one image to another in the sequence. The method includes a step CALC of calculation of the positions of the marked points MP(t1) and MP(t2), a step DET of determining parameters of an explicit mathematical function f(t1/t2) of the deformation of the organ observed between the two images. Said determining step is carried out from positions of a group MP′ of marked points in the two images. Moreover, the invention proposes practical tools to follow the deformation and its possible pathologic abnormalities.

Abstract: The invention relates to a method of processing an image belonging to a sequence of at least two images IM(t1), IM(t2) having a surface representing an organ or part of an organ deformable over time and called the organ surface, said surface including characteristic points, denoted marking points MP, which correspond to each other from one image to another in the sequence. The method includes a step DEF of defining, on an image IM(t1), a structure per unit length whose deformation is to be followed, LS(t1), a step CALC of calculating the positions of the marking points MP(t1) and MP(t2), and a step DET of determining the parameters of an explicit mathematical expression f(t1/t2) of the deformation of the organ observed between the two images. Said determination is carried out from the positions of a set MP′ of marking points on the two images.

Abstract: An image processing method of accurately fully automatically detecting Tag Points (16) of a tagged Image (10) of a sequence of MRI tagged images (for example, SPAMM protocol) comprises the steps of estimating (13) optimum value points of the intensity profile; labeling said points as Candidate Points (14) of a tag; automatically constructing (18) a Predicted Image (17) from determined Tags equations (19) of at least a preceding image of the sequence and from spatial and temporal parameters; detecting (15) Tag Points (16) among Candidate Points (14), using characteristics of the constructed Predicted Image (17); determining (20) Tag equations (21) from detected Tag Points (16), said Tag equations (21) intended to be used in the construction (18) of at least another Predicted Image for a next image of the sequence. The method further allows to assign Tag Point to a specific Tag whatever the temporal resolution, this feature allowing the tracking of Tags from one image to the next of the sequence.