Papers by Mario Mastriani
arXiv (Cornell University), Aug 3, 2020
In this paper, five techniques, for the representation of a digital image inside a quantum proces... more In this paper, five techniques, for the representation of a digital image inside a quantum processor, are compared. The techniques are: flexible representation of quantum images (FRQI), novel enhanced quantum representation (NEQR), generalized quantum image representation (GQIR), multi-channel representation for quantum images (MCQI), and quantum Boolean image processing (QBIP). The comparison will be based on implementations on the Quirk simulator, and on the IBM Q Experience processors, from the point of view of performance, robustness (noise immunity), deterioration of the outcomes due to decoherence, and technical viability.
Scientific Reports, Dec 10, 2023
arXiv (Cornell University), Jul 7, 2022
Optical and Quantum Electronics, 2021
This paper presents a protocol for a relay configuration of one quantum CubeSat and two quantum d... more This paper presents a protocol for a relay configuration of one quantum CubeSat and two quantum drones positioned at distant places over the Earth, where: (a) an entangled pair is generated and distributed by the CubeSat between both drones located over the clouds, (b) each drone descends through the clouds with its respective entangled photon, and (c) each drone generates a new entangled photon pair, conserves one and distributes the other one to Mobile Ground Stations (MGS). These latter photons allow to teleport both CubeSat’s entangled photons to the MGSs. Once on Earth, the CubeSat’s entangled photons constitute a bridge for the teleportation of an arbitrary qubit among the MGSs. In this way, we solve the main problem of all quantum communication between a satellite and the Earth: the weather as well as unfavorable environmental conditions. Finally, this paper evaluates the performance of the protocol, which first teleports the CubeSat’s entangled photons and thanks to these the definitive desired qubit, with implementations on the 16-qubit Melbourne processor of IBM Q Experience, where this evaluation constitutes the first stage of a project that tries to communicate two distant points of the earth at any time regardless of the weather.
ArXiv, 2016
This paper introduces an Enhanced Boolean version of the Correlation Matrix Memory (CMM), which i... more This paper introduces an Enhanced Boolean version of the Correlation Matrix Memory (CMM), which is useful to work with binary memories. A novel Boolean Orthonormalization Process (BOP) is presented to convert a non-orthonormal Boolean basis, i.e., a set of non-orthonormal binary vectors (in a Boolean sense) to an orthonormal Boolean basis, i.e., a set of orthonormal binary vectors (in a Boolean sense). This work shows that it is possible to improve the performance of Boolean CMM thanks BOP algorithm. Besides, the BOP algorithm has a lot of additional fields of applications, e.g.: Steganography, Hopfield Networks, Bi-level image processing, etc. Finally, it is important to mention that the BOP is an extremely stable and fast algorithm.
EPJ Quantum Technology
In this study, a new version of the quantum teleportation protocol is presented, which does not r... more In this study, a new version of the quantum teleportation protocol is presented, which does not require a Bell state measurement (BSM) module on the sender side (Alice), a unitary transform to reconstruct the teleported state on the receiver side (Bob), neither a disambiguation process through two classic bits that travel through a classic disambiguation channel located between sender and receiver. The corresponding theoretical deduction of the protocol, as well as the experimental verification of its operation for several examples of qubits through implementation on an optical table, complete the present study. Both the theoretical and experimental outcomes show a marked superiority in the performance of the new protocol over the original version, with more simplicity and lower implementation costs, and identical fidelity in its most complete version.
arXiv (Cornell University), Oct 11, 2016
A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is present... more A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it's based on Schrödinger equation, which is a partial differential equation that describes how the quantum state of a non-relativistic physical system changes with time. In classic world is named frequency in time (FIT), which is presented here in opposition and as a complement of traditional spectral analysis frequency-dependent based on Fourier theory. Besides, FIT is a metric, which assesses the impact of the flanks of a signal on its frequency spectrum, which is not taken into account by Fourier theory and even less in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages: a) compact support with excellent energy output treatment, b) low computational cost, O(N) for signals and O(N 2) for images, c) it does not have phase uncertainties (indeterminate phase for magnitude = 0) as Discrete and Fast Fourier Transform (DFT, FFT, respectively), d) among others. In fact, FIT constitutes one side of a triangle (which from now on is closed) and it consists of the original signal in time, spectral analysis based on Fourier Theory and FIT. Thus a toolbox is completed, which it is essential for all applications of Digital Signal Processing (DSP) and Digital Image Processing (DIP); and, even, in the latter, FIT allows edge detection (which is called flank detection in case of signals), denoising, despeckling, compression, and superresolution of still images. Such applications include signals intelligence and imagery intelligence. On the other hand, we will present other DIP tools, which are also derived from the Schrödinger equation. Besides, we discuss several examples for spectral analysis, edge detection, denoising, despeckling, compression and superresolution in a set of experimental results in an important section on Applications and Simulations, respectively. Finally, we finish this work with special section dedicated to Conclusions.
arXiv (Cornell University), Jul 16, 2016
The Gram-Schmidt Process (GSP) is used to convert a non-orthogonal basis (a set of linearly indep... more The Gram-Schmidt Process (GSP) is used to convert a non-orthogonal basis (a set of linearly independent vectors, matrices, etc) into an orthonormal basis (a set of orthogonal, unit-length vectors, bi or tri dimensional matrices). The process consists of taking each array and then subtracting the projections in common with the previous arrays. This paper introduces an enhanced version of the Gram-Schmidt Process (EGSP) with inverse, which is useful for Digital Signal and Image Processing, among others applications.
arXiv (Cornell University), Jul 11, 2016
We describe a novel method for removing speckle (in wavelet domain) of unknown variance from SAR ... more We describe a novel method for removing speckle (in wavelet domain) of unknown variance from SAR images. The method is based on the following procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the speckled image, 2) scaling and rounding to the coefficients of the highest subbands (to obtain integer and positive coefficients), 3) bit-slicing to the new highest subbands (to obtain bit-planes), 4) then we apply the Systholic Boolean Orthonormalizer Network (SBON) to the input bit-plane set and we obtain two orthonormal output bit-plane sets (in a Boolean sense), we project a set on the other one, by means of an AND operation, and then, 5) we apply reassembling , and, 6) re-scaling. Finally, 7) we apply Inverse DWT-2D and reconstruct a SAR image from the modified wavelet coefficients. Despeckling results compare favorably to the most of methods in use at the moment.
This paper introduces a Quantum Correlation Matrix Memory (QCMM) and Enhanced QCMM (EQCMM), which... more This paper introduces a Quantum Correlation Matrix Memory (QCMM) and Enhanced QCMM (EQCMM), which are useful to work with quantum memories. A version of classical Gram-Schmidt orthogonalisation process in Dirac notation (called Quantum Orthogonalisation Process: QOP) is presented to convert a non-orthonormal quantum basis, i.e., a set of non-orthonormal quantum vectors (called qudits) to an orthonormal quantum basis, i.e., a set of orthonormal quantum qudits. This work shows that it is possible to improve the performance of QCMM thanks QOP algorithm. Besides, the EQCMM algorithm has a lot of additional fields of applica-tions, e.g.: Steganography, as a replacement Hopfield Net-works, Bi-level image processing, etc. Finally, it is important to mention that the EQCMM is an extremely easy to implement in any firmware.
We describe a novel method for removing noise (in wavelet domain) of unknown variance from microa... more We describe a novel method for removing noise (in wavelet domain) of unknown variance from microarrays. The method is based on a smoothing of the coefficients of the highest subbands. Specifically, we decompose the noisy microarray into wavelet subbands, apply smoothing within each highest subband, and reconstruct a microarray from the modified wavelet coefficients. This process is applied a single time, and exclusively to the first level of decomposition, i.e., in most of the cases, it is not necessary a multirresoltuion analysis. Denoising results compare favorably to the most of methods in use at the moment.
Super-resolution is nowadays used for a high-resolution image produced from several low-resolutio... more Super-resolution is nowadays used for a high-resolution image produced from several low-resolution noisy frames. In this work, we consider the problem of high-quality interpolation of a single noise-free image. Such images may come from different sources, i.e., they may be frames of videos, individual pictures, etc. On the other hand, in the encoder we apply a downsampling via bidimen-sional interpolation of each frame, and in the decoder we apply a upsampling by which we restore the original size of the image. If the compression ratio is very high, then we use a convolutive mask that restores the edges, eliminating the blur. Finally, both, the encoder and the complete decoder are implemented on General-Purpose computation on Graphics Processing Units (GPGPU) cards. In fact, the mentioned mask is coded inside texture memory of a GPGPU.
In this work, we present a comparison between different techniques of image compression. First, t... more In this work, we present a comparison between different techniques of image compression. First, the image is divided in blocks which are organized according to a certain scan. Later, several compression techniques are applied, combined or alone. Such techniques are: wavelets (Haar's basis), Karhunen-Loève Transform, etc. Simulations show that the combined versions are the best, with minor Mean Squared Error (MSE), and higher Peak Signal to Noise Ratio (PSNR) and better image quality, even in the presence of noise.
In this work, we developed the concept of supercompression, i.e., compression above the compressi... more In this work, we developed the concept of supercompression, i.e., compression above the compression standard used. In this context, both compression rates are multiplied. In fact, supercompression is based on super-resolution. That is to say, supercompression is a data compression technique that superpose spatial image compression on top of bit-per-pixel compression to achieve very high compression ratios. If the compression ratio is very high, then we use a convolutive mask inside decoder that restores the edges, eliminating the blur. Finally, both, the encoder and the complete decoder are implemented on General-Purpose computation on Graphics Processing Units (GPGPU) cards. Specifically, the mentio-ned mask is coded inside texture memory of a GPGPU.
In this work, we present a comparison between two techniques of image compression. In the first c... more In this work, we present a comparison between two techniques of image compression. In the first case, the image is divided in blocks which are collected according to zig-zag scan. In the second one, we apply the Fast Cosine Transform to the image, and then the transformed image is divided in blocks which are collected according to zig-zag scan too. Later, in both cases, the Karhunen-Loève transform is applied to mentioned blocks. On the other hand, we present three new metrics based on eigenvalues for a better comparative evaluation of the techniques. Simulations show that the combined version is the best, with minor Mean Absolute Error (MAE) and Mean Squared Error (MSE), higher Peak Signal to Noise Ratio (PSNR) and better image quality. Finally, new technique was far superior to JPEG and JPEG2000.
This work deals with unsupervised image deblurring. We present a new deblurring procedure on imag... more This work deals with unsupervised image deblurring. We present a new deblurring procedure on images provided by lowresolution synthetic aperture radar (SAR) or simply by multimedia in presence of multiplicative (speckle) or additive noise, respectively. The method we propose is defined as a two-step process. First, we use an original technique for noise reduction in wavelet domain. Then, the learning of a Kohonen self-organizing map (SOM) is performed directly on the denoised image to take out it the blur. This technique has been successfully applied to real SAR images, and the simulation results are presented to demonstrate the effectiveness of the proposed algorithms.
This paper introduces a Quantum Correlation Matrix Memory (QCMM) and Enhanced QCMM (EQCMM), which... more This paper introduces a Quantum Correlation Matrix Memory (QCMM) and Enhanced QCMM (EQCMM), which are useful to work with quantum memories. A version of classical Gram-Schmidt orthogonalisation process in Dirac notation (called Quantum Orthogonalisation Process: QOP) is presented to convert a non-orthonormal quantum basis, i.e., a set of non-orthonormal quantum vectors (called qudits) to an orthonormal quantum basis, i.e., a set of orthonormal quantum qudits. This work shows that it is possible to improve the performance of QCMM thanks QOP algorithm. Besides, the EQCMM algorithm has a lot of additional fields of applications, e.g.: Steganography, as a replacement Hopfield Networks, Bilevel image processing, etc. Finally, it is important to mention that the EQCMM is an extremely easy to implement in any firmware.
The Gram-Schmidt Process (GSP) is used to convert a non-orthogonal basis (a set of linearly indep... more The Gram-Schmidt Process (GSP) is used to convert a non-orthogonal basis (a set of linearly independent vectors) into an orthonormal basis (a set of orthogonal, unit-length vectors). The process consists of taking each vector and then subtracting the<br> elements in common with the previous vectors. This paper introduces an Enhanced version of the Gram-Schmidt Process (EGSP) with inverse, which is useful for signal and image processing applications.
IET Quantum Communication, 2021
This study establishes, for the first time in literature, that quantum entanglement, as well as t... more This study establishes, for the first time in literature, that quantum entanglement, as well as the most important protocol derived from it, that is, quantum teleportation, completely rest on the Quantum Fourier Transform. This highlights the spectral nature of both protocols with important consequences on quantum computing, quantum communications, and quantum cryptography, with a particular emphasis on quantum Internet. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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Papers by Mario Mastriani