Deep learning, also known as DL, holds great potential within the field of artificial intelligence. Fast problem-solving approaches are widely used in quantum computing. Large multidimensional space is utilized to cat...
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Deep learning, also known as DL, holds great potential within the field of artificial intelligence. Fast problem-solving approaches are widely used in quantum computing. Large multidimensional space is utilized to categorize and address intricate problems. The different algorithms have the ability to interact in a space with multiple dimensions and find solutions to the problems. quantum deep learning facilitates different mining procedures by incorporating precise advancements in quantum computing. Prompt and accurate identification during the early stages of progression is crucial for various severe and life-threatening illnesses like cancer, hepatotoxicity, cardio toxicity, nephrotoxicity, and others. Currently, there is a critical need to create rapid, precise, and highly effective approaches for predicting different diseases. These methods should also be feasible and nonintrusive. Dementia, a highly hazardous condition, has a significant impact on the human nervous system. Dementia often includes Parkinson's as one of its prominent symptoms. The patient's entire operational behavior will be impacted. The proposed system is utilizing machine learning and quantum computing to develop a method for predicting Parkinson's disease based on speech signals. quantum computers can be used to assist in identifying cancer by using a hybrid quantum-classical convolution neural network (QCCNN). This network is inspired by convolution neural networks (CNNs) but has been modified for quantum computing in order to improve the process of mapping features. Dimensionality reduction algorithms, principal component analysis (PCA) are applied to the preprocessed dataset to make predictions about diseases. The standard dataset from UCI machine learning repository will be used to determine the performance of the model. Ensemble models exceed the precision of highly accurate techniques such as neural networks. To demonstrate the superior detection capability of our model, we have compar
With the rapid evolution of quantum computing, digital quantum simulations are essential for quantum algorithm verification, quantum error analysis, and new quantum applications. However, the exponential increase in m...
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With the rapid evolution of quantum computing, digital quantum simulations are essential for quantum algorithm verification, quantum error analysis, and new quantum applications. However, the exponential increase in memory overhead and operation time is challenging issues that have not been solved for years. We propose a novel approach that provides more qubits and faster quantum operations with smaller memory than before. Our method selectively tracks realized quantum states using a reduced quantum state representation scheme instead of loading the entire quantum states into memory. This method dramatically reduces memory space ensuring fast quantum computations without compromising the global quantum states. Furthermore, our empirical evaluation reveals that our proposed idea outperforms traditional methods for various algorithms. We verified that the Grover algorithm supports up to 55 qubits and the surface code algorithm supports up to 85 qubits in 512 GB memory on a single computational node, which is against the previous studies that support only between 35 qubits and 49 qubits.
The last five years have seen a dramatic evolution of platforms for quantum computing, taking the field from physics experiments to quantum hardware and software engineering. Nevertheless, despite this progress of qua...
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Optimization problems, which involve finding the best solution from a set of possible solutions, are ubiquitous in various fields, from finance to engineering. Traditional algorithms sometimes struggle with these prob...
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Biometric authentication systems identify or verify a person from a digital image taken by security cameras or fingerprint readers. Digital images are used for authentication wherever a security system exists, such as...
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Biometric authentication systems identify or verify a person from a digital image taken by security cameras or fingerprint readers. Digital images are used for authentication wherever a security system exists, such as in airports and banks. Although biometric data authentication boosts security, it has several practical challenges and is a difficult problem in computer vision. Another application classifies biometric data according to certain characteristics such as age, gender, or race. One of the biometric data frequently used for this purpose and has become very important is face images. Deep learning systems can learn rich, compact representations of faces from very big face datasets, allowing people to surpass their facial analysis talents. The Convolutional Neural Network (CNN) has recently obtained very promising face analysis results among these methods. Although CNN has the beneficial use of the data's correlation information, it has trouble learning efficiently when the supplied amount of the data or model is too huge. quantum Convolutional Neural Network (QCNN) provides a new solution to a CNN-related problem using a quantum computing environment. In this study, gender recognition is performed with CNN and QCNN algorithms, and the results are compared in terms of time and accuracy. The purpose of the study is to show the comparative evaluation of QCNN and its classical counterpart CNN algorithms with detailed applications under the same conditions. 92% accuracy for QCNN and 90% accuracy for CNN are obtained. The total processing time is 128.85 s for QCNN and 832.30 s for CNN.
quantum programs allow to process multiple bits of information at the same time, which is useful in multidimensional data handling. Images are an example of such multidimensional data. Our work reviews 14 quantum imag...
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quantum programs allow to process multiple bits of information at the same time, which is useful in multidimensional data handling. Images are an example of such multidimensional data. Our work reviews 14 quantum image encoding works and compares implementations of 8 of them by 3 metrics: number of utilized qubits, quantum circuit depth, and quantum volume. Our work includes a practical comparison of 2(n) x 2(n) images encoding, where n varies from 1 up to 8. We observed that Qubit Lattice approach shows the minimum circuit depth as well as quantum volume, Flexible Representation of quantum Images (FRQI) utilizes the minimum number of qubits. If to talk about variety of processing techniques, FRQI and Novel Enhanced quantum Representation (NEQR) representations are the most fruitful. As far as quantum computers are limited in qubit number, we concluded that almost all approaches except Qubit Lattice are promising for the near future of quantum image representation and processing. From the point of view of the quantum depth, discrete methods showed the most appropriate result.
We present quantum Register Algebra (QRA) as an efficient tool for quantum computing. We show the direct link between QRA and Dirac formalism. We present Geometric Algebra algorithms Optimizer (GAALOP) implementation ...
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We present quantum Register Algebra (QRA) as an efficient tool for quantum computing. We show the direct link between QRA and Dirac formalism. We present Geometric Algebra algorithms Optimizer (GAALOP) implementation of our approach. We demonstrate the ability to fully describe and compute with QRA in GAALOP using the geometric product.
To perform reliable informationprocessing in quantum computers, quantum error correction (QEC) codes are essential for the detection and correction of errors in the qubits. Among QEC codes, topological QEC codes are ...
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To perform reliable informationprocessing in quantum computers, quantum error correction (QEC) codes are essential for the detection and correction of errors in the qubits. Among QEC codes, topological QEC codes are designed to interact between the neighboring qubits, which is a promising property for easing the implementation requirements. In addition, the locality to the qubits provides unusual tolerance to local errors. Recently, various decoding algorithms based on machine learning have been proposed to improve the decoding performance and latency of QEC codes. In this work, we propose a new decoding algorithm for surface codes, i.e., a type of topological codes, by using convolutional neural networks (CNNs) tailored for the topological lattice structure of the surface codes. In particular, the proposed algorithm takes advantage of the syndrome pattern, which is represented as a part of a rectangular lattice given to the CNN as its input. The remaining part of the rectangular lattice is filled with a carefully selected incoherent value for better logical error rate performance. In addition, we introduce how to optimize the hyperparameters in the CNN, according to the lattice structure of a given surface code. This reduces the overall decoding complexity and makes the CNN-based decoder computationally more suitable for implementation. The numerical results show that the proposed decoding algorithm effectively improves the decoding performance in terms of logical error rate as compared to the existing algorithms on various quantum error models.
In the field of informationprocessing, Walsh-Hadamard transforms (WHTs) are exten-sively employed. However, there exists a dearth of WHT algorithms based on sequence ordering that can be employed for quantum informat...
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In the field of informationprocessing, Walsh-Hadamard transforms (WHTs) are exten-sively employed. However, there exists a dearth of WHT algorithms based on sequence ordering that can be employed for quantuminformationprocessing. This paper presents a pioneering attempt to address this issue by proposing a recursive circuit for quantum version of WHT (QWHT) based on sequence ordering, which can effectively process discrete signals, images, and videos. To provide a more intuitive representation of the sequency ordering QWHT, we design quantum circuits to implement both the QWHT and its inverse transform. Furthermore, simulation results on the IBM platform demonstrate the effectiveness of the algorithm. Meanwhile, comparative analysis reveals that the proposed sequency ordering QWHT approach is superior to the equivalent quantum version of the conventional WHT algorithm. Based on the complexity analysis, our methods are exponentially faster than traditional WHTs. & COPY;2023 Elsevier B.V. All rights reserved.
Ray tracing algorithm is a category of rendering algorithms that calculate pixel colors by simulating light rays in parallel. The quantum supersampling has achieved a quadratic speedup over classical Monte Carlo metho...
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Ray tracing algorithm is a category of rendering algorithms that calculate pixel colors by simulating light rays in parallel. The quantum supersampling has achieved a quadratic speedup over classical Monte Carlo method, but its output image contains many detached abnormal noisy dots. In this paper, we improve quantum supersampling by replacing the QFT-based phase estimation in quantum supersampling with a robust quantum counting scheme. We do simulation experiments to show that the quantum ray tracing with improved quantum supersampling does perform better than classical path tracing algorithm as well as the original form of quantum supersampling.
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