The generalized eigenvalue (GE) problems are of particular importance in various areas of science engineering and machine learning. We present a variational quantum algorithm for finding the desired generalized eigenv...
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The generalized eigenvalue (GE) problems are of particular importance in various areas of science engineering and machine learning. We present a variational quantum algorithm for finding the desired generalized eigenvalue of the GE problem, A vertical bar psi = lambda B vertical bar psi , by choosing suitable loss functions. Our approach imposes the superposition of the trial state and the obtained eigenvectors with respect to the weighting matrix B on the Rayleigh quotient. Furthermore, both the values and derivatives of the loss functions can be calculated on near-term quantum devices with shallow quantum circuit. Finally, we propose a full quantum generalized eigensolver (FQGE) to calculate the minimal generalized eigenvalue with quantum gradient descent algorithm. As a demonstration of the principle, we numerically implement our algorithms to conduct a 2-qubit simulation and successfully find the generalized eigenvalues of the matrix pencil (A, B). The numerically experimental result indicates that FQGE is robust under Gaussian noise.
quantum computing is changing the way we think about computing. Significant strides in research and development for managing and harnessing the power of quantum systems has been made in recent years, demonstrating the...
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This paper continues the study of a post-quantum algorithm built on the basis of algebro-geometric codes, begun in previous works, in the interests of ensuring the information security of the Internet of things system...
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This paper presents a vision for generating formal specifications of the problems involved in the mapping of quantumalgorithms to quantum networks, called the Sub-problems of Distribution (SpD). This is a signif...
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quantum abstract detecting systems (QADS) were introduced as a common framework for the study and design of detecting algorithms in a quantum computing setting. In this paper, we introduce new families of such QADS, k...
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quantum abstract detecting systems (QADS) were introduced as a common framework for the study and design of detecting algorithms in a quantum computing setting. In this paper, we introduce new families of such QADS, known as combinatorial and rotational, which, respectively, generalize detecting systems based on single qubit controlled gates and on Grover's algorithm. We study the algorithmic closure of each family and prove that some of these QADS are equivalent (in the sense of having the same detection rate) to others constructed from tensor product of controlled operators and their square roots. We also apply the combinatorial QADS construction to a problem of eigenvalue decision, and to a problem of phase estimation.
Using machine learning to improve quantumalgorithms or quantum mechanics to improve machine learning is known as quantum machine learning. In order to include quantum physics into machine learning, we provide a uniqu...
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The Internet of things (IoT) is a heterogeneous network, and devices produced by different manufacturers lack trust and coordination mechanisms, so they are vulnerable to attacks. Blockchain can provide credible commu...
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The Internet of things (IoT) is a heterogeneous network, and devices produced by different manufacturers lack trust and coordination mechanisms, so they are vulnerable to attacks. Blockchain can provide credible communication at a low cost for IoT nodes. However, the classic digital signature algorithms commonly used in blockchain are no longer secure enough against quantum computing attacks. Given that the emergence of universal quantum computers is just around the corner, we propose a quantum-assisted blockchain of things (QBoT) based on quantum signature. This scheme can protect IoT systems from computation-based attacks and provide more secure communication assurance between nodes. Finally, we analyze the possible attacks on the IoT, and the results show that our proposed scheme can guarantee the security of common IoT attacks. Furthermore, compared with previous quantum-assisted blockchain schemes, QBoT is more efficient and scalable.
The feasibility of variational quantumalgorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum c...
We have ported and optimized the graphics processing unit (GPU)-accelerated QUICK and AMBER-based ab initio quantum mechanics/molecular mechanics (QM/MM) implementation on AMD GPUs. This encompasses the entire Fock ma...
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We have ported and optimized the graphics processing unit (GPU)-accelerated QUICK and AMBER-based ab initio quantum mechanics/molecular mechanics (QM/MM) implementation on AMD GPUs. This encompasses the entire Fock matrix build and force calculation in QUICK including one-electron integrals, two-electron repulsion integrals, exchange-correlation quadrature, and linear algebra operations. General performance improvements to the QUICK GPU code are also presented. Benchmarks carried out on NVIDIA V100 and AMD MI100 cards display similar performance on both hardware for standalone HF/DFT calculations with QUICK and QM/MM molecular dynamics simulations with QUICK/AMBER. Furthermore, with respect to the QUICK/AMBER release version 21, significant speedups are observed for QM/MM molecular dynamics simulations. This significantly increases the range of scientific problems that can be addressed with open-source QM/MM software on state-of-the-art computer hardware.
In this paper, a noninvasive portable prototype is presented for biomedical audio signal processing. The proposed prototype is suitable for monitoring the health of patients. The proposed hardware setup consists of a ...
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In this paper, a noninvasive portable prototype is presented for biomedical audio signal processing. The proposed prototype is suitable for monitoring the health of patients. The proposed hardware setup consists of a cost-effective microphone, multipurpose microcontroller and computing node that could be a mobile phone or general-purpose computer. Using parallel and high-performance techniques, this setup allows one to register and wirelessly multicast the recorded biomedical signals to computing nodes in real time. The developed prototype was used as a case study to estimate the heart rate (HR) from the captured biomedical audio signal. In this regard, the developed algorithm for estimating HR comprises three stages: preprocessing, separation, and HR estimation. In the first stage, the signal captured by the microphone is adapted for processing. Subsequently, a separation stage was proposed to alleviate the acoustic interference between the lungs and heart. The separation is performed by combining a non-negative matrix factorization algorithm, clustering approach, and soft-filter strategy. Finally, HR estimation was obtained using a novel and efficient method based on the autocorrelation function. The developed prototype could be used not only for the estimation of the HR, but also for the retrieval of other biomedical information related to the recording of cardiac or respiratory audio signals. The proposed method was evaluated using well-known datasets and compared with state-of-the-art algorithms for source-separation. The results showed that it is possible to obtain an accurate separation and reliable real-time estimation in terms of source separation metrics and relative error in the tested scenarios by combining multi-core architectures with parallel and high-performance techniques. Finally, the proposed prototype was validated in a real-world scenario.
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