A new algorithm has been developed for delineation of significant points of various electrocardiographic signal (ECG) waves, taking into account information from all available leads and providing similar or higher accuracy in comparison with other modern technologies. The test results for the QT database show a sensitivity above 97% when detecting ECG wave peaks and 96% for their onsets and offsets, as well as better positive predictive value compared to the previously known algorithms. In contrast to the previously published algorithms, the proposed approach also allows one to determine the morphology of waves. The segmentation mean errors of all significant points are below the tolerances defined by the Committee of General Standards for Electrocardiography (CSE).

Due to complexity of the systems and processes it addresses, the development of computational quantum physics is influenced by the progress in computing technology. Here we overview the evolution, from the late 1980s to the current year 2020, of the algorithms used to simulate dynamics of quantum systems. We put the emphasis on implementation aspects and computational resource scaling with the model size and propagation time. Our minireview is based on a literature survey and our experience in implementing different types of algorithms.

Many-body quantum systems are subjected to the Curse of Dimensionality: The dimension of the Hilbert space H, where these systems live in, grows exponentially with number of their components ('bodies'). However, with some systems it is possible to escape the curse by using low-rank tensor approximations known as "matrix-product state/operator (MPS/O) representation" in the quantum community and "tensor-train decomposition" among applied mathematicians. Motivated by recent advances in computational quantum physics, we consider chains of N spins coupled by nearestneighbor interactions. The spins are subjected to an action coming from the environment. Spatially disordered interaction and environment-induced decoherence drive systems into non-trivial asymptotic states. The dissipative evolution is modeled with a Markovian master equation in the Lindblad form. By implementing the MPO technique and propagating system states with the time-evolving block decimation (TEBD) scheme (which allows keeping the length of the state descriptions fixed), it is in principle possible to reach the corresponding steady states. We propose and realize a cluster implementation of this idea. The implementation on four nodes allowed us to resolve steady states of the model systems with N = 128 spins (total dimension of the Hilbert space dimH = 2 128 ≈ 10 39 ). arXiv:1905.08365v2 [cond-mat.stat-mech]

Due to complexity of the systems and processes it addresses, the development of computational quantum physics is influenced by the progress in computing technology. Here we overview the evolution, from the late 1980s to the current year 2020, of the algorithms used to simulate dynamics of quantum systems. We put the emphasis on implementation aspects and computational resource scaling with the model size and propagation time. Our minireview is based on a literature survey and our experience in implementing different types of algorithms.

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