Research on palmprint identification method based on quantum algorithms.
Ontology highlight
ABSTRACT:
SUBMITTER: Li H
PROVIDER: S-EPMC4106095 | biostudies-other | 2014
REPOSITORIES: biostudies-other
Shared Molecules
Only show the datasets with similarity scores above: 0.5
Threshold
0.5
Similar Datasets
Project description:Background and aimsAn unanticipated difficult airway is one of the greatest challenges for anesthesiologists. Proper preoperative airway assessment is crucial to reducing complications. However, current screening tests based on anthropometric features are of uncertain benefit. Therefore, our study explores using voice analysis with machine learning algorithms to predict a difficult airway.MethodsObservational, multicenter study with N = 438 patients initially enrolled at Centro Medico Teknon and Institut Universitari Dexeus (2019-2022) for the research study. After excluding 125 patients, N = 313 were included. Ethics committee approval was obtained. Adults ASA I-III scheduled for elective procedures under general anesthesia with endotracheal intubation were selected. Patient clinical features and traditional predictive tests were collected. Vowels "A, E, I, O, U" were recorded in normal, flexion, and extension positions. Cormack grade was assessed, and data were analyzed using KNIME, resulting in multiple models based on demographics and voice data. ROC curves and other metrics were evaluated for each model.ResultsAmong multiple models evaluated, two yielded the best performance to predict a difficult airway both exclusively analyzing Cormack I and IV cases which showed the most distinct differences. The variables included in each model were the following: Model 1; included demographic data, vowel "A" in all positions and harmonics of the voice achieving an AUC of 0.91. Model 2; Included demographic data, vowel "O" in normal positions and voice parameters (Shimmer, Jitter, HNR); achieving in an AUC of 0.90. In contrast, models which focused on analyzing all Cormack grades (I, II, III, IV) cases performed less effectively.ConclusionsAcoustic parameters of the voice together with the demographic data of the patients, when introduced into classification algorithms based on machine learning showed promising signs of predicting a difficult airway.
| S-EPMC11628723 | biostudies-literature
Project description:Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although traditional biotechnology has high precision, its cost and efficiency are increasingly unable to meet the needs of modern society. Machine learning methods can make up for the deficiencies of biological experimental techniques to a certain extent, but they are not as simple and fast as deep learning for data processing. In this paper, a deep learning framework based on parallel long and short-term memory(LSTM) and convolutional neural networks(CNN) was proposed to identify DNA-binding protein. This model can not only further extract the information and features of protein sequences, but also the features of evolutionary information. Finally, the two features are combined for training and testing. On the PDB2272 dataset, compared with PDBP_Fusion model, Accuracy(ACC) and Matthew's Correlation Coefficient (MCC) increased by 3.82% and 7.98% respectively. The experimental results of this model have certain advantages.
| S-EPMC9184165 | biostudies-literature
Project description:It is difficult to accurately establish a model of the real mesa system. Furthermore, a model of a seismic simulation vibration table array system is critical to increasing the accuracy of seismic testing in laboratory settings. Herein a model of the nine subarray shaking table system is identified by recursive extension of the least square method, which is used to accurately identify the structure parameters by simulation of the structure assuming a single degree-of-freedom. Then, through the displacement of the empty shaking table and the application of the recursive least squares algorithm, the model of the seismic simulation vibration table array is established. Through this study, the vibration table model of different construction forms can be obtained, and the parameters that are difficult to measure for some complex structures can effectively be determined.
| S-EPMC9797076 | biostudies-literature
Project description:When quantum computing becomes a wide-spread commercial reality, Quantum Search Algorithms (QSA) and especially Grover's QSA will inevitably be one of their main applications, constituting their cornerstone. Most of the literature assumes that the quantum circuits are free from decoherence. Practically, decoherence will remain unavoidable as is the Gaussian noise of classic circuits imposed by the Brownian motion of electrons, hence it may have to be mitigated. In this contribution, we investigate the effect of quantum noise on the performance of QSAs, in terms of their success probability as a function of the database size to be searched, when decoherence is modelled by depolarizing channels' deleterious effects imposed on the quantum gates. Moreover, we employ quantum error correction codes for limiting the effects of quantum noise and for correcting quantum flips. More specifically, we demonstrate that, when we search for a single solution in a database having 4096 entries using Grover's QSA at an aggressive depolarizing probability of 10-3, the success probability of the search is 0.22 when no quantum coding is used, which is improved to 0.96 when Steane's quantum error correction code is employed. Finally, apart from Steane's code, the employment of Quantum Bose-Chaudhuri-Hocquenghem (QBCH) codes is also considered.
| S-EPMC5141447 | biostudies-literature
Project description:Metasurfaces have recently opened up applications in the quantum regime, including quantum tomography and the generation of quantum entangled states. With their capability to store a vast amount of information by utilizing the various geometric degrees of freedom of nanostructures, metasurfaces are expected to be useful for processing quantum information. Here, we propose and experimentally demonstrate a programmable metasurface capable of performing quantum algorithms using both classical and quantum light with single photons. Our approach encodes multiple programmable quantum algorithms and operations, such as Grover's search algorithm and the quantum Fourier transform, onto the same metalens array on a metasurface. A spatial light modulator selectively excites different sets of metalenses to carry out the quantum algorithms, while the interference patterns captured by a single-photon camera are used to extract information about the output state at the selected output directions. Our programmable quantum metasurface approach holds promising potential as a cost-effective means of miniaturizing components for quantum computing and information processing.
| S-EPMC11614333 | biostudies-literature
Project description:Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10-1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.
| S-EPMC9938886 | biostudies-literature
Project description:One of the most important properties of classical neural networks is how surprisingly trainable they are, though their training algorithms typically rely on optimizing complicated, nonconvex loss functions. Previous results have shown that unlike the case in classical neural networks, variational quantum models are often not trainable. The most studied phenomenon is the onset of barren plateaus in the training landscape of these quantum models, typically when the models are very deep. This focus on barren plateaus has made the phenomenon almost synonymous with the trainability of quantum models. Here, we show that barren plateaus are only a part of the story. We prove that a wide class of variational quantum models—which are shallow, and exhibit no barren plateaus—have only a superpolynomially small fraction of local minima within any constant energy from the global minimum, rendering these models untrainable if no good initial guess of the optimal parameters is known. We also study the trainability of variational quantum algorithms from a statistical query framework, and show that noisy optimization of a wide variety of quantum models is impossible with a sub-exponential number of queries. Finally, we numerically confirm our results on a variety of problem instances. Though we exclude a wide variety of quantum algorithms here, we give reason for optimism for certain classes of variational algorithms and discuss potential ways forward in showing the practical utility of such algorithms. Implementations of shallow quantum machine learning models are a promising application of near-term quantum computers, but rigorous results on their trainability are sparse. Here, the authors demonstrate settings where such models are untrainable.
| S-EPMC9755303 | biostudies-literature
Project description:The water source, as a significant body of the earth, with a high value, serves as a hot topic to study Underwater Sensor Networks (UWSNs). Various applications can be realized based on UWSNs. Our paper mainly concentrates on the localization algorithms based on the acoustic communication for UWSNs. An in-depth survey of localization algorithms is provided for UWSNs. We first introduce the acoustic communication, network architecture, and routing technique in UWSNs. The localization algorithms are classified into five aspects, namely, computation algorithm, spatial coverage, range measurement, the state of the nodes and communication between nodes that are different from all other survey papers. Moreover, we collect a lot of pioneering papers, and a comprehensive comparison is made. In addition, some challenges and open issues are raised in our paper.
| S-EPMC5795900 | biostudies-literature
Project description:The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.
| S-EPMC8254374 | biostudies-literature
Project description:Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise on NISQ devices places fundamental limitations on VQA performance. We rigorously prove a serious limitation for noisy VQAs, in that the noise causes the training landscape to have a barren plateau (i.e., vanishing gradient). Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of qubits n if the depth of the ansatz grows linearly with n. These noise-induced barren plateaus (NIBPs) are conceptually different from noise-free barren plateaus, which are linked to random parameter initialization. Our result is formulated for a generic ansatz that includes as special cases the Quantum Alternating Operator Ansatz and the Unitary Coupled Cluster Ansatz, among others. For the former, our numerical heuristics demonstrate the NIBP phenomenon for a realistic hardware noise model.
| S-EPMC8630047 | biostudies-literature