Journal articles: 'Quantum Machine Learning (QML)'

1

Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (November 16, 2022): 5774. http://dx.doi.org/10.3390/rs14225774.

Full text

Abstract:

A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.

APA, Harvard, Vancouver, ISO, and other styles

2

Karandashev, Konstantin, and O. Anatole von Lilienfeld. "An orbital-based representation for accurate quantum machine learning." Journal of Chemical Physics 156, no. 11 (March 21, 2022): 114101. http://dx.doi.org/10.1063/5.0083301.

Full text

Abstract:

We introduce an electronic structure based representation for quantum machine learning (QML) of electronic properties throughout chemical compound space. The representation is constructed using computationally inexpensive ab initio calculations and explicitly accounts for changes in the electronic structure. We demonstrate the accuracy and flexibility of resulting QML models when applied to property labels, such as total potential energy, HOMO and LUMO energies, ionization potential, and electron affinity, using as datasets for training and testing entries from the QM7b, QM7b-T, QM9, and LIBE libraries. For the latter, we also demonstrate the ability of this approach to account for molecular species of different charge and spin multiplicity, resulting in QML models that infer total potential energies based on geometry, charge, and spin as input.

APA, Harvard, Vancouver, ISO, and other styles

3

Choppakatla, Arathi. "Quantum Machine Learning: Bridging the Gap Between Quantum Computing and Artificial Intelligence: An Overview." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 1149–53. http://dx.doi.org/10.22214/ijraset.2023.55318.

Full text

Abstract:

Abstract: Quantum Machine Learning (QML) at the intersection of quantum computing and artificial intelligence (AI) is explored, emphasizing its role in connecting these domains. The transformative potential of QML in enhancing classical machine learning and the introduction of the Variational Quantum Classifier (VQC) algorithm (Ref. 4) are highlighted. Fundamental quantum principles, quantum feature maps, and the VQC's use of parameterized quantum circuits are discussed (Refs. 1, 3). The paper addresses practical implementation, optimization techniques, and the VQC's performance through empirical evaluations (Ref. 4). Implications of QML extend to diverse applications (Ref. 5), positioning it as a bridge between quantum computing and AI to unlock transformative possibilities.

APA, Harvard, Vancouver, ISO, and other styles

4

Avramouli, Maria, Ilias Κ. Savvas, Anna Vasilaki, and Georgia Garani. "Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery." Electronics 12, no. 11 (May 25, 2023): 2402. http://dx.doi.org/10.3390/electronics12112402.

Full text

Abstract:

The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of machine learning (ML), leading to the emergence of quantum machine learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals to the classical ones, and a hybrid solution is the recommended approach at present.

APA, Harvard, Vancouver, ISO, and other styles

5

T West, Maxwell, Martin Sevior, and Muhammad Usman. "Reflection equivariant quantum neural networks for enhanced image classification." Machine Learning: Science and Technology 4, no. 3 (August 24, 2023): 035027. http://dx.doi.org/10.1088/2632-2153/acf096.

Full text

Abstract:

Abstract Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of work which suggests that generic, data agnostic quantum machine learning (QML) architectures may suffer from severe trainability issues, with the gradient of typical variational parameters vanishing exponentially in the number of qubits. Additionally, the high expressibility of QML models can lead to overfitting on training data and poor generalisation performance. A promising strategy to combat both of these difficulties is to construct models which explicitly respect the symmetries inherent in their data, so-called geometric quantum machine learning (GQML). In this work, we utilise the techniques of GQML for the task of image classification, building new QML models which are equivariant with respect to reflections of the images. We find that these networks are capable of consistently and significantly outperforming generic ansatze on complicated real-world image datasets, bringing high-resolution image classification via quantum computers closer to reality. Our work highlights a potential pathway for the future development and implementation of powerful QML models which directly exploit the symmetries of data.

APA, Harvard, Vancouver, ISO, and other styles

6

Christensen, Anders S., and O. Anatole von Lilienfeld. "Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties." CHIMIA International Journal for Chemistry 73, no. 12 (December 18, 2019): 1028–31. http://dx.doi.org/10.2533/chimia.2019.1028.

Full text

Abstract:

The identification and use of structure–property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties.

APA, Harvard, Vancouver, ISO, and other styles

7

Nguyen, Quoc Chuong, Le Bin Ho, Lan Nguyen Tran, and Hung Q. Nguyen. "Qsun: an open-source platform towards practical quantum machine learning applications." Machine Learning: Science and Technology 3, no. 1 (March 1, 2022): 015034. http://dx.doi.org/10.1088/2632-2153/ac5997.

Full text

Abstract:

Abstract Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms (VQAs) have been proposed and tested on QVMs to surpass the limitations of quantum hardware. Our goal is to exploit further the VQAs towards practical applications of quantum machine learning (QML) using state-of-the-art quantum computers. In this paper, we first introduce a QVM named Qsun, whose operation is underlined by quantum state wavefunctions. The platform provides native tools supporting VQAs. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of QML: quantum linear regression and quantum neural network.

APA, Harvard, Vancouver, ISO, and other styles

8

Srikumar, Maiyuren, Charles D. Hill, and Lloyd C. L. Hollenberg. "Clustering and enhanced classification using a hybrid quantum autoencoder." Quantum Science and Technology 7, no. 1 (December 21, 2021): 015020. http://dx.doi.org/10.1088/2058-9565/ac3c53.

Full text

Abstract:

Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify—and classically represent—their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states—which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

APA, Harvard, Vancouver, ISO, and other styles

9

Kumar, Tarun, Dilip Kumar, and Gurmohan Singh. "Performance Analysis of Quantum Classifier on Benchmarking Datasets." International Journal of Electrical and Electronics Research 10, no. 2 (June 30, 2022): 375–80. http://dx.doi.org/10.37391/ijeer.100252.

Full text

Abstract:

Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up.

APA, Harvard, Vancouver, ISO, and other styles

10

Chen, Samuel Yen-Chi, and Shinjae Yoo. "Federated Quantum Machine Learning." Entropy 23, no. 4 (April 13, 2021): 460. http://dx.doi.org/10.3390/e23040460.

Full text

Abstract:

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

APA, Harvard, Vancouver, ISO, and other styles

11

Belis, Vasilis, Samuel González-Castillo, Christina Reissel, Sofia Vallecorsa, Elías F. Combarro, Günther Dissertori, and Florentin Reiter. "Higgs analysis with quantum classifiers." EPJ Web of Conferences 251 (2021): 03070. http://dx.doi.org/10.1051/epjconf/202125103070.

Full text

Abstract:

We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limitations in both simulation hardware and real quantum hardware — we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.

APA, Harvard, Vancouver, ISO, and other styles

12

Mancilla, Javier, and Christophe Pere. "A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms." Entropy 24, no. 11 (November 15, 2022): 1656. http://dx.doi.org/10.3390/e24111656.

Full text

Abstract:

Quantum Machine Learning (QML) has not yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific cases where some quantum-inspired techniques have achieved small incremental advantages, and a few experimental cases in hybrid quantum computing are promising, considering a mid-term future (not taking into account the achievements purely associated with optimization using quantum-classical algorithms). The current quantum computers are noisy and have few qubits to test, making it difficult to demonstrate the current and potential quantum advantage of QML methods. This study shows that we can achieve better classical encoding and performance of quantum classifiers by using Linear Discriminant Analysis (LDA) during the data preprocessing step. As a result, the Variational Quantum Algorithm (VQA) shows a gain of performance in balanced accuracy with the LDA technique and outperforms baseline classical classifiers.

APA, Harvard, Vancouver, ISO, and other styles

13

Wang, Maida, Anqi Huang, Yong Liu, Xuming Yi, Junjie Wu, and Siqi Wang. "A Quantum-Classical Hybrid Solution for Deep Anomaly Detection." Entropy 25, no. 3 (February 27, 2023): 427. http://dx.doi.org/10.3390/e25030427.

Full text

Abstract:

Machine learning (ML) has achieved remarkable success in a wide range of applications. In recent ML research, deep anomaly detection (AD) has been a hot topic with the aim of discriminating among anomalous data with deep neural networks (DNNs). Notably, image AD is one of the most representative tasks in current deep AD research. ML’s interaction with quantum computing is giving rise to a heated topic named quantum machine learning (QML), which enjoys great prospects according to recent academic research. This paper attempts to address the image AD problem in a deep manner with a novel QML solution. Specifically, we design a quantum-classical hybrid DNN (QHDNN) that aims to learn directly from normal raw images to train a normality model and then exclude images that do not conform to this model as anomalies during its inference. To enable the QHDNN to perform satisfactorily in deep image AD, we explore multiple quantum layer architectures and design a VQC-based QHDNN solution. Extensive experiments were conducted on commonly used benchmarks to test the proposed QML solution, whose results demonstrate the feasibility of addressing deep image AD with QML. Importantly, the experimental results show that our quantum-classical hybrid solution can even yield superior performance to that of its classical counterpart when they share the same number of learnable parameters.

APA, Harvard, Vancouver, ISO, and other styles

14

Gyurik, Casper, Dyon Vreumingen, van, and Vedran Dunjko. "Structural risk minimization for quantum linear classifiers." Quantum 7 (January 13, 2023): 893. http://dx.doi.org/10.22331/q-2023-01-13-893.

Full text

Abstract:

Quantum machine learning (QML) models based on parameterized quantum circuits are often highlighted as candidates for quantum computing's near-term “killer application''. However, the understanding of the empirical and generalization performance of these models is still in its infancy. In this paper we study how to balance between training accuracy and generalization performance (also called structural risk minimization) for two prominent QML models introduced by Havlíček et al. \cite{havlivcek:qsvm}, and Schuld and Killoran \cite{schuld:qsvm}. Firstly, using relationships to well understood classical models, we prove that two model parameters – i.e., the dimension of the sum of the images and the Frobenius norm of the observables used by the model – closely control the models' complexity and therefore its generalization performance. Secondly, using ideas inspired by process tomography, we prove that these model parameters also closely control the models' ability to capture correlations in sets of training examples. In summary, our results give rise to new options for structural risk minimization for QML models.

APA, Harvard, Vancouver, ISO, and other styles

15

Gyurik, Casper, Chris Cade, and Vedran Dunjko. "Towards quantum advantage via topological data analysis." Quantum 6 (November 10, 2022): 855. http://dx.doi.org/10.22331/q-2022-11-10-855.

Full text

Abstract:

Even after decades of quantum computing development, examples of generally useful quantum algorithms with exponential speedups over classical counterparts are scarce. Recent progress in quantum algorithms for linear-algebra positioned quantum machine learning (QML) as a potential source of such useful exponential improvements. Yet, in an unexpected development, a recent series of "dequantization" results has equally rapidly removed the promise of exponential speedups for several QML algorithms. This raises the critical question whether exponential speedups of other linear-algebraic QML algorithms persist. In this paper, we study the quantum-algorithmic methods behind the algorithm for topological data analysis of Lloyd, Garnerone and Zanardi through this lens. We provide evidence that the problem solved by this algorithm is classically intractable by showing that its natural generalization is as hard as simulating the one clean qubit model – which is widely believed to require superpolynomial time on a classical computer – and is thus very likely immune to dequantizations. Based on this result, we provide a number of new quantum algorithms for problems such as rank estimation and complex network analysis, along with complexity-theoretic evidence for their classical intractability. Furthermore, we analyze the suitability of the proposed quantum algorithms for near-term implementations. Our results provide a number of useful applications for full-blown, and restricted quantum computers with a guaranteed exponential speedup over classical methods, recovering some of the potential for linear-algebraic QML to become one of quantum computing's killer applications.

APA, Harvard, Vancouver, ISO, and other styles

16

Wieder, Marcus, Josh Fass, and John D. Chodera. "Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution." Chemical Science 12, no. 34 (2021): 11364–81. http://dx.doi.org/10.1039/d1sc01185e.

Full text

Abstract:

We show how alchemical free energies can be calculated with QML potentials to identify deficiencies in RRHO approximations for computing tautomeric free energies, and how these potentials can be learned from experiment to improve prediction accuracy.

APA, Harvard, Vancouver, ISO, and other styles

17

., Harshita. "6G Communication Network & Emerging Technologies." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 10, 2021): 507–14. http://dx.doi.org/10.22214/ijraset.2021.36029.

Full text

Abstract:

The sixth-generation (6G) wireless communication network here is going to integrate with terrestrial, aerial, and maritime communications to make network robust that will be more reliable, fast, and will support a massive number of devices with ultra-low latency requirements. The researchers around the globe are using emerging technologies like artificial intelligence (AI), machine learning (ML), quantum communication, quantum machine learning (QML), block chain, tera-Hertz and millimeter wave’s communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog, edge computing, etc., with 6G network communication beyond 5G. In this paper, an overview will be provided of 6G network along emerging technologies associated with it.

APA, Harvard, Vancouver, ISO, and other styles

18

Wang, Xinbiao, Yuxuan Du, Yong Luo, and Dacheng Tao. "Towards understanding the power of quantum kernels in the NISQ era." Quantum 5 (August 30, 2021): 531. http://dx.doi.org/10.22331/q-2021-08-30-531.

Full text

Abstract:

A key problem in the field of quantum computing is understanding whether quantum machine learning (QML) models implemented on noisy intermediate-scale quantum (NISQ) machines can achieve quantum advantages. Recently, Huang et al. [Nat Commun 12, 2631] partially answered this question by the lens of quantum kernel learning. Namely, they exhibited that quantum kernels can learn specific datasets with lower generalization error over the optimal classical kernel methods. However, most of their results are established on the ideal setting and ignore the caveats of near-term quantum machines. To this end, a crucial open question is: does the power of quantum kernels still hold under the NISQ setting? In this study, we fill this knowledge gap by exploiting the power of quantum kernels when the quantum system noise and sample error are considered. Concretely, we first prove that the advantage of quantum kernels is vanished for large size of datasets, few number of measurements, and large system noise. With the aim of preserving the superiority of quantum kernels in the NISQ era, we further devise an effective method via indefinite kernel learning. Numerical simulations accord with our theoretical results. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.

APA, Harvard, Vancouver, ISO, and other styles

19

Yun, Won Joon, Jihong Park, and Joongheon Kim. "Quantum Multi-Agent Meta Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 11087–95. http://dx.doi.org/10.1609/aaai.v37i9.26313.

Full text

Abstract:

Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-design multi-agent reinforcement learning (MARL) based on the unique characteristics of quantum neural networks (QNNs) having two separate dimensions of trainable parameters: angle parameters affecting the output qubit states, and pole parameters associated with the output measurement basis. Exploiting this dyadic trainability as meta-learning capability, we propose quantum meta MARL (QM2ARL) that first applies angle training for meta-QNN learning, followed by pole training for few-shot or local-QNN training. To avoid overfitting, we develop an angle-to-pole regularization technique injecting noise into the pole domain during angle training. Furthermore, by exploiting the pole as the memory address of each trained QNN, we introduce the concept of pole memory allowing one to save and load trained QNNs using only two-parameter pole values. We theoretically prove the convergence of angle training under the angle-to-pole regularization, and by simulation corroborate the effectiveness of QM2ARL in achieving high reward and fast convergence, as well as of the pole memory in fast adaptation to a time-varying environment.

APA, Harvard, Vancouver, ISO, and other styles

20

Riaz, Farina, Shahab Abdulla, Hajime Suzuki, Srinjoy Ganguly, Ravinesh C. Deo, and Susan Hopkins. "Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach." Sensors 23, no. 5 (March 2, 2023): 2753. http://dx.doi.org/10.3390/s23052753.

Full text

Abstract:

Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first demonstrate that the previously proposed quanvolutional neural network (QuanvNN) using a randomly generated quantum circuit improves the image classification accuracy of a fully connected neural network against the Modified National Institute of Standards and Technology (MNIST) dataset and the Canadian Institute for Advanced Research 10 class (CIFAR-10) dataset from 92.0% to 93.0% and from 30.5% to 34.9%, respectively. We then propose a new model referred to as a Neural Network with Quantum Entanglement (NNQE) using a strongly entangled quantum circuit combined with Hadamard gates. The new model further improves the image classification accuracy of MNIST and CIFAR-10 to 93.8% and 36.0%, respectively. Unlike other QML methods, the proposed method does not require optimization of the parameters inside the quantum circuits; hence, it requires only limited use of the quantum circuit. Given the small number of qubits and relatively shallow depth of the proposed quantum circuit, the proposed method is well suited for implementation in noisy intermediate-scale quantum computers. While promising results were obtained by the proposed method when applied to the MNIST and CIFAR-10 datasets, a test against a more complicated German Traffic Sign Recognition Benchmark (GTSRB) dataset degraded the image classification accuracy from 82.2% to 73.4%. The exact causes of the performance improvement and degradation are currently an open question, prompting further research on the understanding and design of suitable quantum circuits for image classification neural networks for colored and complex data.

APA, Harvard, Vancouver, ISO, and other styles

21

Shahwar, Tayyaba, Junaid Zafar, Ahmad Almogren, Haroon Zafar, Ateeq Ur Rehman, Muhammad Shafiq, and Habib Hamam. "Automated Detection of Alzheimer’s via Hybrid Classical Quantum Neural Networks." Electronics 11, no. 5 (February 26, 2022): 721. http://dx.doi.org/10.3390/electronics11050721.

Full text

Abstract:

Deep Neural Networks have offered numerous innovative solutions to brain-related diseases including Alzheimer’s. However, there are still a few standpoints in terms of diagnosis and planning that can be transformed via quantum Machine Learning (QML). In this study, we present a hybrid classical–quantum machine learning model for the detection of Alzheimer’s using 6400 labeled MRI scans with two classes. Hybrid classical–quantum transfer learning is used, which makes it possible to optimally pre-process complex and high-dimensional data. Classical neural networks extract high-dimensional features and embed informative feature vectors into a quantum processor. We use resnet34 to extract features from the image and feed a 512-feature vector to our quantum variational circuit (QVC) to generate a four-feature vector for precise decision boundaries. Adam optimizer is used to exploit the adaptive learning rate corresponding to each parameter based on first- and second-order gradients. Furthermore, to validate the model, different quantum simulators (PennyLane, qiskit.aer and qiskit.basicaer) are used for the detection of the demented and non-demented images. The learning rate is set to 10−4 for and optimized quantum depth of six layers, resulting in a training accuracy of 99.1% and a classification accuracy of 97.2% for 20 epochs. The hybrid classical–quantum network significantly outperformed the classical network, as the classification accuracy achieved by the classical transfer learning model was 92%. Thus, a hybrid transfer-learning model is used for binary detection, in which a quantum circuit improves the performance of a pre-trained ResNet34 architecture. Therefore, this work offers a method for selecting an optimal approach for detecting Alzheimer’s disease. The proposed model not only allows for the automated detection of Alzheimer’s but would also speed up the process significantly in clinical settings.

APA, Harvard, Vancouver, ISO, and other styles

22

Sato, Kyosuke, and Kenji Tsuruta. "Optimization of Molecular Characteristics via Machine Learning Based on Continuous Representation of Molecules." Materials Science Forum 1016 (January 2021): 1492–96. http://dx.doi.org/10.4028/www.scientific.net/msf.1016.1492.

Full text

Abstract:

We demonstrate an automatic materials design method using continuous representation of molecule and its atomic arrangement via a neural network algorithm. This method is applied to optimizing and predicting the HOMO-LUMO gap within the molecules composed of carbon, oxygen, nitrogen, fluorine, and hydrogen. Adopting the Quantum Machine 9 (QM9) dataset as a training dataset for the molecules, we first established a continuous representation of molecules in a latent space, then predicted molecules that have target values of the HOMO-LUMO gap. In the gap maximization calculation, the CF4 with the largest gap value in the QM9 dataset was automatically found despite there is no a priori data for the gap. In the case of a target gap value of 0.10 hartree, we found a new molecule whose gap value is closer to 0.10 hartree than any other molecules in the QM9 dataset.

APA, Harvard, Vancouver, ISO, and other styles

23

Pinheiro, Gabriel A., Johnatan Mucelini, Marinalva D. Soares, Ronaldo C. Prati, Juarez L. F. Da Silva, and Marcos G. Quiles. "Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset." Journal of Physical Chemistry A 124, no. 47 (November 11, 2020): 9854–66. http://dx.doi.org/10.1021/acs.jpca.0c05969.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Pilling, Michael J., Alex Henderson, Benjamin Bird, Mick D. Brown, Noel W. Clarke, and Peter Gardner. "High-throughput quantum cascade laser (QCL) spectral histopathology: a practical approach towards clinical translation." Faraday Discussions 187 (2016): 135–54. http://dx.doi.org/10.1039/c5fd00176e.

Full text

Abstract:

Infrared microscopy has become one of the key techniques in the biomedical research field for interrogating tissue. In partnership with multivariate analysis and machine learning techniques, it has become widely accepted as a method that can distinguish between normal and cancerous tissue with both high sensitivity and high specificity. While spectral histopathology (SHP) is highly promising for improved clinical diagnosis, several practical barriers currently exist, which need to be addressed before successful implementation in the clinic. Sample throughput and speed of acquisition are key barriers and have been driven by the high volume of samples awaiting histopathological examination. FTIR chemical imaging utilising FPA technology is currently state-of-the-art for infrared chemical imaging, and recent advances in its technology have dramatically reduced acquisition times. Despite this, infrared microscopy measurements on a tissue microarray (TMA), often encompassing several million spectra, takes several hours to acquire. The problem lies with the vast quantities of data that FTIR collects; each pixel in a chemical image is derived from a full infrared spectrum, itself composed of thousands of individual data points. Furthermore, data management is quickly becoming a barrier to clinical translation and poses the question of how to store these incessantly growing data sets. Recently, doubts have been raised as to whether the full spectral range is actually required for accurate disease diagnosis using SHP. These studies suggest that once spectral biomarkers have been predetermined it may be possible to diagnose disease based on a limited number of discrete spectral features. In this current study, we explore the possibility of utilising discrete frequency chemical imaging for acquiring high-throughput, high-resolution chemical images. Utilising a quantum cascade laser imaging microscope with discrete frequency collection at key diagnostic wavelengths, we demonstrate that we can diagnose prostate cancer with high sensitivity and specificity. Finally we extend the study to a large patient dataset utilising tissue microarrays, and show that high sensitivity and specificity can be achieved using high-throughput, rapid data collection, thereby paving the way for practical implementation in the clinic.

APA, Harvard, Vancouver, ISO, and other styles

25

Pacheco-Londoño, Leonardo C., Eric Warren, Nataly J. Galán-Freyle, Reynaldo Villarreal-González, Joaquín A. Aparicio-Bolaño, María L. Ospina-Castro, Wei-Chuan Shih, and Samuel P. Hernández-Rivera. "Mid-Infrared Laser Spectroscopy Detection and Quantification of Explosives in Soils Using Multivariate Analysis and Artificial Intelligence." Applied Sciences 10, no. 12 (June 18, 2020): 4178. http://dx.doi.org/10.3390/app10124178.

Full text

Abstract:

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

APA, Harvard, Vancouver, ISO, and other styles

26

Mittal, Shachi, Kevin Yeh, L. Suzanne Leslie, Seth Kenkel, Andre Kajdacsy-Balla, and Rohit Bhargava. "Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology." Proceedings of the National Academy of Sciences 115, no. 25 (June 4, 2018): E5651—E5660. http://dx.doi.org/10.1073/pnas.1719551115.

Full text

Abstract:

Histopathology based on spatial patterns of epithelial cells is the gold standard for clinical diagnoses and research in carcinomas; although known to be important, the tissue microenvironment is not readily used due to complex and subjective interpretation with existing tools. Here, we demonstrate accurate subtyping from molecular properties of epithelial cells using emerging high-definition Fourier transform infrared (HD FT-IR) spectroscopic imaging combined with machine learning algorithms. In addition to detecting four epithelial subtypes, we simultaneously delineate three stromal subtypes that characterize breast tumors. While FT-IR imaging data enable fully digital pathology with rich information content, the long spectral scanning times required for signal averaging and processing make the technology impractical for routine research or clinical use. Hence, we developed a confocal design in which refractive IR optics are designed to provide high-definition, rapid spatial scanning and discrete spectral tuning using a quantum cascade laser (QCL) source. This instrument provides simultaneously high resolving power (2-μm pixel size) and high signal-to-noise ratio (SNR) (>1,300), providing a speed increase of ∼50-fold for obtaining classified results compared with present imaging spectrometers. We demonstrate spectral fidelity and interinstrument operability of our developed instrument by accurate analysis of a 100-case breast tissue set that was analyzed in a day, considerably speeding research. Clinical breast biopsies typical of a patients’ caseload are analyzed in ∼1 hour. This study paves the way for comprehensive tumor-microenvironment analyses in feasible time periods, presenting a critical step in practical label-free molecular histopathology.

APA, Harvard, Vancouver, ISO, and other styles

27

Biamonte, Jacob, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. "Quantum machine learning." Nature 549, no. 7671 (September 2017): 195–202. http://dx.doi.org/10.1038/nature23474.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Allcock, Jonathan, and Shengyu Zhang. "Quantum machine learning." National Science Review 6, no. 1 (November 30, 2018): 26–28. http://dx.doi.org/10.1093/nsr/nwy149.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Peleshenko, Vitaly A. "QUANTUM MACHINE LEARNING." SOFT MEASUREMENTS AND COMPUTING 11, no. 60 (2022): 82–107. http://dx.doi.org/10.36871/2618-9976.2022.11.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Pudenz, Kristen L., and Daniel A. Lidar. "Quantum adiabatic machine learning." Quantum Information Processing 12, no. 5 (November 21, 2012): 2027–70. http://dx.doi.org/10.1007/s11128-012-0506-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Saini, Shivani, PK Khosla, Manjit Kaur, and Gurmohan Singh. "Quantum Driven Machine Learning." International Journal of Theoretical Physics 59, no. 12 (December 2020): 4013–24. http://dx.doi.org/10.1007/s10773-020-04656-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Lamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics." Photonics 8, no. 2 (January 28, 2021): 33. http://dx.doi.org/10.3390/photonics8020033.

Full text

Abstract:

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

APA, Harvard, Vancouver, ISO, and other styles

33

Fung, Fred. "QUANTUM SOFTWARE: Quantum Machine Learning in Telecommunication." Digitale Welt 6, no. 2 (March 12, 2022): 30–31. http://dx.doi.org/10.1007/s42354-022-0472-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Cárdenas‐López, Francisco A., Mikel Sanz, Juan Carlos Retamal, and Enrique Solano. "Enhanced Quantum Synchronization via Quantum Machine Learning." Advanced Quantum Technologies 2, no. 7-8 (January 7, 2019): 1800076. http://dx.doi.org/10.1002/qute.201800076.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Lamata, Lucas, Mikel Sanz, and Enrique Solano. "Quantum Machine Learning and Bioinspired Quantum Technologies." Advanced Quantum Technologies 2, no. 7-8 (August 2019): 1900075. http://dx.doi.org/10.1002/qute.201900075.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

BANG, Jeongho. "Machine Learning and Quantum Algorithm." Physics and High Technology 26, no. 12 (December 30, 2017): 25–29. http://dx.doi.org/10.3938/phit.26.048.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Roggero, Alessandro, Jakub Filipek, Shih-Chieh Hsu, and Nathan Wiebe. "Quantum Machine Learning with SQUID." Quantum 6 (May 30, 2022): 727. http://dx.doi.org/10.22331/q-2022-05-30-727.

Full text

Abstract:

In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.

APA, Harvard, Vancouver, ISO, and other styles

38

Spagnolo, Nicolò, Alessandro Lumino, Emanuele Polino, Adil S. Rab, Nathan Wiebe, and Fabio Sciarrino. "Machine Learning for Quantum Metrology." Proceedings 12, no. 1 (August 23, 2019): 28. http://dx.doi.org/10.3390/proceedings2019012028.

Full text

Abstract:

Phase estimation represents a significant example to test the application of quantum theory for enhanced measurements of unknown physical parameters. Several recipes have been developed, allowing to define strategies to reach the ultimate bounds in the asymptotic limit of a large number of trials. However, in certain applications it is crucial to reach such bound when only a small number of probes is employed. Here, we discuss an asymptotically optimal, machine learning based, adaptive single-photon phase estimation protocol that allows us to reach the standard quantum limit when a very limited number of photons is employed.

APA, Harvard, Vancouver, ISO, and other styles

39

Fabrizio, Alberto, Benjamin Meyer, Raimon Fabregat, and Clemence Corminboeuf. "Quantum Chemistry Meets Machine Learning." CHIMIA International Journal for Chemistry 73, no. 12 (December 18, 2019): 983–89. http://dx.doi.org/10.2533/chimia.2019.983.

Full text

Abstract:

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.

APA, Harvard, Vancouver, ISO, and other styles

40

Wang, Bingjie. "Quantum algorithms for machine learning." XRDS: Crossroads, The ACM Magazine for Students 23, no. 1 (September 20, 2016): 20–24. http://dx.doi.org/10.1145/2983535.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Carrasquilla, Juan. "Machine learning for quantum matter." Advances in Physics: X 5, no. 1 (January 1, 2020): 1797528. http://dx.doi.org/10.1080/23746149.2020.1797528.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Das Sarma, Sankar, Dong-Ling Deng, and Lu-Ming Duan. "Machine learning meets quantum physics." Physics Today 72, no. 3 (March 2019): 48–54. http://dx.doi.org/10.1063/pt.3.4164.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Khan, Tariq M., and Antonio Robles-Kelly. "Machine Learning: Quantum vs Classical." IEEE Access 8 (2020): 219275–94. http://dx.doi.org/10.1109/access.2020.3041719.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Stajic, Jelena. "Machine learning and quantum physics." Science 355, no. 6325 (February 9, 2017): 591.15–593. http://dx.doi.org/10.1126/science.355.6325.591-o.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Schuld, Maria. "Machine learning in quantum spaces." Nature 567, no. 7747 (March 2019): 179–81. http://dx.doi.org/10.1038/d41586-019-00771-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Sheng, Yu-Bo, and Lan Zhou. "Distributed secure quantum machine learning." Science Bulletin 62, no. 14 (July 2017): 1025–29. http://dx.doi.org/10.1016/j.scib.2017.06.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Hush, Michael R. "Machine learning for quantum physics." Science 355, no. 6325 (February 9, 2017): 580. http://dx.doi.org/10.1126/science.aam6564.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Sarkar, Soumyadip. "Quantum Machine Learning: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 352–54. http://dx.doi.org/10.22214/ijraset.2023.49421.

Full text

Abstract:

Abstract: Quantum machine learning is an emerging field that aims to leverage the unique properties of quantum computing to accelerate machine learning tasks. In this paper, we review recent advances in quantum machine learning and discuss the potential applications and challenges associated with this technology. Specifically, we examine the current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models. We also discuss the challenges associated with practical quantum computing resources, algorithm design, and interdisciplinary collaboration. Furthermore, we highlight the potential applications of quantum machine learning in areas such as drug discovery, speech and image recognition, financial modeling, and many others. We also examine the ethical and societal implications of this technology, including the potential impact on privacy and security. Finally, we discuss future prospects for quantum machine learning, including the potential for quantum-inspired classical algorithms and the development of error correction techniques. We conclude by emphasizing the importance of interdisciplinary collaboration in the continued advancement of this field.

APA, Harvard, Vancouver, ISO, and other styles

49

Watkins, William M., Samuel Yen-Chi Chen, and Shinjae Yoo. "Quantum machine learning with differential privacy." Scientific Reports 13, no. 1 (February 11, 2023). http://dx.doi.org/10.1038/s41598-022-24082-z.

Full text

Abstract:

AbstractQuantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.

APA, Harvard, Vancouver, ISO, and other styles

50

Moussa, Charles, Max Hunter Gordon, Michał Baczyk, Marco Cerezo, Lukasz Cincio, and Patrick J. Coles. "Resource frugal optimizer for quantum machine learning." Quantum Science and Technology, August 11, 2023. http://dx.doi.org/10.1088/2058-9565/acef55.

Full text

Abstract:

Abstract Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. However, training these algorithms can be challenging and calls for tailored optimization procedures. Specifically, QML applications can require a large shot-count overhead due to the large datasets involved. In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function. We consider a highly general loss function that encompasses many QML applications, and we show how to construct an unbiased estimator of its gradient. This allows us to propose a shot-frugal gradient descent optimizer called Refoqus (REsource Frugal Optimizer for QUantum Stochastic gradient descent). Our numerics indicate that Refoqus can save several orders of magnitude in shot cost, even relative to optimizers that sample over measurement operators alone.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Quantum Machine Learning (QML)'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles