dataesr/scientific-paragraphs-categorization huggingface dataset:
The qubits thus have simultaneously all the possible values with 2 n+1 combinations of values. It is easy to understand why this quantum algorithm is much more efficient than its conventional counterpart. In conventional computation, more than half of the possible input values would have to be sequentially scanned, whereas, in the quantum version, they are all scanned at the same time. This is an example of a strong algorithm that has no known practical use. Moreover, there are classical probabilistic algorithms algorithms that erase a good part of the quantum power gain of the Deutsch-Jozsa algorithm. Grover's algorithm is the other popular algorithm  [1] , created in 1996. It allows for a fast quantum search in a database. A bit like the Deutsch-Jorza algorithm, it scans a list of elements to find those that verify a specific condition.
It also uses the superposition of qubit states to speed up the processing compared to a traditional sequential search. The improvement in performance is significant compared to an unsorted database. Grover's algorithm also uses an "oracle" or "black box" function that will indicate whether a set of input qubits verifies a search condition or not, such as verifying that a given phone number has been found in a list of phone numbers. In such a case, the function compares the phone number searched and the one submitted to it, to answer one if they are identical and zero otherwise. The black box is a quantum box and will evaluate this function for 2N registers of qubits at the same time. It will therefore output a one once and zeros elsewhere. The computing time is determined by the square root of the list size, while a classical approach has a computation time proportional to the size of the list.
For instance, if we wish to obtain a complete explanation of the state of a computing device by taking a few measurements, we may analyze the measurement data using machine learning  [49]. There are several applications for machine learning to distinguish either quantum states generated by a quantum source or manipulations carried out by a quantum experiment  [50, 51]. The scenario CQ uses quantum computing to examine conventional datasets. Observations from classical computers, which include images and text, are supplied to a quantum device for evaluation. The primary objective of the CQ methodology is the development of quantum algorithms to be used in data mining, for which the community has offered a variety of solutions. They might be adaptations of traditional machine learning models to function with quantum algorithms, or they can be entirely novel creations inspired by the characteristics of quantum computing.
Grover's Algorithm The speed at which database components may be accessed is one of the possible benefits of quantum computing above traditional computing. Such an algorithm is Grover's algorithm, which may yield a quadratic speedup while searching a database. The algorithm of Grover  [1]  employs the amplitude amplification approach  [67]  that is used in database search as well as in several other applications. Assuming we have such a database containing N = 2 n entries, and we wish to find the one indexed by w, which we refer to as the winner. The computational basis states |x that correspond to n input quantum bits may be used to define the N items. The oracle operates on every one of the computational basis states |x ∈ {0, 1} n with the output f (x) = 1, while for the other elements, it returns 0.
• Quantum random access memory (QRAM) data translation: Preparing the collected data as input into the quantum device for computation is among the difficult tasks in QML. QRAM aids in transforming a collection of classical data into its quantum counterpart. QRAM requires O(log 2 d) steps to retrieve data from storage in order to reconstruct a state, with d representing the feature vector's dimension; • Computation of the kernel matrix: The kernel matrix is mostly determined by the examination of the dot product. Therefore, if we obtain speedup benefits of the dot product performance in the quantum approach, this will result in an overall speedup increase in the computation of the kernel matrix. With the use of quantum characteristics, the authors of  [5]  provide a quicker method for calculating dot products.
Implementation The architecture that we have implemented is inspired from  [77] , as we have already mentioned. The goal is to encode the real vectors as amplitude vectors (amplitude encoding) and training the model to detect the first two classes of the Iris dataset. • Dataset: Our classification data sets made up of three sorts of irises (Setosa, Versicolour, and Virginica), and containing four features that are Sepal Length, Sepal Width, Petal Length and Petal Width. In our implementation, we used the two first classes: • Implemented models: -VQC: The Variational Quantum Classifiers commonly define a "layer", whose fundamental circuit design is replicated to create the variational circuit. Our circuit layer is composed of a random rotation upon each qubit, and also a CNOT gate that entangles each qubit with its neighbor.
The classical data were encoded to amplitude vectors via amplitude encoding; -SVC: A support vector classifier (SVC) implemented by the sklearn Python library; -Decision Tree: Is a non-parametric learning algorithm which anticipates the target variable through learning decision rules; -Naive Bayes: Naive Bayes classifiers utilize Bayes theory with the assumption of conditional independence in between each pair of features; • Experimental Environment: We use the Jupyter Notebook and PennyLane  [80]  (A cross-platform Python framework for discrete programming of quantum computing) for developing all the codes and executing them on IBM Quantum simulators  [81]. We implemented three classical Scikitlearn  [82]  algorithms to accomplish the same classification task on a conventional computer in order to compare their performance with that of the VQC.
SVM vs. QSVM Quantum Support Vector Machine (QSVM) is a quantum variant of the standard SVM algorithm that uses quantum principles to perform computations. QSVM employs the advantages of quantum hardware and quantum software to enhance the performance of classic SVM algorithms that perform on classical computers with GPUs or CPUs. The initial procedure QSVM is to encode the conventional data which contains specific categories (class, features, dimension, etc. ) giving the information about the data we wish to classify. Current quantum hardware functions are yet to achieve full capability as there is a limited number of qubits accessible. Therefore, we need to decrease the features of the data to render it compatible with the available qubits. The principal component analysis (PCA) is typically employed in machine learning for this kind of procedure.
• Breast cancer dataset: The Wisconsin Diagnostic Breast Cancer dataset (WDBC) of UCI machine learning repository is a classification dataset that includes breast cancer case metrics. There are two categories: benign and malignant. This dataset contains information on 31 characteristics that define a tumor, among which are: average radius, mean perimeter, mean texture, etc. , and a total of 569 records and 31 features; • Principal Component Analysis: The existing quantum computers are yet to attain full potential as their functions are constrained by the limited number of accessible qubits, noise, and decoherence. Thereby, a dataset with exceptionally big dimensionality is difficult to be implemented. In addition, this is where Principal Component Analysis (PCA) comes to help. As mentioned above, PCA is the technique that reduces the huge dimension into smaller-scaled dimensions keeping the correlation given in the dataset.
The principal components are orthogonal since they are the eigenvectors of the covariance matrix. Generally, we can assume that the data summarized can be processed by a quantum computer. Using PCA offers us the flexibility to leave out some of the components without losing much information and therefore minimizing the complexity of the problem as illustrated in Figure  15 , where we reduced our dataset in order to handle it with a quantum computer; • Quantum Feature Map: As we discussed earlier, the Kernel techniques are the set of algorithms for pattern analysis or recognition of the data points. Kernel techniques map data to higher dimensional spaces in order to ease data structure and separability. The data may be translated not only to higher-level space but to an endless dimension. Kernel "trick" is the technique of replacing the inner products of two vectors in our algorithm with the kernel function.
In other hand, we have Quantum Kernel techniques which is a method for identifying a hyperplane that is performing a nonlinear transformation to the data, This is termed as a "feature map". We may either apply available Qiskit (an open-source quantum computing framework created by IBM. ) feature maps including ZZ feature map, Z feature map, or a Pauli feature map that has Pauli (X, Y, Z) gates, or create a custom feature map based on the dataset compatibility. The quantum feature map can be built by using Hadamard gates with entangling unitary gates between them. In our case, we used the ZZ feature map.
Results We have implemented SVM on the breast cancer dataset from the Scikit learn library in a classical computer using SVM as well as using QSVM on a quantum simulator in the IBM cloud using its quantum machine learning library Qiskit  [81] , in order to compare their performances. As we can observe from Figure  16 , the QSVM has predicted accurately the classes of our test data, and we obtained an accuracy around 90%. Notice that, on the diagonal of the kernel matrix, we have only black squares, which means that each point is at a distance of zero from itself. In addition, what we are seeing in the kernel matrix in Figures  16  and 17 , is the calculated distance of the kernel in the space with higher dimensions. Thus, we may say that, with QSVM, our dataset can simply classify, due to the quantum feature map method which encodes data in a space with a higher dimension.
This technique for converting classical data using quanvolutional filters can be formalized as follows and illustrated in Figure  19:  1. Let us simply begin with a basic quanvolutional filter. The latter filter employs the arbitrary quantum circuit q that accepts as inputs subsections of images of dataset a u. Each input is denoted by the letter u x , with each u x being a two-dimensional matrix of length n-by-n, where n is greater than 1;.
Results Before analyzing the performance of the QNN model and traditional CNN results, we should first confirm that the model works as planned; the addition of the quanvolutional layer to the model has resulted in a highest accuracy, and the more the training iterations there are, the more the accurate the model becomes. The value of the loss function in the QNN models is quite low, which shows that our model has fewer errors during testing. As shown in Table  5  below, the QNN has a slightly higher accuracy than the CNN which indicates that our QNN model has well predicted our test data which are not used in the training of the model. We can conclude that the generated quantum features were effective in constructing features for the MNIST data set for classification purposes. In Figure  20 , we clearly observe the reduction of the sampling of the resolution and certain local deformations generated by our quanvolutional layer.
Conclusions and Perspectives This article reviews a selection of the most recent research on QML algorithms for a variety of issues which are more accurate and effective than classical algorithms in classical computing, especially focusing on the quantum variant of certain supervised machine learning algorithms that rely on the VQC. In addition, we discuss numerous ways for mapping data into quantum computers. We covered approaches of quantum machine learning such as quantum sub-processes. In addition, then, we compared the performance of various QML algorithms such as QSVM, VQC, and QNN with that of its classical counterparts. The QSVM algorithm exceeded the classical SVM in performance and speed up, but when it performed on a small subset of the dataset. Most quantum machine learning techniques are at some point restricted by the absence of an appropriate quantum RAM (QRAM) which facilitates the mapping of classical input to a quantum computer.
The number of practical machine learning algorithms in the area of QML is expected to rise as QRAM implementations reach a stable state. The VQC obtains a greater advantage over certain classical classifiers when implemented on a quantum computer with a smaller circuit depth, employing only two qubits to encode a subset of the dataset. Furthermore, we aim for a similar result on a large dataset with the existence of large number of qubits. The result produced by the QNN demonstrated that the model can be a solution in several classification tasks, and it is a more efficient and effective learning model when employed with a classical CNN model. Moreover, the QNN model may also predict highly efficient results in much more sophisticated and large-scale training using quantum computers of the NISQ (Noisy Intermediate-Scale Quantum) era.
In conclusion, comprehensive benchmarks on large datasets are needed to systematically examine the influence of growing input data. In addition, in the near-term, the quantum encoding (feature maps) approach is a strong and theoretically interesting approach to think about in order to successfully implement quantum machine learning algorithms. As a future work, we are currently developing new quantum machine learning algorithms that are able to handle a large actual world datasets which can be handled on currently quantum computers. Our next objective is to examine the implementation of classical machine learning models on quantum computers.
Introduction Accurately identifying organisms observed in the wild is an essential step in ecological studies. Unfortunately, observing and identifying living organisms requires high levels of expertise. For instance, plants alone account for more than 400,000 different species and the distinctions between them can be quite subtle. Since the Rio Conference of 1992, this taxonomic gap has been recognized as one of the major obstacles to the global implementation of the Convention on Biological Diversity  [1]. In 2004, Gaston and O'Neill  [6]  discussed the potential of automated approaches for species identification. They suggested that, if the scientific community were able to (i) produce large training datasets, (ii) precisely evaluate error rates, (iii) scale-up automated approaches, and (iv) detect novel species, then it would be possible to develop a generic automated species identification system that would open up new vistas for research in biology and related fields.
e. , with or without a strong revision of species names by human experts). The trusted training subset will be based on a dataset of more than 2. 8 million images, covering more than 80,000 plant species, shared and aggregated by the Global Biodiversity Information Facility (GBIF) platform. The web training dataset will be based on a web crawl with Google & Bing search engines. All datasets provided in previous editions of PlantCLEF can also be used; the use of external data will be possible. Finally, the test set will contain more than 60k pictures verified by world class experts related to various regions of the world and taxonomic groups. Task description: the task will be evaluated as a plant species retrieval task based on multi-image plant observations from the test set. The goal will be to retrieve the correct plant species among the top results of a ranked list of species returned by the evaluated system.
BirdCLEF 202Challenge: Bird species identification in soundscape recordings Motivation: Recognizing bird sounds in complex soundscapes is an important sampling tool that often helps reduce the limitations of point counts. In the future, archives of recorded soundscapes will become increasingly valuable as the habitats in which they were recorded will be lost. In the past few years, deep learning approaches have transformed the field of automated soundscape analysis. Yet, when training data is sparse, detection systems struggle with the recognition of rare species. The goal of this competition is to establish training and test datasets that can serve as real-world applicable evaluation scenarios for endangered habitats and help the scientific community to advance their conservation efforts through automated bird sound recognition.
Data collection: We will build on the experience from previous editions and adjust the overall task to encourage participants to focus on few-shot learning and task-specific model designs. We will select training and test data to suit this demand. As in previous iterations, Xeno-canto will be the primary source for training data, expertly annotated soundscape recordings will be used for testing. We will focus on bird species for which there is limited training data, but we will also include common species so that participants can train good recognition systems. In search of suitable test data, we will consider different data sources with varying complexity (call density, chorus, signal-to-noise ratio, an-thropophony…), and quality (mono and stereo recordings). We also want to focus on very specific real-world use cases (e.
g. , conservation efforts in India) and frame the competition based on the demand of the particular use case. Additionally, we are considering including unlabeled data to encourage self-supervised learning regimes. Task description: The challenge will be held on Kaggle and the evaluation mode will resemble the 2022 test mode (i. e. , hidden test data, code competition). We will use established metrics like F1 score and LwLRAP which reflect use cases for which precision is key and also allow organizers to assess system performance independent of fine-tuned confidence thresholds. Participants will be asked to return a list of species for short audio segments extracted from labeled soundscape data. In the past, we used 5-second segments, and we will consider increasing the duration of these context windows to better reflect the overall ground truth label distribution.
GeoLifeCLEF 2023 Challenge: Species presence prediction based on occurrences data and high-resolution remote sensing images Motivation: Predicting which species are present in a given area through species distribution modeling is a central problem in ecology and a crucial issue for biodiversity conservation. Such predictions are a fundamental element of many decision-making processes, whether for land use planning, the definition of protected areas or the implementation of more ecological agricultural practices. The models classically used in ecology are well-established but have the drawback of covering only a limited number of species at spatial resolutions often coarse in the order of kilometers or hundreds of meters at best. The objective of GeoLifeCLEF is to evaluate models with orders of magnitude hitherto unseen, whether in terms of the number of species covered (thousands), spatial resolution (on the order of 10 meters), or the number of occurrences used as training data (several million).
These models have the potential to greatly improve biodiversity management processes, especially at the local level (e. g. municipalities), where the need for spatial and taxonomic precision is greatest. Data collection: A brand new dataset will be built for the 2023 edition of GeoLifeCLEF in the framework of a large-scale European project on biodiver-sity monitoring (MAMBO, Horizon EU program). It will contain about 5 million species occurrences extracted from various selected datasets of the Global Biodiversity Information Facility (GBIF) and covering the whole EU territory (38 countries including E. U. members). For the explanatory variables, we will provide both high resolution remote sensing data (i. e. , Sentinel-2 RGB, Near-IR, Red-Edge and SWIR, along with altitude) and coarser resolution environmental raster data (e.
Data collection: The dataset of the previous year will be extended up to ≈ 1, 800% snake species from around the world (minimum 10 images per species). The images will be divided into observations that depict the same snake individual. Additionally, medical importance (i. e. how venomous the species is) and country-species relevance will be provided for each species. The evaluation will be done on various subsets of a newly created "secret" test set with around 50k images. Task description: Given the set of authentic snake species observations and corresponding locations, the goal of the task is to create a classification model that, for each observation, returns a ranked list of predicted species. The classification model will have to fit limits for memory footprint (ONNX model with max size of 1GB) and prediction time limit (will be announced later) measured on the submission server.
Data collection: The dataset comes from a citizen science project, the Atlas of Danish Fungi, where all samples went through an expert validation process, guaranteeing a high quality of labels. Rich metadata (Habitat, Substrate, Timestamp, GPS, EXIF etc. ) are provided for most samples. The training set will be the union of the training and test set (without out-of-scope samples) from the 2022 challenge (i. e. 295,938 training images belonging to 1,604 species observed mostly in Denmark). The test set will consist of new fungi observations from the citizen science project. Task description: Given the set of real fungi species observations and corresponding metadata, the goal of the task is to create a classification model that, for each observation, returns a ranked list of predicted species.
Discussion and Conclusion To fully reach its objective, an evaluation campaign such as LifeCLEF requires a long-term research effort so as to (i) encourage non-incremental contributions, (ii) measure consistent performance gaps, (iii) progressively scale-up the problem and (iv) enable the emergence of a strong community. The 2023 edition of the lab supports this vision and also includes the following innovations: -The GeoLifeCLEF challenge will be entirely revisited towards running at the scale of the whole Europe thanks to a newly created dataset with millions of occurrences paired with high-resolution remote sensing data. -The BirdCLEF challenge will include new data with a focus on Central African species. -The world's coverage of PlantCLEF test set will be improved with a focus on tropical regions and biodiversity hotspots.
New isolates were identified from publicly available chestnut HTS data. Incidence and genetic variability of ChMV were studied using samples from France and Italy  (Marais et al. , 2021). Another example is papaya virus X (PapVX), first identified in diseased papaya crops from northwest Argentina using HTS. Viral particles were confirmed with electron microscopy, and after obtaining a complete genome sequence, the genome organization and provisional taxonomic assignation were done. In addition, publicly available transcriptome datasets were also explored for other isolates of PapVX. The phylogenetic relationships were studied at nucleotide and amino acid levels for the RNA replicase (RdRp) and coat protein (CP) sequences and the complete genome. Mechanical inoculations were done to study the host range of PapVX, and a local survey in the northern region of Argentina was conducted to determine the distribution of the novel virus  (Cabrera Mederos et al.