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Bloch sphere representation of (2, 1, 0.85)-QRAC and (3, 1, 0.78)-QRAC. Each bitstring is mapped on the surface of the sphere. The distance between two quantum states is proportional to the Hamming distance of their corresponding bitstrings.
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Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational Quantum Classification (VQC) is one of...
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... is half of the bits used in the classical RACs; that is, (n, m, p)-QRACs exist for any n < 2 2m , while their classical counterparts (n, m, p)-RACs exist only for n < 2 m . The optimal QRACs for encoding up to three bits into one qubit are known: (2, 1, 0.85)-QRAC and (3, 1, 0.78)-QRAC. The Bloch sphere representation of these QRACs is shown in Fig. 1. It is also known how to construct QRACs for encoding up to 15 bits into two qubits, such as the (3, 2, 0.90)-QRAC [24]. However, the general way to construct QRACs is not known. In what follows, we often use (n, m)-QRAC to refer to (n, m, p)-QRAC, when the meaning of p is clear from the ...
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... experimental results are shown in Fig. 10 and Table 2. The depth l = 1 was used in the experiment, and the number of shots was 1024 for classifying one instance. We can see that the quantum processor achieved almost the same performance as the simulator. The reason the quantum processor performed slightly better than the simulator can be explained by Fig. 10; that is, whereas ...
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... results are shown in Fig. 10 and Table 2. The depth l = 1 was used in the experiment, and the number of shots was 1024 for classifying one instance. We can see that the quantum processor achieved almost the same performance as the simulator. The reason the quantum processor performed slightly better than the simulator can be explained by Fig. 10; that is, whereas the optimization of Fold 1 on the simulator (blue dashed line) seems to be stacked at local minima, the optimizer with the quantum processor (blue solid line) circumvents being trapped in such a plateau. This might be caused by the noise in the real device, but except for Fold 1 we confirmed that the optimization with ...
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This paper presents a hybrid classical-quantum program for density estimation and supervised classification. The program is implemented as a quantum circuit in a high-dimensional quantum computer simulator. We show that the proposed quantum protocols allow to estimate probability density functions and to make predictions in a supervised learning ma...
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... Quantum computing has introduced novel approaches to this problem, such as quantum similarity-based binary classifiers and kernel methods that utilize quantum interference and explore the quantum Hilbert space. These quantum-enhanced methods promise significant advancements in machine learning, offering new ways to harness the computational power of quantum systems for complex classification tasks [25,26]. ...
Quantum machine learning (QML) is a promising field that explores the applications of quantum computing to machine learning tasks. A significant hurdle in the advancement of quantum machine learning lies in the development of efficient and resilient quantum classifiers capable of accurately mapping input data to specific, discrete target outputs. In this paper, we propose a novel approach to improve quantum classifier performance by using a data re-uploading strategy. Re-uploading classical information into quantum states multiple times can enhance the accuracy of quantum classifiers. We investigate the effects of different cost functions, such as fidelity and trace distance, on the optimization process and the classification results. We demonstrate our approach to two classification patterns: a linear classification pattern (LCP) and a non-linear classification pattern (NLCP). We evaluate the efficacy of our approach by benchmarking it against four distinct optimization techniques: L-BFGS-B, COBYLA, Nelder-Mead, and SLSQP. Additionally, we study the different impacts of fixed datasets and random datasets. Our results show that our approach can achieve high classification accuracy and robustness and outperform the existing quantum classifier models.
... Subsequently, QML involves training the data using quantum optimization and parameter shift techniques. Lastly, the quantum measurements use observable to convert the quantum probabilities to classical results [59,60]. In this part, the technical specifics of these stages are covered. ...
Link prediction has been challenging, especially when the network is dynamic and complex. The most effective classical method for performing this task involved using machine learning algorithms with features taken from topological network indices. Even while these traditional ML algorithms perform better, they still require a lot of processing resources as the size and number of features in the network increase. This is the ideal situation where quantum computing may fit, as it provides impressive predictions and speedup arising out of quantum phenomena like superposition, entanglement, parallelization, and high dimensional space. Additionally, relatively little research has been done to examine the full potential of quantum computation for link prediction. A few of the earlier attempts are limited to projecting the features to quantum space and then using quantum-projected kernels with classical ML techniques or using hybrid classifiers by incorporating quantum enhancement in traditional random walks. We propose Parameterized Quantum Circuit based Link Prediction (PQCLP) model where we have used quantum circuits not only for projecting the classical data but also for training and optimization in quantum space using Variational Circuits Aka Ansatz which is a parameterized circuit. Here we employ two quantum methods namely Variational Quantum Classifier (VQC) and Quantum Neural Network Classifier (QNN) having classical equivalence with Support Vector Machines and Neural Networks respectively. We present here a detailed comparison of these models with their classical counterparts, within different feature categories and test ratios, and finally with a few state-of-the-art methods using several performance evaluation metrics.
... Any data that is submitted into an error-correction coding block will have random error added to it before being transferred. Code-based cryptosystems like the McEliece algorithm are frequently employed [24]. The linear error correction codes, which are based on matrix multiplication, are how the McEliece algorithm operates. ...
Cryptography is an art of hiding the significant data or information with some other codes. It is a practice and study of securing information and communication. Thus, cryptography prevents third party intervention over the data communication. The cryptography technology transforms the data into some other form to enhance security and robustness against the attacks. The thrust of enhancing the security among data transfer has been emerged ever since the need of Artificial Intelligence field came into a market. Therefore, modern way of computing cryptographic algorithm came into practice such as AES, 3DES, RSA, Diffie-Hellman and ECC. These public-key encryption techniques now in use are based on challenging discrete logarithms for elliptic curves and complex factorization. However, those two difficult problems can be effectively solved with the help of sufficient large-scale quantum computer. The Post Quantum Cryptography (PQC) aims to deal with an attacker who has a large-scale quantum computer. Therefore, it is essential to build a robust and secure cryptography algorithm against most vulnerable pre-quantum cryptography methods. That is called ‘Post Quantum Cryptography’. Therefore, the present crypto system needs to propose encryption key and signature size is very large.in addition to careful prediction of encryption/decryption time and amount of traffic over the communication wire is required. The post-quantum cryptography (PQC) article discusses different families of post-quantum cryptosystems, analyses the current status of the National Institute of Standards and Technology (NIST) post-quantum cryptography standardisation process, and looks at the difficulties faced by the PQC community.
... The quantum encoding contains object encoding and an RF classifier is used for ensemble learning, which results in accurate prediction. A VQC model for heart disease and breast cancer prediction which takes different encoding techniques into consideration is also described in [78]. During pre-processing a quantum random access coding (QRAC) was used to map the discrete future, which outperforms in terms of accuracy compared to the ZZ-FeatureMaping technique. ...
Quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference. These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the case of traditional computers. The potential impact of QC extends beyond its computational power and reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and full-text criteria, respectively. Ultimately, the remaining 49 articles were subject to evaluation, thus providing a brief overview of the recent literature and contributing to existing knowledge and comprehension of Quantum Machine Learning (QML) algorithms and their applications in the healthcare sector. This analysis establishes a foundational framework for forthcoming research and development at the intersection of QC and machine learning, ultimately paving the way for innovative approaches to addressing complex challenges within the healthcare domain.
... 2) Quantum Feature Encoding: The more compact the representation of molecules is, the more efficient and faster the analysis of potential drug candidates. QML can encode various molecular structures and properties in the quantum state [65]. 3) Quantum Neural Networks: Neural networks can understand and learn the patterns of quantum data that might not be easily perceivable using classical methods. ...
The preservation of human health is of utmost importance, and unrestricted availability of medications is essential for the sustenance of overall wellness. Pharmaceuticals, which consist of a wide range of therapeutic substances utilized to diagnose, treat, and improve various diseases and conditions, play a crucial part in the field of healthcare. However, the drug research and development process is widely recognized for its lengthy duration, demanding nature, and substantial expenses. To enhance the effectiveness of this complex process, interdisciplinary groups have converged, giving rise to the field known as “Bioinformatics”. The emergence and future advancements of Quantum Computing (QC) technologies have the potential to significantly enhance and accelerate the complex process of drug discovery and development. This paper explores various disciplines, such as Computer-Aided Drug Design (CADD), quantum simulations, quantum chemistry, and clinical trials, that stand to gain significant advantages from the rapidly advancing field of quantum technology. This study aims to explore a range of fundamental quantum principles, intending to facilitate a thorough understanding of this revolutionary technology.
... When the classical data x is a bitstring of length d, which is often used to represent discrete features, [357] proposes to utilize the so-caled Quantum Random Access Codes (QRAC) to obtain a constant factor saving in the number of qubits. For example, the previous |e i ⟩ is known as one-hot encoding in classical machine learning that requires d qubits. ...
The past decade has seen considerable progress in quantum hardware in terms of the speed, number of qubits and quantum volume which is defined as the maximum size of a quantum circuit that can be effectively implemented on a near-term quantum device. Consequently, there has also been a rise in the number of works based on the applications of Quantum Machine Learning (QML) on real hardware to attain quantum advantage over their classical counterparts. In this survey, our primary focus is on selected supervised and unsupervised learning applications implemented on quantum hardware, specifically targeting real-world scenarios. Our survey explores and highlights the current limitations of QML implementations on quantum hardware. We delve into various techniques to overcome these limitations, such as encoding techniques, ansatz structure, error mitigation, and gradient methods. Additionally, we assess the performance of these QML implementations in comparison to their classical counterparts. Finally, we conclude our survey with a discussion on the existing bottlenecks associated with applying QML on real quantum devices and propose potential solutions for overcoming these challenges in the future.
... Also, they adopted a quantum information encoding approach to reduce the number of model parameters when compared to traditional neural networks. Yano et al. (2021) [27] suggested a quantum classifier based on Quantum Random-Access Coding (QRAC) for a discrete-featured dataset. The system's major benefit is that it offers a mechanism for encoding an input bitstring to a quantum state with fewer quantum bits. ...
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... Up to now, machine learning has been employed to quantum areas. Thus far, a number of promising applications have been proposed, such as quantum metric learning [1], the gate decomposition problem [2], quantum states discrimination [3], quantum discrete feature encoding [4], quantum nodes based on variational unsampling protocols [5] and quantifying steerability [6]. ...
Quantum entanglement becomes more complicated and capricious when more than two parties are involved. There have been methods for classifying some inequivalent multipartite entanglements, such as GHZ states and W states. In this paper, based on the fact that the set of all W states is convex, we approximate the convex hull by some critical points from the inside and propose a method of classification via the tangent hyperplane. To accelerate the calculation, we bring ensemble learning of machine learning into the algorithm, thus improving the accuracy of the classification.
... A Variational Quantum Classifier (VQC) [27][28][29] is a supervised quantum-classical hybrid method widely used in NISQ devices and simulators. The cost function in the case of this algorithm is calculated using iterative measurements, which also provide the possibility of mitigating errors. ...
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.
... The encoding gates are selected based on the encoding method chosen and the number of input features. The optimal selection of the encoding method is pivotal to successful learning of a QML model as this represents the classical information to be fed into the circuit [7]- [10]. While the effect of different encoding methods in data representation and expressivity of a VQC have been studied before [10]- [14], the encoding pattern we propose (see Section II) has not been discussed in the literature previously to the best of our knowledge. ...
