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Currently, access to quantum processors is costly in terms of time, and power. There are quantum simulators and emulators on the market that offer alternatives for evaluating the behavior of a real quantum processor. However, these emulation environments present accuracy deviations from real devices, mainly because of difficult-to-model error sources. In this study, a methodology is proposed that allows the selection of noise models and adjustment of their parameters, considering the nature of the backends (technology, topology, vendor, model, etc.). The proposed methodology is illustrated using a small superconducting example based on the ibm_perth backend (seven qubits) and a comparison between the quantum emulators Qaptiva and Qiskit, where six different noise models are applied, achieving a fidelity deviation of 0.686% at best with respect to the real device. [ABSTRACT FROM AUTHOR]

Extensive research is currently underway to determine the security of existing ciphers in light of the advancements in quantum computing. Against symmetric key cryptography, Grover's search algorithm is a prominent attack, capable of reducing search costs to the square root. For using Grover's algorithm, it is imperative to embed the target cipher into a quantum circuit. Even so, this area of research is relatively new; it has garnered significant attention from the research community. In this study, we provide the first estimation of the cost of Grover's key search attack against the AES-based AEAD schemes Rocca-S, AEGIS-128, and Tiaoxin-346. Our analysis considers circuit depth restrictions specified in NIST's PQC standardization process. Considering NIST's maximum depth constraints, We present the overall cost of these attacks using gate count and depth-times-width metrics. We observed that for MAXDEPTH = 2 40 , Rocca-S, AEGIS-128, and Tiaoxin-346 can be retrieved using Grover's search algorithm with gate count of 1.09 × 2253, 1.14 × 2124, and 1.22 × 2124 respectively. Concerning the current updated values by NIST, these ciphers are secure in terms of the cost of implementing Grover's attack for key recovery. The quantum circuits of these ciphers are implemented using QISKIT, an open-source software development kit (SDK) designed for working with quantum computers running on the IBM Quantum Experience platform. [ABSTRACT FROM AUTHOR]

Abstract Extensive research is currently underway to determine the security of existing ciphers in light of the advancements in quantum computing. Against symmetric key cryptography, Grover’s search algorithm is a prominent attack, capable of reducing search costs to the square root. For using Grover’s algorithm, it is imperative to embed the target cipher into a quantum circuit. Even so, this area of research is relatively new; it has garnered significant attention from the research community. In this study, we provide the first estimation of the cost of Grover’s key search attack against the AES-based AEAD schemes Rocca-S, AEGIS-128, and Tiaoxin-346. Our analysis considers circuit depth restrictions specified in NIST’s PQC standardization process. Considering NIST’s maximum depth constraints, We present the overall cost of these attacks using gate count and depth-times-width metrics. We observed that for $$\textsf{MAXDEPTH} = 2^{40}$$ MAXDEPTH = 2 40 , Rocca-S, AEGIS-128, and Tiaoxin-346 can be retrieved using Grover’s search algorithm with gate count of 1.09 × 2253, 1.14 × 2124, and 1.22 × 2124 respectively. Concerning the current updated values by NIST, these ciphers are secure in terms of the cost of implementing Grover’s attack for key recovery. The quantum circuits of these ciphers are implemented using QISKIT, an open-source software development kit (SDK) designed for working with quantum computers running on the IBM Quantum Experience platform.

In this paper, we propose a novel protection scheme for N-qubit Greenberger–Horne–Zeilinger (GHZ) states against amplitude damping noise, using unilateral operations. The key innovation lies in the implementation of local operations on a single qubit, irrespective of the total number of qubits, thereby significantly reducing the operational complexity and resource requirements compared to existing methods. The scheme involves a unilateral rotation to transform the GHZ state into a less vulnerable configuration before exposing it to noise, followed by a weak measurement and another unilateral rotation to recover the initial state. We provide a comprehensive mathematical analysis of the proposed unilateral protection scheme, deriving expressions for both fidelity and success probability. The practical feasibility of our scheme is demonstrated through simulations using the Qiskit SDK, in which we provide the corresponding quantum circuit for performing the partial weak measurement used in our scheme. Comparative analysis with pioneer protection schemes reveals a substantial enhancement in success probability and fidelity for both GHZ and generalized GHZ states, emphasizing the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Context: Quantum computing is an exciting area, which has grown at an astonishing rate in the last decade. It is especially promising for the computational and theoretical chemistry area. One algorithm has received a lot of attention lately, the variational quantum eigensolver (VQE). It is used to solve electronic structure problems and it is suitable to the noisy intermediate-scale quantum (NISQ) hardware. VQE calculations require ansatze and one of the most known is the unitary coupled cluster (UCC). It uses the chosen basis set to generate a quantum computing circuit which will be iteratively minimized. The present work investigates the circuit depth and the number of gates as a function of basis sets and molecular size. It has been shown that for the current quantum devices, only the smallest molecules and basis sets are tractable. The H 2 molecule with the cc-pVTZ and aug-cc-pVTZ basis sets have circuit depths in the order of 10 6 to 10 7 gates and the C 2 H 6 molecule with 3–21G basis set has a circuit depth of 2.2 × 10 8 gates. At the same time the analysis demonstrates that the H 2 molecule with STO-3G basis set, requires at least 500 shots to reduce the error and that, although error mitigation schemes can diminish the error, they were not able to completely negate it. Methods: The quantum computing and electronic structure calculations were performed using the Qiskit package from IBM and the PySCF package, respectively. The ansatze were generated using the UCCSD method as implemented in Qiskit, using the basis sets STO-3G, 3–21G, 6–311G(d,p), def2-TZVP, cc-pVDZ, aug-cc-pVDZ, cc-pVTZ, and aug-cc-pVTZ. The operators and the Hamiltonian were mapped using the Jordan-Wigner scheme. The classical optimizer chosen was the simultaneous perturbation stochastic approximation (SPSA). The quantum computers used were the Nairobi and Osaka, with 7 and 127 qubits respectively. [ABSTRACT FROM AUTHOR]

Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows. [ABSTRACT FROM AUTHOR]

As the field of quantum computing rapidly advances, there is a growing demand for skilled professionals adept in quantum computing and programming. Recognizing this need, in this paper, we share our experiences teaching an introductory-level quantum computing course to students at Cleveland State University (CSU). The course integrates dedicated hands-on programming labs, allowing students to verify their experimental results with corresponding examples from the textbook. These labs cover a diverse range of topics, including fundamental elements such as quantum gates and circuits, quantum key distribution protocols, and quantum algorithms. As educators, our goal is to share teaching insights and resources with fellow instructors in the field. This article elucidates the rationale behind the design of each experiment, providing a deeper understanding of quantum computing. [ABSTRACT FROM AUTHOR]

This research aims to establish a secure system for key exchange by using post-quantum cryptography (PQC) schemes in the classic channel of quantum key distribution (QKD). Modern cryptography faces significant threats from quantum computers, which can solve classical problems rapidly. PQC schemes address critical security challenges in QKD, particularly in authentication and encryption, to ensure the reliable communication across quantum and classical channels. The other objective of this study is to balance security and communication speed among various PQC algorithms in different security levels, specifically CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon, which are finalists in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography Standardization project. The quantum channel of QKD is simulated with Qiskit, which is a comprehensive and well-supported tool in the field of quantum computing. By providing a detailed analysis of the performance of these three algorithms with Rivest–Shamir–Adleman (RSA), the results will guide companies and organizations in selecting an optimal combination for their QKD systems to achieve a reliable balance between efficiency and security. Our findings demonstrate that the implemented PQC schemes effectively address security challenges posed by quantum computers, while keeping the the performance similar to RSA. [ABSTRACT FROM AUTHOR]

The application of quantum principles in computing has garnered interest since the 1980s. Today, this concept is not only theoretical, but we have the means to design and execute techniques that leverage the quantum principles to perform calculations. The emergence of the quantum walk search technique exemplifies the practical application of quantum concepts and their potential to revolutionize information technologies. It promises to be versatile and may be applied to various problems. For example, the coined quantum walk search allows for identifying a marked item in a combinatorial search space, such as the quantum hypercube. The quantum hypercube organizes the qubits such that the qubit states represent the vertices and the edges represent the transitions to the states differing by one qubit state. It offers a novel framework to represent k-mer graphs in the quantum realm. Thus, the quantum hypercube facilitates the exploitation of parallelism, which is made possible through superposition and entanglement to search for a marked k-mer. However, as found in the analysis of the results, the search is only sometimes successful in hitting the target. Thus, through a meticulous examination of the quantum walk search circuit outcomes, evaluating what input-target combinations are useful, and a visionary exploration of DNA k-mer search, this paper opens the door to innovative possibilities, laying down the groundwork for further research to bridge the gap between theoretical conjecture in quantum computing and a tangible impact in bioinformatics.

This paper proposed the design and development of reversible cost-efficient innovative quantum dual-full adder and subtractor or QD-FAS circuit using quantum gate. The proposed circuit can be used as full adder and full subtractor simultaneously, which is designed using double Peres gate or DPG and Feynman gate or FG. The quantum cost, garbage output and constant input of the QD-FAS is 8, 1 and 1. Which is better w.r.t previously reported work. The QD-FAS circuit, as proposed, includes shared sum and difference terminals, as well as a carry-out and a borrow output terminal. Notably, this innovation showcases a remarkable 27.27% reduction in quantum cost. The improvement in garbage output is even more striking, showing a 50% enhancement. When assessing the overall advancement in quantum cost, it falls within the range of 27.27% to 66.66%. To confirm the viability of this design, extensive testing is carried out using the IBM Qiskit simulator. This design holds significant importance in a variety of applications, including quantum computing, cryptography, and the realm of reversible Arithmetic Logic Units (ALU).

This paper proposed the design and development of reversible cost-efficient innovative quantum dual-full adder and subtractor or QD-FAS circuit using quantum gate. The proposed circuit can be used as full adder and full subtractor simultaneously, which is designed using double Peres gate or DPG and Feynman gate or FG. The quantum cost, garbage output and constant input of the QD-FAS is 8, 1 and 1. Which is better w.r.t previously reported work. The QD-FAS circuit, as proposed, includes shared sum and difference terminals, as well as a carry-out and a borrow output terminal. Notably, this innovation showcases a remarkable 27.27% reduction in quantum cost. The improvement in garbage output is even more striking, showing a 50% enhancement. When assessing the overall advancement in quantum cost, it falls within the range of 27.27% to 66.66%. To confirm the viability of this design, extensive testing is carried out using the IBM Qiskit simulator. This design holds significant importance in a variety of applications, including quantum computing, cryptography, and the realm of reversible Arithmetic Logic Units (ALU). [ABSTRACT FROM AUTHOR]

Quantum multidimensional scaling is a quantum dimensionality reduction algorithm. Its complex quantum circuit design structure and excessive qubits consumption make it difficult to run on the current quantum computers. In order to solve this problem, this paper proposes the variational quantum multidimensional scaling algorithm based on the variational quantum algorithm. Utilizing the parallel advantages of quantum computing to quickly compute low-dimensional embeddings of high-dimensional data, the variational quantum multidimensional scaling algorithm can provide lower time complexity; compared with the non-variational quantum multidimensional scaling algorithm, the variational quantum multidimensional scaling algorithm provides a simpler quantum circuit. In the noisy intermediate scale quantum era, the algorithm can run on a quantum computer. In addition, the article finally implemented the variational quantum multidimensional scaling algorithm on the Qiskit framework, proving the correctness of the algorithm. [ABSTRACT FROM AUTHOR]

Skin diseases affect millions of people worldwide, leading to significant healthcare burdens and challenges in diagnosis and treatment. In the past few years, machine learning techniques have demonstrated potential in assisting dermatologists with diagnosing various skin conditions. As in, conventional machine learning algorithms might encounter challenges in handling the complexity and distinction of skin disease classification tasks, primarily because of the intricate nature of medical image data with its high dimensional properties. In this work, the main analysis is done based on exploring quantum machine learning models for skin disease classification. This approach blends with the aspects of quantum computing with the conventional machine learning techniques to push the boundaries of skin disease classification. This work harnesses the HAM10000 dataset, an extensive compilation of categorized images portraying common skin lesions, to train and assess the efficacy of the proposed methodologies. Quantum computing libraries such as PennyLane and Qiskit is used in this study. Using different combination of qubit rotation encoding and decoding using three types of Pauli gates such as Pauli X, Y and Z gates are implemented and compared using proposed Quanvolutional neural network. Features extracted using MobileNet pre-trained network is used to build Quantum support vector classifier. These quantum machine learning models are compared with some well-known pre-trained models such as Resnet50, Inception-Resnet, Densenet121, DenseNet201 and MobileNet. The combination of RY qubit rotation and PauliZ gate in quantum convolution layer in Quanvolutional neural network produced the optimal classification accuracy of 82.86% more than any other models included in this study. In contrast, Quantum Support Vector Classifier produced similar classification accuracy of 72.5% with respect to pre-trained models.

A chaos-based cryptosystem requires an extremely nonlinear chaotic map with many chaotic regions. Quantum computers efficiently provide speed and security in image communication. A new two-dimensional triangle function combined with a discrete chaotic map (2D-TFCDM) is proposed in this research. The generated map is tested on various grounds such as attractor plot, bifurcation diagram, sensitivity test, Lyapunov exponent, 0–1 Test, permutation entropy and National Institute of Standards and Technology (NIST) test suite. The proposed map combined with the Secure Hash Algorithm (SHA) is utilised in image cryptography applications. Furthermore, the encrypted image is communicated by the novel enhanced quantum representation (NEQR) method using the qasm_simulator of IBM quantum computer (Qiskit) to utilise the benefits of the laws of physics to secure data. The numerical analyses are done, and simulation results are compared with recent techniques that depict the effectiveness of the image encryption method in resisting various attacks. [ABSTRACT FROM AUTHOR]

Quantum logic gates differ from classical logic gates as the former involves quantum operators. The conventional gates such as AND, OR, NOT etc., are generally classified as classical gates, however, some of the quantum gates are known as Pauli gates, Toffoli gates and Hadamard gates, respectively. Normally classical states only involve 0 and 1, whereas quantum states involve the superpositions of 0 and 1. Hence, underlying principles of algorithm implementation for classical logic gate and quantum logic gate are indeed different. In this paper, we introduce significant concepts of quantum computations, analyse the discrepancy between classical and quantum gates, compare quantum algorithms using Qiskit against equivalent classical algorithms and analyse their performance in terms of runtime. [ABSTRACT FROM AUTHOR]

In secure IT systems, a One Time Password (OTP) is used. They can only be used once for a transaction or a login session. It is usually used for multi-factor authentication and is one of the security services based on human responses. In the field of cryptography, the creation of pseudo-random bitstreams is similar to how one-time passwords (OTPs) work. In modern computers, the security of cryptographic procedures is greatly enhanced by the use of random numbers. The use of a Quantum True Random Number Generator (QTRNG) makes it possible to replace periodic sequences that appear random with real random data. A QTRNG is used in this research on a real Quantum Computational Device (QCD) in a local environment. The IBM Quantum Experience Qiskit platform is used to build a random number generator. Qiskit is an open source platform that provides users to work with actual quantum devices for simulating quantum algorithms, different gates and circuits on IBM Quantum Experience or on simulators. The random number is investigated with different qubits (quantum bits) to generate random bits that can be used for One Time Password Generation. [ABSTRACT FROM AUTHOR]

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In this work, we present a quantum circuit for a binary classification prediction algorithm using a random forest model. The quantum prediction algorithm is presented in our previous works. We construct a circuit and implement it using qiskit tools (python module for quantum programming). One of our goals is reducing the number of basic quantum gates (elementary gates). The set of basic quantum gates which we use in this work consists of single-qubit gates and a controlled NOT gate. The number of CNOT gates in our circuit is estimated by , when trivial circuit decomposition techniques give CNOT gates, where is the number of trees in a random forest model, is a tree height and is the length of attributes of an input object . The prediction process returns an index of the corresponding class for the input . [ABSTRACT FROM AUTHOR]

In this study, we design an algorithm to work on gate-based quantum computers. Based on the algorithm, we construct a quantum circuit that represents the surplus process of a cedant under a reinsurance agreement. This circuit takes into account a variety of factors: initial reserve, insurance premium, reinsurance premium, and specific amounts related to claims, retention, and deductibles for two different non-proportional reinsurance contracts. Additionally, we demonstrate how to perturb the actuarial stochastic process using Hadamard gates to account for unpredictable damage. We conclude by presenting graphs and numerical results to validate our capital modelling approach. [ABSTRACT FROM AUTHOR]

In this study, we design an algorithm to work on gate-based quantum computers. Based on the algorithm, we construct a quantum circuit that represents the surplus process of a cedant under a reinsurance agreement. This circuit takes into account a variety of factors: initial reserve, insurance premium, reinsurance premium, and specific amounts related to claims, retention, and deductibles for two different non-proportional reinsurance contracts. Additionally, we demonstrate how to perturb the actuarial stochastic process using Hadamard gates to account for unpredictable damage. We conclude by presenting graphs and numerical results to validate our capital modelling approach.

Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows.

The integer factorization problem is a major challenge in the field of computer science, and Shor’s algorithm provides a promising solution for this problem. However, Shor’s algorithm involves complex modular exponentiation computation, which leads to the construction of complicated quantum circuits. Moreover, the precision of continued fraction computations in Shor’s algorithm is influenced by the number of qubits, making it difficult to implement the algorithm on Noisy Intermediate-Scale Quantum (NISQ) computers. To address these issues, this paper proposes variational quantum computation integer factorization (VQCIF) algorithm based on variational quantum algorithm (VQA). Inspired by classical computing, this algorithm utilizes the parallelism of quantum computing to calculate the product of parameterized quantum states. Subsequently, the quantum multi-control gate is used to map the product satisfying p q = N onto an auxiliary qubit. Then the variational quantum circuit is adjusted by the optimizer, and it is possible to obtain a prime factor of the integer N with a high probability. While maintaining generality, VQCIF has a simple quantum circuit structure and requires only 2 n + 1 qubits. Furthermore, the time complexity is exponentially accelerated. VQCIF algorithm is implemented using the Qiskit framework, and tests are conducted on factorization instances to demonstrate its feasibility. [ABSTRACT FROM AUTHOR]

The max independent set problem is a well-known NP problem and has applications in many scenarios. Traditional exact algorithms need exponential time complexity to solve the max independent set problem. In order to solve the max independent set problem more efficiently, proposes a quantum circuit solution based on the quantum approximate optimization algorithm. In this scheme, derives the Hamiltonian expression of the max independent set problem from the mathematical model of the max independent set, designes the quantum circuit based on the quantum approximation optimization algorithm. Using the COBYLA classical optimization algorithm to optimize the parameters in the parameter quantum gate, and using the quantum development framework Qiskit provided by IBM to conduct simulation experiments. Simulation results show that the solution of the max independent set problem can be obtained in polynomial time with high probability using the quantum approximate optimization algorithm, achieving an exponential speedup. Quantum approximate optimization algorithm is feasible and effective for solving the max independent set problem. [ABSTRACT FROM AUTHOR]

Trong bài báo này, nhóm tác giả đề xuất sử dụng nền tảng phần mềm hỗ trợ làm việc với máy tính lượng tử Qiskit để nghiên cứu các thuật toán lượng tử sau khi đối sánh các đặc tính của bốn nền tảng phổ biến là Forest, ProjectQ, QDK và Qiskit. Cùng với đó, nhóm tác giả khai triển hợp số N = 15 thành các thừa số bằng thuật toán lượng tử Shor và chạy chúng trên máy tính lượng tử IBM thông qua cloud của nền tảng Qiskit. Kết quả cho thấy, với một bài toán gần như được xem là bất khả thi đối với thuật toán cổ điển lại có thể dễ dàng được giải bằng thuật toán lượng tử nhờ các tính chất lượng tử thông qua việc chỉ ra các tính chất và “hành xử” của vật lí lượng tử trong từng bước của thuật toán.

True random numbers are crucial in many applications ranging from stochastic simulations to many other applications, especially cryptography. The quest for true randomness is, in general, considered to be impossible with only classical means. This insight enables the construction of various proposals for producing a good random number generator in realistic quantum scenarios. We presented a new fast 24 qubits quantum true random number generator (QTRNG) based on rotation and phase quantum gates on IBM’s cloud platform. It is a provable true random number since it is based on the quantum process and experimented with through QISKIT. The raw output of the generator reveals the foundational unpredictability of quantum mechanics inherent nature, which is different from classical physics. The achievable eminence of the numbers generated from a practical carrying out can differ from the theoretically promising solution. The proposed Quantum True Random Number Generation is accomplished through IBM quantum lab, and its architecture results also passed National Institute of Standards and Technology (NIST) statistical tests 800-90B and 800-22. This method is relatively efficient since it can be implemented locally on our devices. The offered methodology 224 bitstream data has higher certification randomness. The quality of the generated random bitstreams is investigated through restart analysis and autocorrelation analysis. The resulting output passes the NIST standard statistical test with a min-entropy value of 0.000712. [ABSTRACT FROM AUTHOR]

This article explores the Hardware-Efficient Quantum Search Algorithm and compares it with other well-known counterparts. Escalating the count of qubits may elevate susceptibility to errors, particularly in iterative algorithms such as Grover’s. Conversely, Noisy-Intermediate-Scale-Quantum (NISQ) computers encounter limitation in the number of gates necessary for excecution of any quantum queries. Thus, we utilize hardware-efficient quantum search algorithm for further investigation due to its optimized circuit depth. Moreover, the Qiskit library and Matlab are used for validation of the analysis. Furthermore, the noise effects, encompassing phase-damping (PD) and amplitude-damping (AD) noises, are explored to present a comparative analsysis of various search algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an Efficient Framework for Quantum Video (EFQV) in quantum computer. EFQV adds time index information into different frames on BRQI (a Quantum Image Representation based on Bitplanes, BRQI) images, and each bitplanes quantum image adds a time index. Complement of Colors (COC) operator, Reverse of Bitplanes (ROB) operator, and Shift of Frames (SOF) operator are designed for EFQV and its operators are tested on the IBM quantum computation framework qiskit. Experimental results show the effectiveness of EFQV and its operations on quantum computing environment. Compared with other frameworks, EFQV has advantages in time complexity and quantum cost. [ABSTRACT FROM AUTHOR]

This paper investigates quantum error correction schemes for fully-correlated noise channels on an n-qubit system, where error operators take the form W ⊗ n , with W being an arbitrary 2 × 2 unitary operator. In previous literature, a recursive quantum error correction scheme can be used to protect k qubits using (k + 1) -qubit ancilla. We implement this scheme on 3-qubit and 5-qubit channels using the IBM quantum computers, where we uncover an error in the previous paper related to the decomposition of the encoding/decoding operator into elementary quantum gates. Here, we present a modified encoding/decoding operator that can be efficiently decomposed into (a) standard gates available in the qiskit library and (b) basic gates comprised of single-qubit gates and CNOT gates. Since IBM quantum computers perform relatively better with fewer basic gates, a more efficient decomposition gives more accurate results. Our experiments highlight the importance of an efficient decomposition for the encoding/decoding operators and demonstrate the effectiveness of our proposed schemes in correcting quantum errors. Furthermore, we explore a special type of channel with error operators of the form σ x ⊗ n , σ y ⊗ n and σ z ⊗ n , where σ x , σ y , σ z are the Pauli matrices. For these channels, we implement a hybrid quantum error correction scheme that protects both quantum and classical information using IBM's quantum computers. We conduct experiments for n = 3 , 4 , 5 and show significant improvements compared to recent work. [ABSTRACT FROM AUTHOR]

This article introduces two controlled quantum broadcast protocols which they utilize 6-qubit and 6n-qubit (generalized scheme) cluster states as the quantum channel, respectively. The proposed protocols involve Alice broadcasting a defined transition state to the recipients under Charlie's control. Simulations conducted on the IBMQ platform and Qiskit library demonstrate the efficacy of the proposed protocols for random input states and various measurement shots. The impact of different types of quantum noises on the protocol are analyzed, and the fidelity metric is employed to evaluate the quality of broadcasted states over a noisy channel. Furthermore, suggestions regarding security risks and the robustness of the proposed protocols under various attack scenarios are presented in this article. [ABSTRACT FROM AUTHOR]

In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, this paper introduces a novel dataset based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results. Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms, such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers such as XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes. Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field. To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository. [ABSTRACT FROM AUTHOR]