Search Results (740)

Zero-day attacks pose a persistent threat to computing infrastructure by exploiting previously unknown software vulnerabilities that evade traditional signature-based network intrusion detection systems (NIDSs). To address this limitation, machine learning (ML) techniques offer a promising approach for enhancing anomaly detection in network traffic. This study evaluates several ML models on a labeled network traffic dataset, with a focus on zero-day attack detection. Ensemble learning methods, particularly eXtreme gradient boosting (XGBoost), achieved perfect classification, identifying all 6231 zero-day instances without false positives and maintaining efficient training and prediction times. While classical support vector machines (SVMs) performed modestly at 64% accuracy, their performance improved to 98% with the use of the borderline synthetic minority oversampling technique (SMOTE) and SMOTE + edited nearest neighbours (SMOTEENN). To explore quantum-enhanced alternatives, a quantum SVM (QSVM) is implemented using three-qubit and four-qubit quantum circuits simulated on the aer_simulator_statevector. The QSVM achieved high accuracy (99.89%) and strong F1-scores (98.95%), indicating that nonlinear quantum feature maps (QFMs) can increase sensitivity to zero-day exploit patterns. Unlike prior work that applies standard quantum kernels, this study introduces a parameterised quantum feature encoding scheme, where each classical feature is mapped using a nonlinear function tuned by a set of learnable parameters. Additionally, a sparse entanglement topology is derived from mutual information between features, ensuring a compact and data-adaptive quantum circuit that aligns with the resource constraints of noisy intermediate-scale quantum (NISQ) devices. Our contribution lies in formalising a quantum circuit design that enables scalable, expressive, and generalisable quantum architectures tailored for zero-day attack detection. This extends beyond conventional usage of QSVMs by offering a principled approach to quantum circuit construction for cybersecurity. While these findings are obtained via noiseless simulation, they provide a theoretical proof of concept for the viability of quantum ML (QML) in network security. Future work should target real quantum hardware execution and adaptive sampling techniques to assess robustness under decoherence, gate errors, and dynamic threat environments. Full article

Eye-written digit recognition presents a promising alternative communication method for individuals affected by amyotrophic lateral sclerosis. However, the development of robust models in this field is limited by the availability of datasets, due to the complex and unstable procedure of collecting eye-written samples. Previous work has proposed both conventional techniques and deep neural networks to classify eye-written digits, achieving moderate to high accuracy with variability across runs. In this study, we explore the potential of quantum machine learning by presenting a hybrid quantum–classical model that integrates a variational quantum circuit into a classical deep neural network architecture. While classical models already achieve strong performance, this work examines the potential of quantum-enhanced models to achieve such performance with fewer parameters and greater expressive capacity. To further improve robustness and stability, we employ an ensemble strategy that aggregates predictions from multiple trained instances of the hybrid model. This study serves as a proof-of-concept to evaluate the feasibility of incorporating a compact 4-qubit quantum circuit within a lightweight hybrid model. The proposed model achieves 98.52% accuracy with a standard deviation of 1.99, supporting the potential of combining quantum and classical computing for assistive communication technologies and encouraging further research in quantum biosignal interpretation and human–computer interaction. Full article

This article presents a new approach to the problem of transforming one quantum state into another. It is shown that an $r$-qubit superposition $|\mathit{x}⟩$ can be obtained from another $r$-qubit superposition $|\mathit{y}⟩$, by using only ${(2}^r-1)$ rotations, each presented by one controlled rotation gate. The quantum superpositions with real amplitudes are considered. The traditional two-stage approach $U_y^{-1}{U}_x:|\mathit{x}⟩\to |{0⟩}^{\oplus r}\to |\mathit{y}⟩$ requires twice as many rotations. Here, both transformations to the conventual basis state, $U_x: |\mathit{x}⟩\to |{0⟩}^{\oplus r}$ and $U_y: |\mathit{y}⟩\to |{0⟩}^{\oplus r}$, use ${(2}^r-1)$ rotations each on two binary planes, and many of these rotations require additional sets of CNOTs to be represented as 1- or 2-qubit-controlled gates. The proposed method is based on the concept of the discrete signal-induced heap transform (DsiHT) which is unitary and generated by a vector and a set of angular equations with given parameters. The quantum analog of this transform is described. The main characteristic of the DsiHT is the path of processing the data. It is shown that there exist such fast paths that allow for effective computing of the DsiHT, which leads to the simple quantum circuits for state preparation and transformation. Examples of such paths are given and quantum circuits for preparation and transformation of 2-, 3-, and 4-qubits are described in detail. CNOT gates are not used, but only controlled gates of elementary rotations around the $y$-axis$.$ It is shown that the transformation and, in particular, only rotation gates with control qubits are required for initialization of 2-, 3-, and 4-qubits. The quantum circuits are simple and have a recursive form, which makes them easy to implement for arbitrary $r$-qubit superposition, with $r\ge 2.$ This approach significantly reduces the complexity of quantum state transformations, paving the way for more efficient quantum algorithms and practical implementations on near-term quantum devices. Full article

We consider dynamics relevant to annealing in qubit networks modelled on the architecture of the D-Wave 2000Q quantum processor (known as the Chimera topology). Our results report on the effects of the qubits’ connectivity and variable coupling strengths (based on physical interactions) on the dynamics of network. The networks we examine are up to 32 qubits in size and include coupling lengths varying by almost an order of magnitude. We show that while information transfer within the network can be strongly affected by the different interactions, the system maintains similar clusters of qubits with comparable fidelities even in the presence of some of the physical interactions. This suggests an intrinsic robustness of the Chimera topology to these perturbations, even if it includes such a variety of coupling lengths. Moreover, a similar clustering geometry was observed for other qubit properties in previous analysis of actual data from D-Wave 2000Q. This comparable behaviour suggests that the real quantum annealing chip is subject to little or no unwanted effects due to interactions that scale with the coupling lengths. This could be due to absence of the most damaging type of physical interactions and/or to D-Wave calibration methods tuning the control lines such that the couplings perform as if there is no effect due to their physical length. Our results are also relevant to the use of chaining for the creation of logical qubits. They show that even with very strong interactions between the chain, significant unwanted perturbations may occur due to the inhomogeneous fidelities of the overall dynamics and inhomogeneous dynamics should be expected for any given algorithm. Full article

This paper proposes a novel quantum image transmission framework to address the limitations of existing single-qubit time domain systems, which struggle with noise resilience and scalability. The framework integrates frequency domain processing with multi-qubit (1 to 8 qubits) encoding to enhance robustness against quantum noise. Initially, images are source-coded using JPEG and HEIF formats with rate adjustment to ensure consistent bandwidth usage. The resulting bitstreams are channel-encoded and mapped to multi-qubit quantum states. These states are transformed into the frequency domain via the quantum Fourier transform (QFT) for transmission. At the receiver, the inverse QFT recovers the time domain states, followed by multi-qubit decoding, channel decoding, and source decoding to reconstruct the image. Performance is evaluated using bit error rate (BER), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Results show that increasing the number of qubits enhances image quality and noise robustness, albeit at the cost of increased system complexity. Compared to time domain processing, the frequency domain approach achieves superior performance across all qubit configurations, with the eight-qubit system delivering up to a 4 dB maximum channel SNR gain for both JPEG and HEIF images. Although single-qubit systems benefit less from frequency domain encoding due to limited representational capacity, the overall framework demonstrates strong potential for scalable and noise-robust quantum image transmission in future quantum communication networks. Full article

In this paper, we explore using the Harrow–Hassidim–Lloyd (HHL) algorithm to address scientific and engineering problems through quantum computing, utilizing the NWQSim simulation package on a high-performance computing platform. Focusing on domains such as power-grid management and climate projection, we demonstrate the correlations of the accuracy of quantum phase estimation, along with various properties of coefficient matrices, on the final solution and quantum resource cost in iterative and non-iterative numerical methods such as the Newton–Raphson method and finite difference method, as well as their impacts on quantum error correction costs using the Microsoft Azure Quantum resource estimator. We summarize the exponential resource cost from quantum phase estimation before and after quantum error correction and illustrate a potential way to reduce the demands on physical qubits. This work lays down a preliminary step for future investigations, urging a closer examination of quantum algorithms’ scalability and efficiency in domain applications. Full article

We study in detail the critical points of Bohmian flow, both in the inertial frame of reference (Y-points) and in the frames centered at the moving nodal points of the guiding wavefunction (X-points), and analyze their role in the onset of chaos in a system of two entangled qubits. We find the distances between these critical points and a moving Bohmian particle at varying levels of entanglement, with particular emphasis on the times at which chaos arises. Then, we find why some trajectories are ordered, without any chaos. Finally, we examine numerically how the Lyapunov Characteristic Number ($LCN$) depends on the degree of quantum entanglement. Our results indicate that increasing entanglement reduces the convergence time of the finite-time $LCN$ of the chaotic trajectories toward its final positive value. Full article

In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence. Full article

This follow-up scientific case study builds on prior research to explore the computational challenges of applying quantum algorithms to financial asset management, focusing specifically on solving the graph-cut problem for investment recommendation. Unlike our prior study, which focused on idealized QAOA performance, this work introduces a graph compression pipeline that enables QAOA deployment under real quantum hardware constraints. This study investigates quantum-accelerated spectral graph compression for financial asset recommendations, addressing scalability and regulatory constraints in portfolio management. We propose a hybrid framework combining the Quantum Approximate Optimization Algorithm (QAOA) with spectral graph theory to solve the Max-Cut problem for investor clustering. Our methodology leverages quantum simulators (cuQuantum and Cirq-GPU) to evaluate performance against classical brute-force enumeration, with graph compression techniques enabling deployment on resource-constrained quantum hardware. The results underscore that efficient graph compression is crucial for successful implementation. The framework bridges theoretical quantum advantage with practical financial use cases, though hardware limitations (qubit counts, coherence times) necessitate hybrid quantum-classical implementations. These findings advance the deployment of quantum algorithms in mission-critical financial systems, particularly for high-dimensional investor profiling under regulatory constraints. Full article

In the evolving field of quantum computing, optimizing Quantum Error Correction (QEC) parameters is crucial due to the varying types and amounts of physical noise across quantum computers. Traditional simulators use a forward paradigm to derive logical error rates from inputs like code distance and rounds, but this can lead to resource wastage. Adjusting QEC parameters manually with tools like STIM is often inefficient, especially given the daily fluctuations in quantum error rates. To address this, we introduce MITS, a reverse engineering tool for STIM that automatically determines optimal QEC settings based on a given quantum computer’s noise model and a target logical error rate. This approach minimizes qubit and gate usage by precisely matching the necessary logical error rate with the constraints of qubit numbers and gate fidelity. Our investigations into various heuristics and machine learning models for MITS show that XGBoost and Random Forest regressions, with Pearson correlation coefficients of $0.98$ and $0.96$, respectively, are highly effective in this context. Full article

Dynamic decoupling (DD) can suppress decoherence caused by environmental noise, while in hybrid system it also hinders coherent manipulation between qubits. We realized the universal high-fidelity quantum gate set and the preparation of Bell states using dynamical decoupling gates (DD gates) in a silicon-based phosphorus-doped (Si:P) system, effectively resolving the contradiction between decoherence protection and manipulation of qubits. The simulation results show that the fidelity of the universal quantum gate set are all above 99%, and the fidelity of Bell state preparation is over 96%. This work realized the compatibility between coherent protection and high-fidelity manipulation of quantum states, provided a reliable theoretical support for high-fidelity quantum computing. Full article

Private array equality comparison (PAEC) aims to evaluate whether two arrays are equal while maintaining the confidentiality of their elements. Current private comparison protocols predominantly focus on determining the relationships of secret integers, lacking exploration of array comparisons. To address this issue, we propose a swap test-based quantum protocol for PAEC, which satisfies both functionality and security requirements using the principles of quantum mechanics. This protocol introduces a semi-honest third party (TP) that acts as a medium for generating Bell states as quantum resources and distributes the first and second qubits of these Bell states to the respective participants. They encode their array elements into the received qubits by performing rotation operations. These encoded qubits are sent to TP to derive the comparison results. To verify the feasibility of the proposed protocol, we construct a quantum circuit and conduct simulations on the IBM quantum platform. Security analysis further indicates that our protocol is resistant to various quantum attacks from outsider eavesdroppers and attempts by curious participants. Full article

We present a graphical framework to represent entanglement in three-qubit states. The geometry associated with each entanglement class and type is analyzed, revealing distinct structural features. We explore the connection between this geometric perspective and the tangle, deriving bounds that depend on the entanglement class. Based on these insights, we conjecture a purely geometric expression for both the tangle and Cayley’s hyperdeterminant for non-generic states. As an application, we analyze the energy eigenstates of physical Hamiltonians, identifying the sufficient conditions for genuine tripartite entanglement to be robust under symmetry-breaking perturbations and level repulsion effects. Full article

Electrons trapped above the surface of helium provide a means to study many-body physics free from the randomness that comes from defects in other condensed-matter systems. Localizing an electron in an electrostatic quantum dot makes its energy spectrum discrete, with controlled level spacing. The lowest two states can act as charge qubit states. In this paper, we study how the coupling to the quantum field of capillary waves on helium—known as ripplons—affects electron dynamics. As we show, the coupling can be strong. This bounds the parameter range where electron-based charge qubits can be implemented. The constraint is different from the conventional relaxation time constraint. The electron–ripplon system in a dot is similar to a color center formed by an electron defect coupled to phonons in a solid. In contrast to solids, the coupling in the electron on helium system can be varied from strong to weak. This enables a qualitatively new approach to studying color center physics. We analyze the spectroscopy of the pertinent synthetic color centers in a broad range of the coupling strength. Full article

Sampling with replacement occurs when drawing without removing individuals from finite populations. It is a common distribution technique used in physics, biology, and medicine. It is used in state analysis of qubits, the physics of particle interactions, studies of genetic variation and variability, and analyzing the treatment effects from clinical trial analyses. When applied, sample statistics should be accompanied by confidence intervals. The major difficulty in expressing the confidence intervals in sampling with replacement consists of discreetness regarding the probability distribution. As a result, no mathematical formula can handle an optimum solution. Using a simple algorithm is proposed in order to obtain confidence intervals for sampling with replacement variables (x from m trials with replacement) and their proportion ($x/m$). A question-based discussion is presented. Traditional confidence intervals often require large sample sizes. Confidence intervals, constructed in a deterministic way provided by the proposed algorithm for sampling with replacement, allow constructing intervals without constraints. Full article