Search Results (72)

Using a global rotation by $\theta$ about the z-axis in the spin sector of the Jordan–Wigner transformation rotates Pauli matrices $\widehat{X}$ and $\widehat{Y}$ in the $x-y$-plane, while it adds a global complex phase to fermionic quantum states that have a fixed number of particles. With the right choice of angles, this relates expectation values of Pauli strings containing products of $\widehat{X}$ and $\widehat{Y}$ to different products, which can be employed to reduce the number of measurements needed when simulating fermionic systems on a quantum computer. Here, we derive this symmetry and show how it can be applied to systems in Physics and Chemistry that involve Hamiltonians with only single-particle (hopping) and two-particle (interaction) terms. We also discuss the consequences of this for finding efficient measurement circuits in variational ground state preparation. Full article

Quantum neural networks leverage quantum computing to address machine learning problems beyond the capabilities of classical computing. In this study, we demonstrate a quantum neural network that learns the nonlinear exclusive OR function on a desktop quantum computer. The exclusive OR task is a nonlinear benchmark that cannot be solved by a single-layer perceptron, making it an excellent test for quantum machine learning. We trained a variational quantum circuit model in a simulation using the PennyLane framework to learn the two-bit exclusive OR mapping. After obtaining the circuit parameters in the simulation, the trained quantum neural network was deployed on a two-qubit Nuclear Magnetic Resonance-based desktop quantum computer operating at room temperature to evaluate the actual hardware performance. The experimental quantum state fidelity reached approximately 98.85%$\left(R_y\right)$ and 99.35%$\left(R_x\right)$, and the overall average purity was 95.16%$\left(R_y\right)$ and 97.43%$\left(R_x\right)$, indicating excellent agreement between the expected and measured results. These positive outcomes underscore the feasibility of quantum machine learning on small-scale quantum hardware, marking a minimal yet physically meaningful benchmark. Full article

Parameterized quantum circuits are central to the development of variational quantum algorithms in the NISQ era. A key feature of these circuits is their ability to generate an expressive set of quantum states, enabling the approximation of solutions to diverse problems. The expressibility of such circuits can be assessed by analyzing the ensemble of states produced when their parameters are randomly sampled, a property closely tied to quantum complexity. In this work, we compare different classes of parameterized quantum circuits with a prototypical family of universal random circuits to investigate how rapidly they approach the asymptotic complexity defined by the Haar measure. We find that parameterized circuits exhibit faster convergence in terms of the number of gates required, as quantified through expressibility and majorization-based complexity measures. Moreover, the topology of qubit connections proves crucial, significantly affecting entanglement generation and, consequently, complexity growth. The majorization criterion emerges as a valuable complementary tool, offering distinct insights into the behavior of random state generation in the considered circuit families. Full article

Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. This systematic literature review synthesises fifteen peer-reviewed studies published between 2015 and 2025, examining the application of quantum and quantum-inspired methods to transport optimisation. The review identifies five key problem domains (vehicle routing, factory scheduling, network design, traffic operations, and energy management) and categorises the quantum techniques used, including quantum annealing, variational circuits, and digital annealers. Although several studies demonstrate performance gains over classical heuristics, most rely on synthetic datasets, lack statistical robustness, and omit critical operational metrics such as energy consumption and queue latency. Four cross-cutting barriers are identified: hardware limitations, data availability, energy inefficiency, and organisational readiness. The review identifies limited real-world deployment, a lack of standardised benchmarks, and scarce cost–benefit evaluations, highlighting key areas where further empirical work is needed. It concludes with a structured research agenda aimed at bridging the gap between laboratory demonstrations and practical implementation, emphasising the need for pilot trials, open datasets, robust experimental protocols, and interdisciplinary collaboration. Full article

We investigate hybrid quantum–classical solvers for nonlinear boundary value problems using Chebyshev spectral collocation. Unlike prior methods such as H–DES, which repeatedly recompile circuits and encode the entire spectral basis on the quantum processor, our framework offloads only the residual minimisation to a quantum backend while retaining classical enforcement of boundary conditions. Two paradigms are considered: (i) gate-based residual minimisation on CUDA-Q using variational circuits to evaluate a Cubic Unconstrained Binary Optimisation (CUBO) cost, which naturally arises from the discretisation, and (ii) a Quadratic Unconstrained Binary Optimisation (QUBO) reformulation, which is required for execution on a quantum annealer, executed via a classical–quantum mapping. We further explore a CUBO extension on CUDA-Q and direct residual-to-energy mapping on annealers. Benchmarks confirm that the classical solver reproduces the analytic solution with spectral accuracy; among quantum-enhanced methods, the annealer-based QUBO yields the closest approximation. The gate-based CUBO solver improves upon a legacy variational baseline but exhibits a small interior bias due to limited circuit depth and precision. These findings underscore the complementary roles of annealers and gate-based devices in hybrid scientific computing and demonstrate a feasible workflow for the NISQ era rather than a speedup over classical methods. Recent progress in quantum algorithms for differential equations signals a rapidly maturing field with significant potential for practical quantum advantage. Full article

The increasing digitization and decentralization of modern energy systems have heightened their vulnerability to sophisticated cyber threats, necessitating advanced, scalable, and privacy-preserving detection frameworks. This paper introduces a novel Federated Quantum Machine Learning (FQML) framework tailored for anomaly detection in multi-agent energy environments. By integrating parameterized quantum circuits (PQCs) at the local agent level with secure federated learning protocols, the framework enhances detection accuracy while preserving data privacy. A trimmed-mean aggregation scheme and differential privacy mechanisms are embedded to defend against Byzantine behaviors and data-poisoning attacks. The problem is formally modeled as a constrained optimization task, accounting for quantum circuit depth, communication latency, and adversarial resilience. Experimental validation on synthetic smart grid datasets demonstrates that FQML achieves high detection accuracy (≥96.3%), maintains robustness under adversarial perturbations, and reduces communication overhead by 28.6% compared to classical federated baselines. These results substantiate the viability of quantum-enhanced federated learning as a practical, hardware-conscious approach to distributed cybersecurity in next-generation energy infrastructures. Full article

Molecules provide the smallest possible circuits in which quantum interference and electron correlation can be engineered to perform logical operations, including the universal NAND gate. We investigate a chemically encoded quantum NAND tree based on alkynyl-extended iso-polyacetylene backbones, where inputs are set by end-group substitution and outputs are read from the presence or absence of transmission nodes. Using quantum many-body transport theory, we show that NAND behavior persists in the presence of dynamic correlations, but that the nodal positions and their chemical shifts depend sensitively on electron–electron interactions. This sensitivity highlights the potential of these systems not only to probe the strength of electronic correlations but also to harness them in shaping logical response. The thermopower is identified as a chemically robust readout of gate logic, providing discrimination margins that greatly exceed typical experimental uncertainties, in an observable governed primarily by the variation of transport rather than its absolute magnitude. Full article

Parameterized quantum circuits play a key role in quantum computing. Measuring the suitability of such a circuit for solving a class of problems is needed. One such promising measure is the expressivity of a circuit, which is defined in two main variants. The variant in focus of this contribution is the so-called dimensional expressivity, which measures the dimension of the submanifold of states produced by the circuit. Understanding this measure needs a lot of background from differential topology, which makes it hard to comprehend. In this article, we provide this background in a vivid as well as pedagogical manner. Especially, it strives towards being self-contained for understanding expressivity, e.g., the required mathematical foundations are provided, and examples are given. Also, the literature makes several statements about expressivity, the proofs of which are omitted or only indicated. In this article, we give proof for key statements from dimensional expressivity, sometimes revealing limits for generalizing them, and sketching how to proceed in practice to determine this measure. Full article

Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions. Full article

Detecting financial fraud is a critical aspect of modern intelligent financial systems. Despite the advances brought by deep learning in predictive accuracy, challenges persist—particularly in capturing complex, high-dimensional nonlinear features. This study introduces a novel hybrid quantum recurrent neural network for fraud detection (HQRNN-FD). The model utilizes variational quantum circuits (VQCs) incorporating angle encoding, data reuploading, and hierarchical entanglement to project transaction features into quantum state spaces, thereby facilitating quantum-enhanced feature extraction. For sequential analysis, the model integrates a recurrent neural network (RNN) with a self-attention mechanism to effectively capture temporal dependencies and uncover latent fraudulent patterns. To mitigate class imbalance, the synthetic minority over-sampling technique (SMOTE) is employed during preprocessing, enhancing both class representation and model generalizability. Experimental evaluations reveal that HQRNN-FD attains an accuracy of 0.972 on publicly available fraud detection datasets, outperforming conventional models by 2.4%. In addition, the framework exhibits robustness against quantum noise and improved predictive performance with increasing qubit numbers, validating its efficacy and scalability for imbalanced financial classification tasks. 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

Silicon-based microcavity quantum dot lasers are attractive candidates for on-chip light sources in photonic integrated circuits due to their small size, low power consumption, and compatibility with silicon photonic platforms. However, integrating components like quantum dot lasers and photodetectors on a single chip remains challenging due to material compatibility issues and mode field mismatch problems. In this work, we have demonstrated monolithic integration of an InAs quantum dot microdisk light emitter, waveguide, and photodetector on a silicon platform using a shared epitaxial structure. The photodetector successfully monitored variations in light emitter output power, experimentally proving the feasibility of this integrated scheme. This work represents a key step toward multifunctional integrated photonic systems. Future efforts will focus on enhancing the light emitter output power, improving waveguide efficiency, and scaling up the integration density for advanced applications in optical communication. Full article

This paper presents an engineering design framework for integrating Variational Quantum Circuits (VQCs) into industrial control systems via real-time quantum emulation on embedded hardware. In this work, we present a novel framework for fully embedded real-time quantum machine learning (QML), in which a four-qubit, four-layer VQC is both emulated and trained in situ on an FPGA-based embedded platform (dSPACE MicroLabBox 1202). The system achieves deterministic microsecond-scale response at a closed-loop frequency of 100 kHz, enabling its application in latency-critical control tasks. We demonstrate the feasibility of online VQC training within this architecture by approximating nonlinear functions in real time, thereby validating the potential of embedded QML for advanced signal processing and control applications. This approach provides a scalable and practical path toward real-time Quantum Reinforcement Learning (QRL) and other quantum-enhanced embedded controllers. The results validate the feasibility of real-time quantum emulation and establish a structured engineering design methodology for implementing trainable quantum machine learning (QML) models on embedded platforms, thereby enabling the development of deployable quantum-enhanced controllers. Full article

This study proposes an index-based quantum neural network (QNN) model, built upon a variational quantum circuit (VQC), as a surrogate framework for the static analysis of truss structures. Unlike coordinate-based models, the proposed QNN uses discrete member and node indices as inputs, and it adopts a separate-domain strategy that partitions the structure for parallel training. This architecture reflects the way nature organizes and optimizes complex systems, thereby enhancing both flexibility and scalability. Independent quantum circuits are assigned to each separate domain, and a mechanics-informed loss function based on the force method is formulated within a Lagrangian dual framework to embed physical constraints directly into the training process. As a result, the model achieves high prediction accuracy and fast convergence, even under complex structural conditions with relatively few parameters. Numerical experiments on 2D and 3D truss structures show that the QNN reduces the number of parameters by up to 64% compared to conventional neural networks, while achieving higher accuracy. Even within the same QNN architecture, the separate-domain approach outperforms the single-domain model with a 6.25% reduction in parameters. The proposed index-based QNN model has demonstrated practical applicability for structural analysis and shows strong potential as a quantum-based numerical analysis tool for future applications in building structure optimization and broader engineering domains. Full article

As photonic integrated circuits (PICs) gain prominence in quantum communication and quantum computation, the development of efficient and stable cryogenic packaging technologies becomes paramount. This paper presents a robust and scalable cryogenic packaging method for thin-film lithium niobate (TFLN) photonic chips. The packaged fiber-to-chip interface shows a coupling efficiency of 15.7% ± 0.3%, with minimal variation of ±0.5% as the temperature cools down from 295 K to 1.5 K. Furthermore, the packaged chip exhibits outstanding stability over multiple thermal cycling, highlighting its potential for practical applications in cryogenic environments. Full article