Search Results (68)

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

Understanding the capabilities of quantum computer devices and computing the required resources to solve realistic tasks remain critical challenges associated with achieving useful quantum computational advantage. We present a study aimed at reducing the quantum resource overhead in quantum chemistry simulations using the variational quantum eigensolver (VQE). Our approach achieves up to a two-orders-of magnitude reduction in the required number of two-qubit operations for variational problem-inspired ansatzes. We propose and analyze optimization strategies that combine various methods, including molecular point-group symmetries, compact excitation circuits, different types of excitation sets, and qubit tapering. To validate the compatibility and accuracy of these strategies, we first test them on small molecules such as LiH and BeH₂, then apply the most efficient ones to restricted active-space simulations of methylamine. We complete our analysis by computing the resources required for full-valence, active-space simulations of methylamine (26 qubits) and formic acid (28 qubits) molecules. Our best-performing optimization strategy reduces the two-qubit gate count for methylamine from approximately 600,000 to about 12,000 and yields a similar order-of-magnitude improvement for formic acid. This resource analysis represents a valuable step towards the practical use of quantum computers and the development of better methods for optimizing computing resources. Full article

The ability to capture long-distance dependencies is critical for improving the prediction accuracy of spatiotemporal prediction models. Traditional ConvLSTM models face inherent limitations in this regard, along with the challenge of information decay, which negatively impacts prediction performance. To address these issues, this paper proposes a QSA-QConvLSTM model, which integrates quantum convolution circuits and quantum self-attention mechanisms. The quantum self-attention mechanism maps query, key, and value vectors using variational quantum circuits, effectively enhancing the ability to model long-distance dependencies in spatiotemporal data. Additionally, the use of quantum convolution circuits improves the extraction of spatial features. Experiments on the Moving MNIST dataset demonstrate the superiority of the QSA-QConvLSTM model over existing models, including ConvLSTM, TrajGRU, PredRNN, and PredRNN v2, with MSE and SSIM scores of 44.3 and 0.906, respectively. Ablation studies further verify the effectiveness and necessity of the quantum convolution circuits and quantum self-attention modules, providing an efficient and accurate approach to quantized modeling for spatiotemporal prediction tasks. Full article

The parity problem, a generalization of the XOR problem to higher-dimensional inputs, is a challenging benchmark for evaluating learning algorithms, due to its increased complexity as the number of dimensions of the feature space grows. In this work, a single-qubit classifier is developed, which can efficiently learn the parity function from input data. Despite the qubit model’s simplicity, the solution landscape created in the context of the parity problem offers an attractive test bed for exploring optimization methods for quantum classifiers. We propose a new optimization method called Ensemble Stochastic Gradient Descent (ESGD), with which density matrices describing batches of quantum states are incorporated into the loss function. We demonstrate that ESGD outperforms both Gradient Descent and Stochastic Gradient Descent given the aforementioned problem. Additionally, we show that applying ESGD with only one measurement per data input does not lead to any performance degradation. Our findings not only highlight the potential of a single-qubit model, but also offer valuable insights into the use of density matrices for optimization. Further to this, we complement the outcome with interesting results arising by the employment of a Doubly Stochastic Gradient Descent for training quantum variational circuits. Full article

We propose a simple and innovative configuration consisting of a quantum dot and micro-optical resonator that emits single photons with good directionality in a plane parallel to the substrate. In this device, a single quantum dot is placed as a light source between the slits of a triangular split-ring micro-optical resonator (SRR) supported in an optical polymer film with an air-bridge structure. Although most of the previous single photon emitters in solid-state devices emitted photons upward from the substrate, operation simulations confirmed that this configuration realizes lateral light emission in narrow regions above, below, left, and right in the optical polymer film, despite the absence of a light confinement structure such as an optical waveguide. This device can be fabricated using silica-coated colloidal quantum dots, focused ion beam (FIB) lithography, and wet etching using an oxide layer on a silicon substrate as a sacrificial layer. The device has a large tolerance to the variation in the position of the SRR in the optical polymer film and the height of the air-bridge. We confirmed that Pt-SRRs can be formed on the optical polymer film using FIB lithography. This simple lateral photon emitter is suitable for coupling with optical fibers and for fabricating planar optical quantum solid-state circuits, and is useful for the development of quantum information processing technology. Full article