It has been applied in areas such as breaking cryptographic systems [1], searching databases [2], and quantum simulation [3, 4], in which it gives a quantum speedup over the best known classical algorithms. With the fast development of quantum hardware, recent results [5-7] have shown quantum advantages in specific tasks.

Thisresultleadstoageneral standpoint that deep quantum circuits would not be feasible for practical tasks.

However, also in classical ML, kernel methods allow us to implicitly work with high-or infinite dimensional function spaces [25, 26].

Security of quantum key distribution (QKD) relies on certifying that observed correlations arise from genuine quantum entanglement rather than eavesdropper manipulation. Theoretical security proofs assume idealized conditions, practical certification must contend with adaptive adversaries who optimize their attack strategies against detection systems. Established fundamental adversarial limits for quantum certification using Eve GAN, a generative adversarial network trained to produce classical correlations indistinguishable from quantum. Our central finding: when Eve interpolates her classical correlations with quantum data at mixing parameter, all tested detection methods achieve ROC AUC = 0.50, equivalent to random guessing. This means an eavesdropper needs only 5% classical admixture to completely evade detection. Critically, we discover that same distribution calibration a common practice in prior certification studies inflates detection performance by 44 percentage points compared to proper cross distribution evaluation, revealing a systematic flaw that may have led to overestimated security claims. Analysis of Popescu Rohrlich (PR Box) regime identifies a sharp phase transition at CHSH S = 2.05: below this value, no statistical method distinguishes classical from quantum correlations; above it, detection probability increases monotonically. Hardware validation on IBM Quantum demonstrates that Eve-GAN achieves CHSH = 2.736, remarkably exceeding real quantum hardware performance (CHSH = 2.691), illustrating that classical adversaries can outperform noisy quantum systems on standard certification metrics. These results have immediate implications for QKD security: adversaries maintaining 95% quantum fidelity evade all tested detection methods. We provide corrected methodology using cross-distribution calibration and recommend mandatory adversarial testing for quantum security claims.

We propose a novel QTGNN framework for detecting fraudulent transactions in large-scale financial networks. By integrating quantum embedding, variational graph convolutions, and topological data analysis, QTGNN captures complex transaction dynamics and structural anomalies indicative of fraud. The methodology includes quantum data embedding with entanglement enhancement, variational quantum graph convolutions with non-linear dynamics, extraction of higher-order topological invariants, hybrid quantum-classical anomaly learning with adaptive optimization, and interpretable decision-making via topological attribution. Rigorous convergence guarantees ensure stable training on noisy intermediate-scale quantum (NISQ) devices, while stability of topological signatures provides robust fraud detection. Optimized for NISQ hardware with circuit simplifications and graph sampling, the framework scales to large transaction networks. Simulations on financial datasets, such as PaySim and Elliptic, benchmark QTGNN against classical and quantum baselines, using metrics like ROC-AUC, precision, and false positive rate. An ablation study evaluates the contributions of quantum embeddings, topological features, non-linear channels, and hybrid learning. QTGNN offers a theoretically sound, interpretable, and practical solution for financial fraud detection, bridging quantum machine learning, graph theory, and topological analysis.

The integration of sensing and communication functionalities within a common system is one of the main innovation drivers for next-generation networks. In this paper, we introduce a quantum integrated sensing and communication (QISAC) protocol that leverages entanglement in quantum carriers of information to enable both superdense coding and quantum sensing. The proposed approach adaptively optimizes encoding and quantum measurement via variational circuit learning, while employing classical machine learning-based decoders and estimators to process the measurement outcomes. Numerical results for qudit systems demonstrate that the proposed QISAC protocol can achieve a flexible trade-off between classical communication rate and accuracy of parameter estimation.

The threat landscape is being shaped by two seismic forces. To future-proof their organizations, security leaders must take a proactive stance with a zero trust approach. AI and quantum technologies are dramatically reconfiguring how cybersecurity functions, redefining the speed and scale with which digital defenders and their adversaries can operate. The weaponization of AI tools for cyberattacks is already proving a worthy opponent to current defenses. This includes using generative AI to create social engineering attacks at scale, churning out tens of thousands of tailored phishing emails in seconds, or accessing widely available voice cloning software capable of bypassing security defenses for as little as a few dollars. And now, agentic AI raises the stakes by introducing autonomous systems that can reason, act, and adapt like human adversaries.

Early identification of abnormal physiological patterns is essential for the timely detection of cardiac disease . This work introduces a hybrid quantum - classical convolutional neural network (QCNN) designed to classify S3 and murmur abnormalities in heart sound signals. The approach transforms one - dimensional phonocardiogram (PCG) signals into compact two - dimensional images through a combination of wavelet feature extraction and adaptive threshold compression methods . We compress the cardiac sound patterns into an 8 - pixel image so that only 8 qubits are needed for the quantum stage. Preliminary results on the HLS - CMDS dataset demonstrate 93.3 3 % classification accuracy on the test set, and 97.14% on the train set, suggesting that quantum models can effi-cientl y capture temporal - spectral correlations in biomedical signals. To our knowledge, this is the first application of a QCNN algorithm for bio acoustic signal processing . The proposed method represents an early step toward quantum - enhanced diagnostic systems f or resource - constrained healthcare environments.

Large Language Models (LLMs) like ChatGPT and LLaMA drive rapid progress in generative AI, yet their huge parameter scales create severe computational and environmental burdens. High training costs, energy use, and limited device deployment hinder accessibility. Existing compression - pruning, distillation, low-rank, and quantization - reduces size but ignores complex inter-layer correlations. We propose KARIPAP, a quantum-inspired tensor network compression using Infinite Projected Entangled Pair States (iPEPS) and Tensor Renormalization Group (TRG) contraction. Unlike 1D Matrix Product States, iPEPS captures multi-directional entanglement in attention and deep transformer layers. TRG ensures polynomial-time contraction, making tensorization feasible while preserving key correlation geometry. Experiments on LLaMA-2 7B show up to 93% memory and 70% parameter reduction, with 50% faster training, 25% faster inference, and only 2-3% accuracy loss. Layer-wise entanglement profiling reveals redundancy in deeper layers, confirming their suitability for tensor factorization. KARIPAP demonstrates that modern LLMs occupy low-dimensional entanglement manifolds, enabling scalable, energy-efficient, and quantum-aware AI architectures.