Abstract Quantum discord has emerged as a measure of nonclassical correlations beyond entanglement, yet its operational relevance under realistic noise remains uncertain. We present a dual validation framework that combines extensive simulation sweeps with experimental runs on the 156-qubit \texttt{ibm\_fez} backend. By overlaying depolarizing and amplitude-damping noise models with hardware accuracy curves, we show that discord-based features remain predictive in the presence of substantial device noise. Hardware accuracy points are reported with 95\% binomial confidence intervals to ensure statistical rigor and reproducibility. The close alignment between hardware results and depolarizing simulations confirms that the theoretical framework captures operational realities of near-term quantum processors. These findings establish discord as a robust diagnostic resource for noisy intermediate-scale quantum (NISQ) devices, bridging theory and experiment. Discord features remain predictive under both simulated and real quantum noise, positioning them as a cornerstone for hardware-aware quantum machine learning.
Abstract Quantum discord has emerged as a measure of nonclassical correlations beyond entanglement, yet its operational relevance under realistic noise remains uncertain. We present a dual validation framework that combines extensive simulation sweeps with experimental runs on the 156-qubit \texttt{ibm_fez} backend. By overlaying depolarizing and amplitude-damping noise models with hardware accuracy curves, we show that discord-based features remain predictive in the presence of substantial device noise. Hardware accuracy points are reported with 95% binomial confidence intervals to ensure statistical rigor and reproducibility. The close alignment between hardware results and depolarizing simulations confirms that the theoretical framework captures operational realities of near-term quantum processors. These findings establish discord as a robust diagnostic resource for noisy intermediate-scale quantum (NISQ) devices, bridging theory and experiment. Discord features remain predictive under both simulated and real quantum noise, positioning them as a cornerstone for hardware-aware quantum machine learning.
Abstract Quantum discord has emerged as a robust diagnostic of nonclassical correlations beyond entanglement, particularly relevant for noisy intermediate-scale quantum (NISQ) devices. We present an experimental study of measurement-induced coherence-transfer (MICT) discord proxies implemented on IBM Quantum hardware and benchmarked against simulator results. Discord and coherence landscapes, collapse maps, fidelity maps, and histograms demonstrate that collapse domains remain identifiable despite gate errors and readout noise. Fidelity difference maps quantify deviations between hardware and simulator, while summary metrics establish reproducible benchmarks for discord/coherence suppression and resilience. These findings indicate that discord-related signatures persist as global, topology-informed features in the presence of realistic noise, positioning discord and measurement-induced coherence as practical diagnostics for NISQ benchmarking and quantum machine-learning pipelines.
Abstract Quantum correlations extend beyond entanglement and play a central role in quantum information. Quantum discord captures nonclassical correlations that can persist in separable states. We present the first systematic geometric and operational analysis of MICT-discord in two-qubit systems, integrating numerical validation, scaling laws, noise robustness, and resource geometry. We validate MICT-discord against analytic benchmarks, uncover distinct scaling for entangled versus separable classes, and identify a crossover regime where separable states exceed entangled ones in discord, challenging monotonic assumptions. Under depolarizing and amplitude-damping channels, MICT-discord shows channel-dependent resilience, highlighting potential utility for NISQ devices. In contrast, maximally entangled mixed states (MEMS) expose limitations where MICT-discord fails to detect correlations at low purity. Geometric resource analysis delineates the convex hull boundaries of separable states and reveals the clustering of entangled states in high-discordance regions, providing operational thresholds. Overall, MICT-discord emerges as a structured, coherence-sensitive measure with demonstrated strengths in scaling and noise scenarios, alongside clear limitations in mixed-state regimes. Our results establish a reproducible, geometry-informed framework for discord analysis and suggest practical pathways for resource classification and benchmarking in quantum.
Abstract Quantum correlations extend beyond entanglement and play a central role in quantum information. Quantum discord captures nonclassical correlations that can persist in separable states. We present the first systematic geometric and operational analysis of MICT-discord in two-qubit systems, integrating numerical validation, scaling laws, noise robustness, and resource geometry. We validate MICT-discord against analytic benchmarks, uncover distinct scaling for entangled versus separable classes, and identify a crossover regime where separable states exceed entangled ones in discord, challenging monotonic assumptions. Under depolarizing and amplitude-damping channels, MICT-discord shows channel-dependent resilience, highlighting potential utility for NISQ devices. In contrast, maximally entangled mixed states (MEMS) expose limitations where MICT-discord fails to detect correlations at low purity. Geometric resource analysis delineates the convex hull boundaries of separable states and reveals the clustering of entangled states in high-discordance regions, providing operational thresholds. Overall, MICT-discord emerges as a structured, coherence-sensitive measure with demonstrated strengths in scaling and noise scenarios, alongside clear limitations in mixed-state regimes. Our results establish a reproducible, geometry-informed framework for discord analysis and suggest practical pathways for resource classification and benchmarking in quantum.
Abstract Quantum discord has emerged as a resource that extends beyond entanglement, yet its operational role remains underexplored. In this work, we introduce the Measurement-Induced Coherence Transfer (MICT) framework as both a theoretical and experimental diagnostic for coherence transfer. The MICT-Discord analysis establishes thresholds, scaling behavior, and noise resilience, mapping the geometry of discord across entanglement and measurement parameters. Complementing this, the MICT-Benchmark validates these predictions on IBM Quantum hardware through teleportation, superdense coding, and QKD protocols. The comparative results between the simulator, the ideal state vector,and the hardware output reveal discord as a sensitive witness of backend stability and coherence degradation. Together, MICT-Discord and MICT-Benchmark provide a reproducible suite for evaluating quantum platforms, positioning Discord as a practical diagnostic tool. Future directions include multipartite discord (GHZ/W states), error correction primitives, dynamic circuits with feed-forward, and cross-architecture reproducibility.
Quantum machine learning (QML) has generated significant excitement as a potential game-changer in artificial intelligence. At the heart of any QML model lies a fundamental challenge: how do we translate classical data into the language of quantum mechanics? This encoding process is crucial because the way we represent information in a quantum system can make or break model performance. While researchers have proposed several encoding strategies over the years, most studies focus on idealized conditions, leaving a significant gap in our understanding of how these methods actually behave under the noisy, imperfect conditions of real quantum hardware. In this work, we address this gap through a systematic, simulation-based comparison of three widely used encoding strategies—Angle Encoding, Amplitude Encoding, and the entangling ZZFeatureMap—within a variational quantum classification framework. We evaluate these approaches on standard benchmark datasets under both noiseless and noisy conditions that mimic real quantum devices. To ensure our findings are statistically reliable, each noisy experiment is repeated across 10 independent runs, with results reported using mean accuracy, standard deviation, and 95% confidence intervals. Our results reveal a nuanced but sobering story. While the entangling ZZFeatureMap outperforms simpler methods in ideal noise-free settings, these advantages largely vanish under realistic depolarizing noise. When statistically averaged, all encoding strategies converge to near-random classification accuracy under moderate noise levels. These findings highlight the fragility of quantum advantages on today's noisy intermediate-scale quantum (NISQ) hardware and emphasize the critical need for noise-aware, statistically grounded evaluation protocols when assessing the practical viability of QML models.
Quantum kernels hold significant promise for achieving computational advantages in quantum machine learning (QML), yet their effectiveness critically depends on the design of expressive and hardware-compatible feature maps, a challenge that is particularly pronounced in Noisy Intermediate-Scale Quantum (NISQ) devices with limited qubits, gate errors, and restricted connectivity. In this work, we propose a hardware-aware methodology for automated quantum kernel design that integrates quantum device characteristics with learning-based evaluation. Specifically, candidate quantum circuits explored within the hardware-aware circuit space are represented as directed acyclic graphs (DAGs) encoding hardware-specific information such as gate operations, qubit interactions, and noise properties, while a dual graph neural network (GNN) predictor is employed to estimate key surrogate metrics, including probability of successful trials (PST) and kernel-target alignment (KTA), enabling efficient and accurate assessment of circuit fidelity and kernel performance to facilitate the identification of task-specific quantum kernels. Furthermore, feature selection is incorporated to reduce input dimensionality and ensure compatibility with near-term devices. Extensive experiments on multiple benchmark datasets, including Credit Card (CC), MNIST-5, and FMNIST-4, demonstrate that our method consistently outperforms existing baselines in classification accuracy, effectively balancing hardware constraints and model expressivity under realistic noise conditions. These results highlight the potential of combining hardware-aware design with deep learning techniques to advance practical quantum kernel methods under hardware-calibrated noisy simulation settings.