Quantum One-Time Pad (QOTP) offers information-theoretic security but its realization on Noisy Intermediate-Scale Quantum (NISQ) devices is challenged by decoherence and hardware imperfections. This work presents a noise-aware QOTP framework that integrates adaptive error mitigation, lightweight 3-qubit repetition coding, and hybrid quantum-classical feedback. The methodology incorporates assumptions such as ideal Pauli gates and i.i.d. noise, and validates feasibility through Qiskit Aer simulations with 1024 shots. Results show that QBER is reduced from$0.21 \pm 0.02$to$0.13 \pm 0.01$under$30\%$depolarizing noise, demonstrating improved robustness without requiring full quantum error correction. While non-Markovian modeling is introduced conceptually, simulations primarily evaluate Markovian noise scenarios. The findings indicate that adaptive, lightweight strategies can enhance QOTP feasibility on current quantum hardware, although scalability to larger systems remains an open challenge.
Quantum One-Time Pad (QOTP) offers information-theoretic security but its realization on Noisy Intermediate-Scale Quantum (NISQ) devices is challenged by decoherence and hardware imperfections. This work presents a noise-aware QOTP framework that integrates adaptive error mitigation, lightweight 3-qubit repetition coding, and hybrid quantum-classical feedback. The methodology incorporates assumptions such as ideal Pauli gates and i.i.d. noise, and validates feasibility through Qiskit Aer simulations with 1024 shots. Results show that QBER is reduced from0.21±0.02to0.13±0.01under30%depolarizing noise, demonstrating improved robustness without requiring full quantum error correction. While non-Markovian modeling is introduced conceptually, simulations primarily evaluate Markovian noise scenarios. The findings indicate that adaptive, lightweight strategies can enhance QOTP feasibility on current quantum hardware, although scalability to larger systems remains an open challenge.
This experimental study introduces a hybrid quantum-classical framework aiming to improve the noise robustness of Shor’s algorithm. By incorporating a novel hybrid logic gate into the modular exponentiation phase, the framework stabilizes qubit interactions and decreases decoherence in a noisy environment simulated by IBM Qiskit. Performance evaluation is accomplished by fidelity and entropy analysis across varied noise levels. Comparative benchmarking with conventional techniques, including Shor’s code, Bacon-Shor code, dynamical decoupling, and zero noise extrapolation, demonstrates that the proposed hybrid gate achieves higher fidelity (0.90) and lower entropy (0.33), while preserving minimal resource overhead. These findings highlight the framework’s effectiveness as a hardware-independent, scalable solution for Noisy Intermediate-Scale Quantum (NISQ) devices. Instead, the study offers an approach toward a scalable, error-resilient model to avoid exaggerating scalability. Despite its promising outcomes, study is presently limited to simulations and a single noise model. Future work will explore implementation on real quantum hardware and extend the framework to incorporate a broader range of noise profiles, contributing to the evolution of fault-tolerant quantum computation.
Quantum Neural Networks (QNNs) represent a promising direction within Quantum Machine Learning (QML), yet their realization on noisy intermediate-scale quantum (NISQ) devices remains constrained by decoherence, gate imperfections, crosstalk, and readout errors. This study provides a systematic evaluation of noise effects and mitigation strategies in hybrid quantum neural networks (HQNNs). Zero-Noise Extrapolation (ZNE), Digital Dynamical Decoupling (DDD), and Layerwise Richardson Extrapolation (LRE) are incorporated into end-to-end QNN training pipelines developed with PennyLane, simulated using Qiskit Aer noise models, and integrated with the Mitiq framework, while Probabilistic Error Cancellation (PEC) is evaluated separately under depolarizing noise due to its computational cost. Experiments conducted on the Iris dataset with five representative noise channels show that the impact of noise and the effect of mitigation are strongly dependent on the noise model and its strength. The model maintains comparatively strong performance under phase-flip and phase-damping noise, while substantial degradation is observed under high depolarizing and amplitude-damping noise. Across the evaluated mitigation methods, the observed benefits remain limited and noise-dependent: ZNE, DDD, and LRE generally follow the same degradation trends as the unmitigated baseline, while PEC shows limited gains only in the low-noise depolarizing regime. These findings highlight the need for context-specific mitigation strategies to improve the robustness of QNNs in practical NISQ settings.
Quantum Secret Sharing (QSS) employs the principles of quantum entanglement to securely distribute confidential information among multiple participants, such that the original secret can only be recovered through collaboration among an authorized subset of users. While QSS offers information-theoretic security independent of computational assumptions, its practical deployment on Noisy Intermediate-Scale Quantum (NISQ) hardware remains challenging. The presence of device imperfections, including gate errors, decoherence, and measurement inaccuracies, adversely affects quantum state fidelity and reduces the reliability of secret reconstruction during protocol execution. This paper presents a noise-resilient multiparty QSS framework integrating a (2,3)- threshold Shamir Secret Sharing scheme over Galois Field GF (8) with nine-qubit Greenberger–Horne–Zeilinger (GHZ) entanglement circuits, fully implemented on the IBM ibm_kingston 156-qubit Heron r2 quantum processor. A three-phase experimental methodology is adopted: noiseless simulation via Qiskit Aer, authentic hardware execution with 1024 measurement shots, and a combined Local Readout Error Mitigation (LRE) and Zero Noise Extrapolation (ZNE) pipeline. Across 24 participants in eight secret-sharing sessions, hardware noise introduced a 6.0% accuracy degradation relative to the ideal baseline, which the LRE+ZNE pipeline substantially recovered. The (2,3)-threshold security invariant was preserved throughout all phases, with singleplayer reconstruction rates held at exactly 0.000, well below the 12.5% random-guess baseline. Security is further reinforced through CRYSTALS-Kyber512 (NIST FIPS 203) post-quantum authentication, SHA-256 share commitments, SHA3-256 fairness verification, and bulletin board consensus, with verified resistance against seven attack categories. Results confirm that reliable hardware-validated QSS on current NISQ devices is achievable through systematic multi-technique error mitigation.
Abstract Quantum computing has recently emerged as a novel paradigm for solving problems otherwise intractable on classical computers, yet the practical progress of quantum computers is severely impacted by noise, decoherence, and imperfect gate operations in Noisy Intermediate-Scale Quantum (NISQ) devices. Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results.This study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time.To achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices.Initial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading.To sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important.
Quantum computing offers exponential advantages for problems in cryptography, optimization, and quantum simulation, yet the practicality of current Noisy Intermediate-Scale Quantum (NISQ) devices is severely constrained by decoherence, gate imperfections, and measurement noise. Conventional quantum error correction (QEC) codes such as Steane and surface codes impose large qubit overheads and require high-fidelity logical operations, making them unsuitable for near-term hardware. To address this gap, this work introduces ML-QEM, an adaptive quantum error mitigation framework that employs machine learning to identify and correct dominant Pauli error patterns in real time using only 3–5 ancillary qubits. By integrating Qiskit Aer noise modeling with a lightweight TensorFlow-based classifier, ML-QEM learns device-specific noise characteristics and applies targeted recovery operations with minimal latency. Evaluations across depolarizing, amplitude damping, and thermal relaxation noise models demonstrate a 28.4% average fidelity improvement, 60–84% lower overhead than Steane and surface codes, and a classical processing delay of only 21.7 ms. ML-QEM thus provides an effective and resource-efficient pathway toward enhancing quantum computation fidelity in the NISQ era.
Quantum Neural Networks (QNNs) offer a compelling approach to quantum machine learning, but their effectiveness is heavily constrained by noise on Noisy Intermediate-Scale Quantum (NISQ) hardware. Prior studies examine global noise effects, yet the sensitivity of individual QNN layers remains underexplored.This paper presents a layer-wise noise sensitivity analysis framework that isolates the impact of depolarizing, amplitude damping, and phase damping noise on the encoding, variational, and measurement layers of QNNs. Using Qiskit Aer, we simulate targeted noise injection and evaluate performance on the Iris dataset. We introduce the Layerwise Sensitivity Drop (LSD) metric to quantify degradation under noise.Results show that the encoding layer is most vulnerable (LSD up to 42.9%), while the measurement layer is comparatively robust. Additionally, entangling gates are found to be more noisesensitive than rotational gates. These insights inform future QNN design strategies, emphasizing layer-specific error mitigation to improve resilience on near-term quantum hardware.