Machine Learning Techniques for Enhancing Quantum Key Distribution

• URL: http://arxiv.org/abs/2603.07384v1
• Date: Sat, 07 Mar 2026 23:51:47 GMT
• Title: Machine Learning Techniques for Enhancing Quantum Key Distribution
• Abstract summary: Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics.<n>Practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections.<n>Recent Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.

Related papers

• Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning [2.175752761257231]
  Quantum machine learning (QML) provides a promising framework for leveraging quantum-mechanical effects in learning tasks.<n>Small perturbations applied to classical inputs can propagate through the quantum encoding stage and distort the resulting quantum state.<n>We propose a defense mechanism that replaces the conventional quantum encoding stage of a QML model with passive steering-based controlled state preparation.
  arXiv  Detail & Related papers  (2026-04-30T16:15:40Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost [53.25965863436039]
  Quantization-aware training (QAT) provides a more principled solution, but its reliance on backpropagation incurs prohibitive memory costs.<n>We propose ZeroQAT, a zeroth-order optimization-based QAT framework that supports both weight and activation quantization.<n>Experiments show that ZeroQAT consistently outperforms representative PTQ and QAT baselines while requiring significantly less memory.
  arXiv  Detail & Related papers  (2025-08-21T01:18:27Z)
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
  We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• Development and Justification of a Physical Layer Model Based on Monitoring Data for Quantum Key Distribution [0.0]
  Quantum Key Distribution (QKD) is a promising technique for ensuring long-term security in communication systems.<n>This thesis addresses the practical imperfections of QKD systems, such as low and fluctuating Secret Key Rates (SKR) and unstable performance.
  arXiv  Detail & Related papers  (2024-12-10T17:25:20Z)
• Towards Scalable Quantum Key Distribution: A Machine Learning-Based Cascade Protocol Approach [2.363573186878154]
  Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. We propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol.
  arXiv  Detail & Related papers  (2024-09-12T13:40:08Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• Challenges for Reinforcement Learning in Quantum Circuit Design [8.894627352356302]
  Hybrid quantum machine learning (QML) comprises both the application of QC to improve machine learning (ML) and ML to improve QC architectures. We propose qcd-gym, a concrete framework formalized as a Markov decision process, to enable learning policies capable of controlling a universal set of continuously parameterized quantum gates.
  arXiv  Detail & Related papers  (2023-12-18T16:41:30Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• Robust and efficient verification of graph states in blind measurement-based quantum computation [52.70359447203418]
  Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. It is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. Here, we propose a robust and efficient protocol for verifying arbitrary graph states with any prime local dimension.
  arXiv  Detail & Related papers  (2023-05-18T06:24:45Z)
• Potential and limitations of quantum extreme learning machines [55.41644538483948]
  We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
  arXiv  Detail & Related papers  (2022-10-03T09:32:28Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• A model for optimizing quantum key distribution with continuous-wave pumped entangled-photon sources [3.0178691659863452]
  Quantum Key Distribution (QKD) allows unconditionally secure communication based on the laws of quantum mechanics. We analyze the underlying mechanisms for QKD with temporally uniform pair-creation probabilities. We develop a model to calculate optimal trade-offs for maximal secure key rates.
  arXiv  Detail & Related papers  (2021-03-26T17:55:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.