A Survey on Quantum-Safe Cryptographic Mechanisms: Building Blocks and Applications
本文探讨了量子安全加密机制，针对可能的量子计算威胁，分析了现有应用迁移至量子安全方案的方法及面临的挑战。
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Synthetic data in cryptocurrencies using generative models
This study addresses the challenge posed by limited access to real-world financial data due to privacy constraints and access barriers, which hinders research in the cryptocurrency domain. To overcome this, the authors propose a synthetic data generation method based on conditional generative adversarial networks (CGANs), employing an LSTM-based generator and an MLP-based discriminator. The approach effectively preserves key market trends and dynamic statistical properties while substantially reducing computational overhead. Experimental results demonstrate that the generated data successfully replicates critical temporal patterns across multiple crypto-assets and outperforms existing sophisticated generative models in downstream tasks such as market behavior analysis and anomaly detection. This work thus offers an efficient and viable alternative for financial modeling in privacy-sensitive scenarios.
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Digital Signal Processing from Classical Coherent Systems to Continuous-Variable QKD: A Review of Cross-Domain Techniques, Applications, and Challenges
Continuous-variable quantum key distribution (CV-QKD) faces critical bottlenecks including challenging phase synchronization, rapid polarization drift, and insufficient excess noise suppression. Method: This work systematically introduces digital signal processing (DSP) techniques from classical coherent optical communications into CV-QKD for the first time. Guided by the APISSER methodology, we synthesize insights from 220 publications (2021–2025) to construct a cross-domain technology mapping framework. We innovatively adapt Kalman filtering, carrier recovery, and adaptive equalization; further, we explore the quantum-domain applicability of emerging DSP paradigms—including neural equalization, probabilistic shaping, and joint retiming-equalization filtering. Contribution/Results: Experiments and analysis demonstrate substantial improvements in phase and polarization tracking accuracy and robustness. The approach explicitly defines the technical boundaries for real-time compensation and secure co-transmission under ultra-low signal-to-noise ratios, providing both theoretical foundations and practical engineering pathways toward scalable, interference-resilient CV-QKD systems.
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Exploring Quantum Machine Learning for Weather Forecasting
Weather forecasting faces persistent challenges in accuracy and robustness due to the chaotic and dynamically nonstationary nature of atmospheric systems. To address this, we propose a quantum neural network (QNN)-based approach for meteorological time-series forecasting—specifically for short- to medium-term wind speed and temperature prediction. Our method employs parameterized quantum circuits to instantiate the QNN, trained end-to-end on the NASA POWER real-world meteorological dataset and validated via classical simulation. Experimental results demonstrate that the QNN achieves a 12.7% reduction in mean absolute error (MAE) over classical RNNs for wind speed prediction, accelerates convergence by 3.2× for temperature forecasting, and exhibits superior resilience and stability against abrupt data perturbations. This work establishes a reproducible paradigm and empirical foundation for deploying quantum machine learning in Earth system science.
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A Survey on Quantum-Safe Cryptographic Mechanisms: Building Blocks and Applications
本文探讨了量子安全加密机制，针对可能的量子计算威胁，分析了现有应用迁移至量子安全方案的方法及面临的挑战。
0 citationsRead paper
Synthetic data in cryptocurrencies using generative models
This study addresses the challenge posed by limited access to real-world financial data due to privacy constraints and access barriers, which hinders research in the cryptocurrency domain. To overcome this, the authors propose a synthetic data generation method based on conditional generative adversarial networks (CGANs), employing an LSTM-based generator and an MLP-based discriminator. The approach effectively preserves key market trends and dynamic statistical properties while substantially reducing computational overhead. Experimental results demonstrate that the generated data successfully replicates critical temporal patterns across multiple crypto-assets and outperforms existing sophisticated generative models in downstream tasks such as market behavior analysis and anomaly detection. This work thus offers an efficient and viable alternative for financial modeling in privacy-sensitive scenarios.
0 citationsRead paper
Digital Signal Processing from Classical Coherent Systems to Continuous-Variable QKD: A Review of Cross-Domain Techniques, Applications, and Challenges
Continuous-variable quantum key distribution (CV-QKD) faces critical bottlenecks including challenging phase synchronization, rapid polarization drift, and insufficient excess noise suppression. Method: This work systematically introduces digital signal processing (DSP) techniques from classical coherent optical communications into CV-QKD for the first time. Guided by the APISSER methodology, we synthesize insights from 220 publications (2021–2025) to construct a cross-domain technology mapping framework. We innovatively adapt Kalman filtering, carrier recovery, and adaptive equalization; further, we explore the quantum-domain applicability of emerging DSP paradigms—including neural equalization, probabilistic shaping, and joint retiming-equalization filtering. Contribution/Results: Experiments and analysis demonstrate substantial improvements in phase and polarization tracking accuracy and robustness. The approach explicitly defines the technical boundaries for real-time compensation and secure co-transmission under ultra-low signal-to-noise ratios, providing both theoretical foundations and practical engineering pathways toward scalable, interference-resilient CV-QKD systems.
0 citationsRead paper
Exploring Quantum Machine Learning for Weather Forecasting
Weather forecasting faces persistent challenges in accuracy and robustness due to the chaotic and dynamically nonstationary nature of atmospheric systems. To address this, we propose a quantum neural network (QNN)-based approach for meteorological time-series forecasting—specifically for short- to medium-term wind speed and temperature prediction. Our method employs parameterized quantum circuits to instantiate the QNN, trained end-to-end on the NASA POWER real-world meteorological dataset and validated via classical simulation. Experimental results demonstrate that the QNN achieves a 12.7% reduction in mean absolute error (MAE) over classical RNNs for wind speed prediction, accelerates convergence by 3.2× for temperature forecasting, and exhibits superior resilience and stability against abrupt data perturbations. This work establishes a reproducible paradigm and empirical foundation for deploying quantum machine learning in Earth system science.
0 citationsRead paper