Tootfinder

Special-Unitary Parameterization for Trainable Variational Quantum Circuits
Kuan-Cheng Chen, Huan-Hsin Tseng, Samuel Yen-Chi Chen, Chen-Yu Liu, Kin K. Leung
https://arxiv.org/abs/2507.05535

Special-Unitary Parameterization for Trainable Variational Quantum Circuits
We propose SUN-VQC, a variational-circuit architecture whose elementary layers are single exponentials of a symmetry-restricted Lie subgroup, $\mathrm{SU}(2^{k}) \subset \mathrm{SU}(2^{n})$ with $k \ll n$. Confining the evolution to this compact subspace reduces the dynamical Lie-algebra dimension from $\mathcal{O}(4^{n})$ to $\mathcal{O}(4^{k})$, ensuring only polynomial suppression of gradient variance and circumventing barren plateaus that plague hardware-efficient ansätze. Exact, hardware-…

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Yen Jui Chang, Wei-Ting Wang, Yun-Yuan Wang, Chen-Yu Liu, Kuan-Cheng Chen, Ching-Ray Chang
https://arxiv.org/abs/2507.03963

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Financial markets are noisy yet contain a latent graph-theoretic structure that can be exploited for superior risk-adjusted returns. We propose a quantum stochastic walk (QSW) optimizer that embeds assets in a weighted graph: nodes represent securities while edges encode the return-covariance kernel. Portfolio weights are derived from the walk's stationary distribution. Three empirical studies support the approach. (i) For the top 100 S\&P 500 constituents over 2016-2024, six scenario portfolio…

Benchmarking Quantum and Classical Sequential Models for Urban Telecommunication Forecasting
Chi-Sheng Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai
https://arxiv.org/abs/2508.04488

Benchmarking Quantum and Classical Sequential Models for Urban Telecommunication Forecasting
In this study, we evaluate the performance of classical and quantum-inspired sequential models in forecasting univariate time series of incoming SMS activity (SMS-in) using the Milan Telecommunication Activity Dataset. Due to data completeness limitations, we focus exclusively on the SMS-in signal for each spatial grid cell. We compare five models, LSTM (baseline), Quantum LSTM (QLSTM), Quantum Adaptive Self-Attention (QASA), Quantum Receptance Weighted Key-Value (QRWKV), and Quantum Fast Weigh…

1/ f noise and two-level systems in MBE-grown Al thin films
Shouray Kumar Sahu, Yen-Hsun Glen Lin, Kuan-Hui Lai, Chao-Kai Cheng, Chun-Wei Wu, Elica Anne Heredia, Ray-Tai Wang, Yen-Hsiang Lin, Juainai Kwo, Minghwei Hong, Juhn-Jong Lin, Sheng-Shiuan Yeh
https://arxiv.org/abs/2507.01850

1/ f noise and two-level systems in MBE-grown Al thin films
Aluminum thin films are essential to the functionalities of electronic and quantum devices, where two-level systems (TLS) can degrade device performance. MBE-grown Al films may appeal to these applications due to their low TLS densities. We studied the energy distributions of TLS densities, g(E), in 10-nm-thick MBE-grown and electron-beam evaporated Al films through 1/f noise measurements between 80 and 360 K. At 300 K, the noise magnitudes in MBE-grown films are about three times lower than in…

Quantum Machine Learning for UAV Swarm Intrusion Detection
Kuan-Cheng Chen, Samuel Yen-Chi Chen, Tai-Yue Li, Chen-Yu Liu, Kin K. Leung
https://arxiv.org/abs/2509.01812 https://

Quantum Machine Learning for UAV Swarm Intrusion Detection
Intrusion detection in unmanned-aerial-vehicle (UAV) swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation corpus that covers five attack types, we benchmark three quantum-machine-learning (QML) approaches - quantum kernels, variational quantum neural networks (QNNs), and hybrid quantum-trained neural networks (QT-NNs) - against strong classical baselines. All models consume an 8-feature flow representation and are evaluat…

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
Chin-Yang Lin, Cheng Sun, Fu-En Yang, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu
https://arxiv.org/abs/2508.14041

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gau…

Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions
Yen-Ku Liu, Yun-Huei Pan, Pei-Fan Lu, Yun-Cheng Tsai, Samuel Yen-Chi Chen
https://arxiv.org/abs/2507.12835

Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions
Financial trading environments are characterized by high volatility, numerous macroeconomic signals, and dynamically shifting market regimes, where traditional reinforcement learning methods often fail to deliver breakthrough performance. In this study, we design a reinforcement learning framework tailored for financial systems by integrating quantum circuits. We compare (1) the performance of classical A3C versus quantum A3C algorithms, and (2) the impact of incorporating LSTM-based prediction…

Quantum Relational Knowledge Distillation
Chen-Yu Liu, Kuan-Cheng Chen, Keisuke Murota, Samuel Yen-Chi Chen, Enrico Rinaldi
https://arxiv.org/abs/2508.13054 https://

Quantum Relational Knowledge Distillation
Knowledge distillation (KD) is a widely adopted technique for compressing large models into smaller, more efficient student models that can be deployed on devices with limited computational resources. Among various KD methods, Relational Knowledge Distillation (RKD) improves student performance by aligning relational structures in the feature space, such as pairwise distances and angles. In this work, we propose Quantum Relational Knowledge Distillation (QRKD), which extends RKD by incorporatin…

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin, Wei-Ting Chen, Kuo-Chung Peng, Prayag Tiwari, Samuel Yen-Chi Chen, En-Jui Kuo
https://arxiv.org/abs/2508.06078

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into federated learning.Within Fed-QK-LSTM framework, we enhance human activity recognition (HAR) in privacy-sensitive environments and leverage quantum computing for distributed learning systems.The DeepConv-QK-LSTM architecture on each client node employs convolutional layers for efficient local pattern capture, this de…