Tootfinder

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…

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-…

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 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…