From the 1 of 7 linked papers with an AI index.
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Lie-Algebraic Subspace Quantization for Zero-Shot Quantum Learning and Barren-Plateau Mitigation
Yuhan Yao, Yoshihiko Hasegawa
The paper introduces a Lie‑algebraic subspace quantization method that maps classical neural‑network weights onto low‑dimensional quantum evolutions, enabling zero‑shot quantum lea…
Gradient Analysis of Barren Plateau in Parameterized Quantum Circuits with multi-qubit gates
Yuhan Yao, Yoshihiko Hasegawa
The emergence of the Barren Plateau phenomenon poses a significant challenge to quantum machine learning. While most Barren Plateau analyses focus on single-qubit rotation gates, t…
Direct Gradient Computation for Barren Plateaus in Parameterized Quantum Circuits
Yuhan Yao, Yoshihiko Hasegawa
The barren plateau phenomenon, where the gradients of parametrized quantum circuits become vanishingly small, poses a significant challenge in quantum machine learning. While previ…
Avoiding Barren Plateaus with Entanglement
Yuhan Yao, Yoshihiko Hasegawa
In the search for quantum advantage with near-term quantum devices, navigating the optimization landscape is significantly hampered by the barren plateaus phenomenon. This study pr…