7 papers
Trade-off between Cooling-Step Count and Geometric Implementation Cost in Non-Markovian Algorithmic Cooling
Yohei Azumai, Yoshihiko Hasegawa
Quantum cooling is important for reliable quantum computation but involves a trade-off between cooling performance and implementation resources. Although reservoir memory can impro…
Lie-Algebraic Subspace Quantization for Zero-Shot Quantum Learning and Barren-Plateau Mitigation
Yuhan Yao, Yoshihiko Hasegawa
The barren plateau phenomenon severely limits the scalability of parameterized quantum circuits (PQCs). We present an analytical framework for zero-shot classical-to-quantum parame…
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…
Dynamics-independent bounds on state transformations and precision in open quantum systems
Yoshihiko Hasegawa
We derive dynamics-independent upper bounds on achievable quantum state transformations. Modeling the evolution as a joint unitary on the system and its environment, we show that t…
High-order interactions in quantum optomechanics: fluctuations, dynamics and thermodynamics
Alessandro Ferreri, Vincenzo Macrì, Yoshihiko Hasegawa +1
Quantum optomechanics describes the interaction between a confined field and a fluctuating wall due to radiation pressure. The dynamics of this system is typically understood using…
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…