3 citations · 6 across the 8 of their papers we have counts for
9 papers
Copy-scarce learning of ground states
Kaito Wada, Nobuyuki Yoshioka
Probing multiple properties of ground states is central to understanding quantum matter, yet conventional readout can be prohibitively costly because it consumes many independently…
Provably Efficient Learning of Fermionic Correlations under Particle-Number Symmetry
Yuki Koizumi, Kaito Wada, Toshinori P. Takama +1
Predicting local fermionic correlations is a central task in quantum many-body physics, as these correlations encode many physically relevant local observables. The ubiquitous part…
Quantum Power Iteration Unified Using Generalized Quantum Signal Processing
Viktor Khinevich, Yasunori Lee, Nobuyuki Yoshioka +1
We propose a unifying framework for the state preparation using quantum power method algorithms based on generalized quantum signal processing (GQSP). We apply GQSP to realize quan…
Comprehensive Study on Heisenberg-limited Quantum Algorithms for Multiple Observables Estimation
Yuki Koizumi, Kaito Wada, Wataru Mizukami +1
In the accompanying paper of arXiv:2505.00697, we have presented a generalized scheme of adaptive quantum gradient estimation (QGE) algorithm, and further proposed two practical va…
Faster Quantum Algorithm for Multiple Observables Estimation in Fermionic Problems
Yuki Koizumi, Kaito Wada, Wataru Mizukami +1
Achieving quantum advantage in efficiently estimating collective properties of quantum many-body systems remains a fundamental goal in quantum computing. While the quantum gradient…
Heisenberg-limited adaptive gradient estimation for multiple observables
Kaito Wada, Naoki Yamamoto, Nobuyuki Yoshioka
In quantum mechanics, measuring the expectation value of a general observable has an inherent statistical uncertainty that is quantified by variance or mean squared error of measur…
Robust Angle Finding for Generalized Quantum Signal Processing
Shuntaro Yamamoto, Nobuyuki Yoshioka
Quantum Signal Processing (QSP), together with the quantum singular value transformation, is one of the central quantum algorithms due to its efficiency and generality in many fiel…
Balancing error budget for fermionic k-RDM estimation
Nayuta Takemori, Yusuke Teranishi, Wataru Mizukami +1
The reduced density matrix (RDM) is crucial in quantum many-body systems for understanding physical properties, including all local physical quantity information. This study aims t…
Adaptive measurement strategy for quantum subspace methods
Yuma Nakamura, Yoshichika Yano, Nobuyuki Yoshioka
Estimation of physical observables for unknown quantum states is an important problem that underlies a wide range of fields, including quantum information processing, quantum physi…