7 citations · 12 across the 6 of their papers we have counts for
8 papers
Optimized Randomized Hamiltonian Simulation via Average-Error Analysis
Hayata Morisaki, Keisuke Fujii
Hamiltonian simulation is a central application of quantum computing. Randomized Hamiltonian simulation approximates the target dynamics by sampling quantum circuits and often allo…
Asymptotic Gate Count Bounds for Ancilla-Free Single-Qubit Synthesis with Arithmetic Gates
Kaoru Sano, Hayata Morisaki, Seiseki Akibue
We study ancilla-free approximation of single-qubit unitaries by gate sequences over Clifford+, where or their generalization. Let denote th…
Optimal ancilla-free Clifford+T synthesis for general single-qubit unitaries
Hayata Morisaki, Kaoru Sano, Seiseki Akibue
We propose two Clifford+ synthesis algorithms that are optimal with respect to -count. The first algorithm, called deterministic synthesis, approximates any single-qubit unit…
Explicit quantum surrogates for quantum kernel models
Akimoto Nakayama, Hayata Morisaki, Kosuke Mitarai +2
Quantum machine learning (QML) leverages quantum states for data encoding, with key approaches being explicit models that use parameterized quantum circuits and implicit models tha…
Error Crafting in Mixed Quantum Gate Synthesis
Nobuyuki Yoshioka, Seiseki Akibue, Hayata Morisaki +2
In fault-tolerant quantum computing, errors in unitary gate synthesis is comparable with noise inherent in the gates themselves. While mixed synthesis can suppress such coherent er…
End-to-end complexity for simulating the Schwinger model on quantum computers
Kazuki Sakamoto, Hayata Morisaki, Junichi Haruna +3
The Schwinger model is one of the simplest gauge theories. It is known that a topological term of the model leads to the infamous sign problem in the classical Monte Carlo method.…
Classical variational optimization of PREPARE circuit for quantum phase estimation of quantum chemistry Hamiltonians
Hayata Morisaki, Kosuke Mitarai, Keisuke Fujii +1
We propose a method for constructing circuits for quantum phase estimation of a molecular Hamiltonian in quantum chemistry by using variational optimization of q…
MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era
Leonardo Placidi, Ryuichiro Hataya, Toshio Mori +4
We introduce the first large-scale dataset, MNISQ, for both the Quantum and the Classical Machine Learning community during the Noisy Intermediate-Scale Quantum era. MNISQ consists…