101 citations · 192 across the 12 of their papers we have counts for
12 papers
Impact of molecular orbital localization on quantum computational resources for Hamiltonian simulation: A benchmark study of hydrogen chain systems
Kenji Sugisaki, Yuhei Tachi, Masayoshi Terabe +1
We investigate how molecular orbitals used as the basis of wave function expansion and how operator coefficient-based and locality-based Hamiltonian truncation affects the computat…
Quantum spectroscopy of topological dynamics via a supersymmetric Hamiltonian
Hiroshi Yamauchi, Satoshi Kanno, Yuki Sato +4
Topological data analysis (TDA) characterizes complex dynamics through global invariants, but classical computation becomes prohibitive for high-dimensional data. We reinterpret ti…
Quantum algorithm for Electromagnetic Field Analysis
Hiroyuki Tezuka, Yuki Sato
Partial differential equations (PDEs) are central to computational electromagnetics (CEM) and photonic design, but classical solvers face high costs for large or complex structures…
Single-shot high-resolution spectroscopy of single-photon-level optical pulses using a virtually imaged phased-array and single-photon avalanche diode array
Yuki Nagoro, Hidehito Sato, Hiroyuki Tezuka +1
Single-shot high-resolution spectroscopy at the single-photon-level has emerged as a promising measurement technique, enabling novel observations and evaluations that were previous…
Quantum algorithm for partial differential equations of non-conservative systems with spatially varying parameters
Yuki Sato, Hiroyuki Tezuka, Ruho Kondo +1
Partial differential equations (PDEs) are crucial for modeling various physical phenomena such as heat transfer, fluid flow, and electromagnetic waves. In computer-aided engineerin…
A combined quantum-classical method applied to material design: optimization and discovery of photochromic materials for photopharmacology applications
Qi Gao, Michihiko Sugawara, Paul D. Nation +4
Integration of quantum chemistry simulations, machine learning techniques, and optimization calculations is expected to accelerate material discovery by making large chemical space…
Approximate complex amplitude encoding algorithm and its application to data classification problems
Naoki Mitsuda, Tatsuhiro Ichimura, Kouhei Nakaji +6
Quantum computing has a potential to accelerate the data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The…
Generative model for learning quantum ensemble via optimal transport loss
Hiroyuki Tezuka, Shumpei Uno, Naoki Yamamoto
Generative modeling is an unsupervised machine learning framework, that exhibits strong performance in various machine learning tasks. Recently we find several quantum version of g…
Deterministic and random features for large-scale quantum kernel machine
Kouhei Nakaji, Hiroyuki Tezuka, Naoki Yamamoto
Quantum machine learning (QML) is the spearhead of quantum computer applications. In particular, quantum neural networks (QNN) are actively studied as the method that works both in…
Grover search revisited; application to image pattern matching
Hiroyuki Tezuka, Kouhei Nakaji, Takahiko Satoh +1
The landmark Grover algorithm for amplitude amplification serves as an essential subroutine in various type of quantum algorithms, with guaranteed quantum speedup in query complexi…
Quantum-enhanced neural networks in the neural tangent kernel framework
Kouhei Nakaji, Hiroyuki Tezuka, Naoki Yamamoto
Recently, quantum neural networks or quantum-classical neural networks (qcNN) have been actively studied, as a possible alternative to the conventional classical neural network (cN…
Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicator
Kouhei Nakaji, Shumpei Uno, Yohichi Suzuki +6
Efficient methods for loading given classical data into quantum circuits are essential for various quantum algorithms. In this paper, we propose an algorithm called Approximate Amp…