activity
20102025
most citedTopological superconductivity in quasicrystals

45 citations · 391 across the 32 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cond-mat.mtrl-sci2023★ 10 cited

Revisit of magnetic orders in 1/1 approximant crystals of Tsai-type quasicrystal from theoretical points of view

Takanori Sugimoto, Shintaro Suzuki, Ryuji Tamura +1

The various magnetic orders observed in approximant crystals of Tsai-type quasicrystal are important for deepening our understanding of mysterious magnetism in quasicrystals. Using…

quant-ph2023

Diamond-shaped quantum circuit for real-time quantum dynamics in one dimension

S. Miyakoshi, T. Sugimoto, T. Shirakawa +2

In recent years, quantum computing has evolved as an exciting frontier, with the development of numerous algorithms dedicated to constructing quantum circuits that adeptly represen…

quant-ph2023★ 24 cited

A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes?

Tsubasa Ichikawa, Hideaki Hakoshima, Koji Inui +18

Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring…

quant-ph2023

Quantum-circuit algorithms for many-body topological invariant and Majorana zero mode

Takanori Sugimoto

Topological states of matter are promising resources for composing fault-tolerant quantum computers, advancing beyond the limitations of current noisy intermediate-scale quantum de…

cond-mat.mes-hall2023★ 7 cited

Confined states and topological phases in two-dimensional quasicrystalline -flux model

Rasoul Ghadimi, Masahiro Hori, Takanori Sugimoto +1

Motivated by topological equivalence between an extended Haldane model and a chiral--flux model on a square lattice, we apply -flux models to two-dimensional bipartite quasic…

quant-ph2023★ 3 cited

VQE-generated quantum circuit dataset for machine learning

Akimoto Nakayama, Kosuke Mitarai, Leonardo Placidi +2

Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical prob…