activity
20032022
most citedDiscovery reach for wino and higgsino dark matter with a disappearing track signature at a 100 TeV collider

38 citations · 47 across the 6 of their papers we have counts for

collaborators

15 papers

quant-ph2022

Resolution enhancement of one-dimensional molecular wavefunctions in plane-wave basis via quantum machine learning

Rei Sakuma, Yutaro Iiyama, Lento Nagano +2

Super-resolution is a machine-learning technique in image processing which generates high-resolution images from low-resolution images. Inspired by this approach, we perform a nume…

physics.ins-det2022

The Phase-I Trigger Readout Electronics Upgrade of the ATLAS Liquid Argon Calorimeters

G. Aad, A. V. Akimov, K. Al Khoury +195

The Phase-I trigger readout electronics upgrade of the ATLAS Liquid Argon calorimeters enhances the physics reach of the experiment during the upcoming operation at increasing Larg…

quant-ph20217 cited

Measurement-Free Ultrafast Quantum Error Correction by Using Multi-Controlled Gates in Higher-Dimensional State Space

Toshiaki Inada, Wonho Jang, Yutaro Iiyama +4

Quantum error correction is a crucial step beyond the current noisy-intermediate-scale quantum device towards fault-tolerant quantum computing. However, most of the error correctio…

cs.LG20211 cited

Event Classification with Multi-step Machine Learning

Masahiko Saito, Tomoe Kishimoto, Yuya Kaneta +6

The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single lar…

quant-ph2021

Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications

Wonho Jang, Koji Terashi, Masahiko Saito +7

There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to ma…

cs.CV20211 cited

An Improvement of Object Detection Performance using Multi-step Machine Learnings

Tomoe Kishimoto, Masahiko Saito, Junichi Tanaka +3

Connecting multiple machine learning models into a pipeline is effective for handling complex problems. By breaking down the problem into steps, each tackled by a specific componen…