32 citations · 71 across the 7 of their papers we have counts for
31 papers
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…
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…
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…
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…
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…
Fast convolutional neural networks on FPGAs with hls4ml
Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti +17
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate a…