JIJ at IEEE Quantum Week 2026: Qamomile Tutorial and Technical Contributions
2026/8/14JIJ will participate in IEEE Quantum Week 2026 (QCE 2026), taking place September 13–18, 2026, in Toronto, Canada. JIJ researchers will contribute to two technical papers and two tutorials, while JIJ serves as the main organizer of QuBench 2026.
Qamomile Tutorial
As part of the program, JIJ will present “Introduction to Qamomile: Estimating Symbolically and Executing Concretely,” a technical tutorial on September 14 introducing a practical workflow for quantum resource estimation and execution using Qamomile.
As quantum algorithms grow in complexity, researchers and developers need practical ways to understand how resource requirements scale with problem size while maintaining a path from algorithm design to executable implementations.
The tutorial introduces Qamomile, which connects symbolic resource estimation and concrete execution within a unified workflow. A single quantum program description can be used across both stages, supported by a typed programming model, multi-backend transpilation, built-in algorithms, and integration with the OMMX optimization format.
Participants will learn how to write quantum programs in Qamomile, estimate resource requirements as functions of problem size, transpile suitable programs for execution on supported software backends, and explore quantum optimization workflows using OMMX.
Tutorial Details
Date: September 14, 2026
Time: 10:00–11:30 / 13:00–14:30 (EDT)
Venue: Metro Toronto Convention Centre, Toronto, Ontario, Canada
Presenters: Wei-Hao Huang, Keisuke Sato, Hiromichi Matsuyama, Kuan-Cheng Chen, Rosse Grassie
Agenda and materials: https://jij-inc.github.io/Qamomile-QCE2026-Tutorial/
Other Technical Contributions
Technical Papers
JIJ researchers will contribute to two technical papers spanning quantum systems and quantum machine learning.
Quantum hardware noise learning via differentiable Kraus representation on tensor networks
JIJ researchers: Ryo Sakai, Yu Yamashiro
This work explores the modeling and learning of quantum hardware noise using differentiable Kraus representation and a tensor-network-based method, supporting realistic noise-aware modeling and evaluation of quantum algorithms.
Quantum Kernel Feature Selection via Quadratic Unconstrained Binary Optimization
JIJ researcher: Louis Chen
This work investigates the application of Quadratic Unconstrained Binary Optimization (QUBO) to feature selection for quantum kernel methods, connecting optimization techniques with quantum machine learning workflows.
Schedule:
https://qce.quantum.ieee.org/2026/qce26-schedule/paper-schedule/
Quantum Machine Learning Tutorial
Louis Chen of JIJ will join experts from the National Center for High-performance Computing in Taiwan, NVIDIA, and Wells Fargo to co-present “Scalable Validation and Optimized Simulation for Quantum Machine Learning.”
The hands-on tutorial brings together perspectives across quantum computing, high-performance computing, and AI to explore scalable validation and optimized simulation techniques for quantum machine learning. Participants will work with reusable notebook-based examples and gain practical tools and workflows for QML simulation, validation, and performance optimization.
Tutorial Details: https://sites.google.com/niar.org.tw/qce26-tutorial-519
QuBench 2026
JIJ is the main organizer of QuBench 2026: Quantum Benchmarking, Validation, and Resource Estimation in Quantum-HPC Systems, a workshop held in conjunction with IEEE Quantum Week 2026.
QuBench 2026 brings together researchers, engineers, and practitioners to advance rigorous and reproducible approaches to quantum benchmarking, including simulator and emulator validation, compiler- and runtime-aware metrics, quantum-HPC performance evaluation, application-oriented benchmarking, and resource estimation.
QuBench Details: https://www.qubench.net/
Together, these activities reflect JIJ’s work across quantum computing research, software development, benchmarking, and practical computational workflows. JIJ will continue working to bridge advances in quantum computing research with tools and approaches that can be tested and applied in practice.
IEEE Quantum Week 2026 official webpage: https://qce.quantum.ieee.org/2026/