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Todo Lab
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The lab uses computer simulations to uncover the behavior of quantum many-body systems.
AboutAbout the lab
Exploring quantum many-body phenomena through simulation
Todo Lab studies the behavior of quantum many-body systems using computer simulations.
Understanding the states of matter requires solving the many-body Schrödinger equation, but it is difficult to solve exactly even on modern supercomputers.
A key challenge in computational physics is therefore to reformulate the original equation into a form that is easier to simulate, without losing the symmetries and quantum correlations it contains.
The lab covers subjects ranging from quantum spin systems and real materials to quantum computers, and also develops and releases open-source software for large-scale computation.
Key terms
- Monte Carlo method
- A computational method that tries many cases using random numbers to estimate the quantity of interest
- Tensor network
- A way of representing large data, in which many numbers are intricately entangled, as a network of connected small components
- Phase transition
- A phenomenon in which the state of a material changes drastically when conditions such as temperature change, like water turning into ice
NewsLab news
Paper on tensor network calculations for the quantum algorithm QAOA published
Paper on ground-state preparation with a non-variational quantum algorithm published
A course participant supervised by the lab won the George Yancopoulos Innovation Award
A student won the Student Presentation Award from the Technical Committee on Quantum Information Technology
Press release: "Discovery of Universal Laws Hidden in Deep Learning"
ResearchResearch themes
The lab carries out its research using methods such as sampling techniques including Monte Carlo methods, path-integral representations of quantum fluctuations, information compression with tensor networks, and statistical machine learning. It aims to elucidate the states, phase transition phenomena, and dynamics specific to a wide range of quantum many-body systems, from quantum spin systems and real materials to quantum computers.
*Illustrative imageA portal for simulation software
To promote materials science simulation software developed in Japan, the lab maintains the portal site MateriApps. It is also working to create an environment where anyone can easily start running calculations.
*Illustrative imageImproving the worm algorithm
For the worm algorithm, which is used in Monte Carlo methods when the states are subject to constraints, the lab proposed a guideline for how to move the worm. Applied to the Ising model, it achieves higher computational efficiency than conventional methods.
*Illustrative imageTensor network renormalization group
Real-space renormalization group methods using tensor networks had the problem that the computational cost balloons as the dimension increases. The lab is developing methods with lower computational cost, as well as methods with higher accuracy for the same computation time.
*Illustrative imagePredicting critical temperature with machine learning
The lab proposed a method that predicts the critical temperature of the Ising model with a neural network. By using the lattice Green's function as the feature, a common network can be used for lattices with different structures.
MembersFaculty
The members of Todo Lab consist of faculty, project researchers, visiting collaborative researchers, graduate students, and academic support staff.As of 2026
- ProfessorSynge TodoPublications ↗
- Assistant ProfessorHidemaro SuwaPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Ising Criticality2” Damian Owls / CC BY-SA 4.0 (cropped)
- A portal for simulation software: “Le Centre Datarmor, ambiance spatiale (Ifremer )” Gregory Rocher / CC BY 4.0 (cropped)
- Improving the worm algorithm: “Gibbs60” Thp229 / CC BY-SA 4.0 (cropped)
- Tensor network renormalization group: “Isinginteractions” Bzhang1990 / CC BY-SA 3.0 (cropped)
- Predicting critical temperature with machine learning: “Accuracy per T allN dec13 submission” Fermigone / CC BY-SA 4.0 (cropped)
- Related lab (Tsuji Lab): “Cloud of ultracold atoms levitated in a magneto-optical trap” Balping / CC BY-SA 4.0 (cropped)
- Related lab (Katsura Lab): “Icosahedrite Diffraction Pattern” Paul Steinhardt / CC BY-SA 4.0 (cropped)
- Related lab (Ashida Lab): “Superconducting Metamaterial Chip 002” OJB Quantum / CC BY 4.0 (cropped)
- Related lab (Ueda Lab): “JILA's strontium optical atomic clock” Sebastian Blatt, JILA, University of Colorado / Public domain (cropped)
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