Artificial intelligence is having a transformative impact on health and scientific discovery. This presentation will trace the evolution from foundational breakthroughs to the sophisticated capabilities of today's large-scale AI models. It will explore how these advanced systems are creating new possibilities across the healthcare landscape, from accelerating therapeutic development to enhancing diagnostic processes and interpreting complex medical data. The session will also take a deeper look at the future possibilities for AI in health and explore the emerging role of agentic AI in scientific discovery. The core theme is the responsible development of AI to create tools that assist scientists, support healthcare professionals, and empower users.

Bio:
Dr Joseph Ledsam leads Google Health in Japan, where he works across AI research, digital health and health in Google products. He has led research in medical AI, genomics and drug discovery published in journals including Nature, Nature Medicine and Nature Methods. Before moving to Japan he worked as a medical doctor in the UK, and founded the Health Research and Genomics teams in Google DeepMind. He obtained his medical degree from The University of Leeds, UK, and was a research fellow at University College London during his clinical residency.

== Mini-workshop Program ==

9:45 - 10:15
Erik Loetstedt (RIKEN)
Rovibrational energy levels of molecules by quantum computing

10:15 - 10:45
Ryusuke Hamazaki (RIKEN)
Quantum many-body Mpemba effect through resonances

10:45 - 11:15
Yang-yang Tan (U. Tokyo)
Generative Modeling of Stochastic Dynamics for Long-Time Evolution

11:15 - 11:45
Yan Lyu (RIKEN)
Charmonia scattering from lattice QCD

11:45 - 13:00
Lunch

13:00 - 13:30
Arata Yamamoto (RIKEN)
Quantum anomaly for benchmarking quantum computing

13:30 - 14:00
Tianyin Li (RIKEN)
Efficient Quantum Simulations of Yang-Mills theory with Maximal-tree Gauge

14:00 - 14:30
Yahui Chai (RIKEN)
Probing Resonances through Real-Time Particle Scattering

14:30 - 15:00
Ermal Rrapaj (LBL)
Quantum Simulations of Two-Dimensional Non-Abelian Adjoint String Breaking

15:00 - 15:30
Coffee Break

15:30 - 17:00
Dima Kharzeev (U. Connecticut)
Quantum Simulations of Entanglement in Jet Production and Structure Functions at the EIC

The Electron–Ion Collider (EIC) offers an opportunity to explore the quantum structure of hadronic matter and the role of entanglement in high-energy scattering. I will present our results on quantum simulations of entanglement in jet production and structure functions, highlighting how quantum computing can help investigate correlations in partonic dynamics. I will discuss the physical insights emerging from these simulations and their implications for connecting quantum information concepts with observables at the EIC.

Data assimilation (DA) is the core of numerical weather prediction, estimating an accurate state of the weather model by combining forecast and observations. DA provides a general approach and is being applied to various problems beyond weather. The state estimation is a linear solve where the background error covariance B is essential. Since B is an N × N matrix with N ~ 10^8 to 10^9 for the weather model, the full B has never been used. For decades the community has approximated B by parameterized covariance models or by low-rank ensembles with localization and thereby discards genuine structures that we know exist from large-ensemble experiments. Fault-tolerant quantum computing (FTQC) could remove this constraint, since quantum linear system solvers scale polylogarithmically in N. However, the DA equations cannot be applied naively: the observation-space form squares the block-encoding normalization factor for observations with wide footprints, and the state-space form contains B^{-1}, whose condition number ~10^8. This talk presents a combined state-observation formulation that avoids both difficulties: it contains no B^{-1}, and its condition number is governed by the observation error covariance R rather than by B. Numerical experiments with the Lorenz-96 model reproduce the exact solution with the full B and outperform localized ensemble Kalman filters. The formulation assumes that B and the observation operator are provided as block-encoded oracles, and how to construct such oracles without ever storing the matrices is the open problem. A first concrete target is an end-to-end demonstration with Lorenz-96 on a quantum simulator with only seven data qubits, which I would like to discuss with the iTHEMS community.

Causal inference from neural time series is confounded by correlations induced by hidden common inputs, a problem that is especially acute because the full set of variables shaping neural activity is rarely accessible. Prediction based measures such as Granger causality can therefore falsely attribute such dependencies to causal influence. Delay coordinate reconstruction, which is also closely connected to observability in control theory, provides a complementary route: in deterministic systems, downstream delays can reconstruct upstream causes, whereas the reverse does not generally hold. This asymmetry provides a signature of causal direction, as exploited by convergent cross mapping. In stochastic dynamics, however, dynamical and observation noise enter delay coordinates and prevent exact trajectory reconstruction. Here, we develop data driven criteria that extend this observability principle to partially observed stochastic systems. First, we show that observability can be tested by regression of time lagged covariance matrices, which removes direct noise contributions while preserving the observability relation. Second, we show that causal direction can be tested through inclusion of stochastic Koopman eigenvalues estimated from upstream and downstream delay coordinates, because downstream observations generically contain the dynamical components observable upstream. Numerical examples show that the proposed criteria recover causal direction and reject spurious dependencies induced by unobserved common inputs, including cases in which Granger causality and convergent cross mapping fail to distinguish common input from causal influence. Although motivated by neural recordings, the framework applies broadly to partially observed stochastic dynamical systems beyond neuroscience.

Birationally rigid varieties form an important class of Fano varieties. A Fano variety is called birationally rigid if it is not birationally equivalent to any other Mori fibre space. Many well-known examples of birationally rigid smooth Fano threefolds have been known for a long time. However, the first example of a birationally rigid singular Fano threefold was found only recently by Krylov, Okada, Paemurru, and Park. Their example is a sextic double solid with only cA1 singularities.

On the other hand, Paemurru proved that sextic double solids with cAn singularities are not birationally rigid when n is at least four. In our work, we generalized the method of Krylov, Okada, Paemurru, and Park to more general cDV singularities and found the first example of a birationally rigid Fano threefold with cD-type singularities.

This is joint work with Jheng-Jie Chen and Takuzo Okada.

We show how the Born--Oppenheimer effective field theory (BOEFT) provides a unified description of ordinary and exotic quarkonia grounded on the non-relativistic expansions of QCD and supplemented with lattice QCD inputs. We show applications of the BOEFT to tetraquarks, pentaquarks, quarkonium hybrids and on threshold effects in the quarkonium spectrum. We present the insights that BOEFT delivers us on the mature of these XYZ exotics states.

Noise and errors are unavoidable in any realistic quantum process, including processes designed to reduce noise and errors in the first place. In particular, quantum thermodynamical protocols for cooling can be significantly affected, potentially altering both their performance and efficiency. Analytically characterizing the impact of such errors becomes increasingly challenging as the system size grows, particularly in deep quantum circuits where noise can accumulate in complex ways. To address this, we introduce a general framework for approximating the cumulative effect of gate-dependent noise using a global depolarizing channel. We specify the regime in which this approximation provides a reliable description of the noisy dynamics. Applying our framework to the thermodynamical two-sort algorithmic cooling (TSAC) protocol, we analytically derive its asymptotic cooling limit in the presence of noise. Using the cooling limit, the optimal cooling performance is achieved by a finite number of qubits --- distinguished from the conventional noiseless TSAC protocol by an infinite number of qubits --- and fundamental bounds on the achievable ground-state population are derived. This approach opens new avenues for exploring noisy quantum thermodynamical processes.

We classify maximally enhanced, rank-preserving non-supersymmetric heterotic strings in eight dimensions by studying orbifolds of supersymmetric heterotic strings on T^2.We first review the construction in nine dimensions, where it reproduces the 95 maximally semisimple enhancement points obtained from previous extended-Dynkin analyses. We then start from the maximally enhanced supersymmetric Narain points in eight dimensions and use Kac's theorem to enumerate candidate order-two inner actions, retaining only those that lift to consistent Narain-lattice shifts. This gives 1210 maximal enhancement points, of which 71 are tachyon-free, and 25 have neither tachyons nor shifted-sector massless scalars. For each entry we determine the gauge algebra and the low-lying scalar and fermion spectra, and for the tachyon-free cases we evaluate the one-loop cosmological constant. For the 25 subcases without shifted-sector massless scalars, we further compute the Hessian of the one-loop potential and test the refined de Sitter swampland conjecture.

How was our Universe created? This is one of the most fundamental questions in cosmology. Quantum cosmology attempts to answer it by using a "wave function of the Universe," and for four decades the discussion has been dominated by two proposals: the no-boundary proposal of Hartle and Hawking, and the tunneling proposal of Vilenkin. The two predict very different initial states for our Universe, yet for a long time there was no common framework in which they could be exactly analyzed and compared.

In this talk I will begin with a pedagogical introduction to the wave function of the Universe and to the Lorentzian path integral formulation based on Picard-Lefschetz theory, which makes the gravitational path integral well defined without the ambiguities of the traditional Euclidean approach and allows both proposals to be treated on the same footing. I will then present our recent results. Using Lefschetz-thimble analysis together with resurgence theory, we resolve the Stokes-line ambiguity that had obscured earlier analyses and show that the path integral uniquely yields the tunneling wave function rather than the no-boundary one. The no-boundary saddles nevertheless leave a trace: their contributions exactly cancel the Borel ambiguity of the perturbative quantum-gravity corrections around the tunneling saddle, demonstrating that resurgence is at work in quantum cosmology. I will also discuss quantum cosmology beyond four-dimensional general relativity, using two-dimensional Jackiw-Teitelboim gravity as an illustrative model.

Mathematical Application Research Team is delighted to welcome Prof. Kodama Yuya from Kagoshima University for an upcoming team meeting. We warmly invite everyone to join us to hear his talk.

Title: Thompson's groups and knot theory

Abstract:
Thompson’s groups were introduced by Richard Thompson in the 1960s. They first appeared in mathematical logic, but they have since been studied in many other areas of mathematics. For example, they were rediscovered in homotopy theory, and some researchers have tried to use them in cryptography. In 2014, Vaughan Jones found an interesting connection between Thompson’s groups and knot theory. In this talk, I will first introduce Thompson’s groups and some of their connections with other areas. I will then explain the basic idea of Jones’s theory. If time permits, I will also mention my joint work with Takano of Osaka University.

The Kuramoto-Sivashinsky (KS) equation describes various phenomena exhibiting chaos and instabilities, including flames, reaction-diffusion processes, and thin liquid film flows. It resembles the Kardar-Parisi-Zhang (KPZ) equation, with the key difference being the negative "viscosity" in the KS equation. The KS equation is known to belong to the KPZ universality class: at large scales, its statistical properties are governed by the KPZ fixed point, characterized by the dynamical exponent $z=3/2$ and a known PDF of fluctuations. In this talk, I will analyze the stochastic KS equation using the functional renormalization group (FRG). In this framework, attractive fixed points of the FRG equation determine large-scale universal behavior, while unstable fixed points govern small-scale universal behavior. I will show that the two-point correlation function exhibits, besides the KPZ scaling at large scale, another universal scaling regime at small scale, with $z=1$. This scaling is very robust as it is intrinsic to the KS dynamics. It arises from the vanishing of the effective viscosity when evolving from its microscopic negative KS value, to its macroscopic effective positive KPZ value. I will also present results from direct numerical simulations, which confirm these findings.

In the past 20 years, there has been significant progress in our understanding of quantum gravity based on AdS/CFT. Recently, study of quantum gravity beyond AdS/CFT attracts a lot of attention, where there is no longer an asymptotically AdS boundary on which we can perform measurements with infinite accuracy. Thus it is important to understand observables in gravitational spacetimes with no asymptotic AdS boundaries, what an observer experiences and measures in such a spacetime,  and what is the microscopic theory that describes them.  This workshop aims to gather researchers in the this subject to exchange cutting edge ideas, aiming to establish new research directions and collaborations.

How evolution navigates sequence space depends on the shape of the fitness landscape. Viewed as a scalar field over a discrete genotype graph, theoretical landscapes range from single-peaked to rugged and fragmented, with very different mutational accessibility. Which shapes real biological sequences occupy remains an open empirical question. Deep mutational scanning now measures sequence–function maps across complete combinatorial regions of sequence space, but the resulting landscapes are large, noisy, and hard to compare across studies. Existing analyses typically reduce each dataset to a few summary statistics, discarding which peaks connect to which, and through which saddles.

We are developing a two-layer toolkit for empirical sequence–function landscapes. A statistical layer models overdispersed sequencing counts directly to separate genuine phenotypic differences from measurement noise, pruning spurious topological features before analysis. A topological layer then summarises the landscape as a merge tree of peaks, saddles, and the high-fitness ridges that link them, using persistence as a natural scale to distinguish global structure from minor ruggedness.

I will first illustrate this approach on the largest published combinatorially complete protein-coding landscape, spanning over 260,000 sequences. Filtering by measurement uncertainty and persistence simplifies an apparently rugged surface, revealing that major peaks remain connected through high-fitness saddles rather than isolated by deep valleys. I will then present preliminary findings from a comparative atlas of more than 150 combinatorially complete sequence–function landscapes spanning DNA, RNA, and protein. By comparing merge-tree structure across this collection, we ask whether high-fitness ridges that bypass rugged regions recur across unrelated sequences, and whether landscapes group by the biological function they encode.

Many of us write software for research without much training in software development. A quick script and a few clever fixes can get us surprisingly far—until the project grows, a collaborator joins, or, heaven forbid, a paper revision means rerunning everything six months later. AI coding assistants make creating and changing software easier, but a well-organised project helps both humans and AI work with it. So what does “well-organised” actually mean? That’s what this hands-on workshop is about. Through small, approachable examples, we’ll explore practical habits for collaboration, reproducible environments, testing whether code does what we intend, and automating repetitive tasks—and how to use AI coding assistants to support those habits. You’ll leave with a repository and tutorial you can adapt to your own research projects.

The KEK Theory Workshop is an annual workshop on string theory and quantum field theory.
Since 2014, it has been held every winter as an international workshop and has become one of the major annual events in the high-energy physics community in Japan.
This year’s workshop will be held on site at the KEK Tsukuba Campus from November 25 to 27, and it will be jointly organized with RIKEN iTHEMS.

The workshop this year aims to provide a forum for extensive discussions on recent developments in string theory, matrix models, gauge/gravity duality, black-hole microstates, lattice constructions of chiral gauge theories, and open quantum systems.

Centromeres are highly variable and rapidly evolving regions of the human genome, and are frequently implicated in various cancers. Human centromeres consist of highly repetitive DNA sequences known as α-satellite higher-order repeats (HORs), which makes their analysis using conventional genome sequencing approaches extremely challenging. Although long-read sequencing technologies now enable near-complete sequencing of centromeres, applying these methods to large numbers of samples remains difficult. As a result, our understanding of centromere diversity across populations and its role in chromosomal rearrangements in cancer has remained limited.

In this study, by focusing on rare k-mer markers, we demonstrate that α-satellite HOR haplotypes cluster into genetically related haplogroups. We further developed a software tool, ascairn, to accurately infer these haplogroups from common short-read whole-genome sequencing (WGS) data. Applying our approach to a dataset of more than 3,300 human samples revealed extensive centromere diversity and geographic variation.

We also applied the rare k-mer–based approach to centromere sequences from 104 Japanese individuals generated as part of the Japanese Pangenome Project. This analysis revealed characteristic patterns of centromere evolution in the Japanese population and provided new insights into Japanese-specific centromere variation.

In addition, we used this method to investigate class-defining chromosomal translocations, specifically der(1;19), also known as 1p/19q co-deletion, in oligodendrogliomas. By analyzing 142 samples from TCGA, we hypothesized that the breakpoints occurred on the p-arm side of D1Z7 and the q-arm side of D19Z3. This hypothesis was subsequently validated through extensive long-read sequencing in two cases, indicating that der(1;19) results in a dicentric chromosome containing two functional centromeres. We also observed that chromosomal rearrangements preferentially occurred in haplotypes with longer repetitive arrays on either the q-arm or p-arm side of the centromere. Furthermore, we identified specific centromere haplogroups associated with either increased or decreased likelihoods of der(1;19) translocations.

Our findings show that rare k-mer analysis is a powerful approach for investigating centromere complexity and clarifying the mechanisms underlying centromere-associated abnormalities in cancer.

This is a lecture-style seminar of quantum computation. The tentative schedule consists of three slots: 10:30-12:00, 13:30-15:00, and 15:30-17:00. Due to equipment limitations, the blackboard might not be visible on Zoom, so we recommend attending in person.

This is a joint seminar co-hosted by Institute for Physics of Intelligence (iπ), UTokyo, and the Venue is #1042,10F of Science Bldg.1

Optimal transport theory is a useful theoretical framework for geometrically characterizing the distance between probability distributions.
It is not only used in recent research on generative models but has also been applied to detect information-processing capabilities in biological systems from a statistical perspective, and to discuss thermodynamic efficiency. In this seminar, I will begin with an introduction to optimal transport theory, and also discuss our research on its various applications.

More specifically, I will discuss the following topics: 1) The problem of minimizing entropy production within a finite time and its experimental testing. 2) The application of optimal transport to the training of generative models, specifically diffusion models, and their optimal protocols. 3) Quantifying information processing performance using optimal transport in biological signal transduction.

We will hold our annual gathering, **“iTHEMS NOW & NEXT 2027,” from July 13 to 15, 2027**.

iTHEMS NOW & NEXT has traditionally been a two-day in-house event, providing an important opportunity for all iTHEMS members, including visiting researchers and new members, to gain a comprehensive overview of current activities across iTHEMS, to learn about research beyond their own fields, and to discuss where iTHEMS should go next.

In 2027, we will **expand NOW & NEXT from two to three days** and, for the first time, invite leading researchers from outside iTHEMS across a broad range of research fields. While the first two days will retain the main character of our traditional in-house gathering, the extended three-day format is intended to create a broader opportunity for scientific exchange and, in particular, to enable leading researchers from outside iTHEMS to become better acquainted with our research activities and with the researchers who make up the iTHEMS community.

As in previous years, the program will present activities across iTHEMS, including those of its divisions, research teams, working groups, and study groups, together with research presentations and poster sessions by iTHEMS members. Particular emphasis will be placed on communication across disciplinary and organizational boundaries and on identifying emerging research directions and opportunities for new collaborations.

The additional third day will build on the presentations and discussions of the preceding two days and provide an opportunity for invited researchers to gain a deeper understanding of the breadth and strengths of research at iTHEMS and to interact directly with our members. We hope that these exchanges will not only introduce iTHEMS and its researchers more widely to leading scientists in different fields, but also bring new perspectives to our community and open possibilities for future scientific interactions and collaborations.

Through this expanded three-day NOW & NEXT 2027, we hope to share where iTHEMS is now and where it is heading next, both within our own community and with leading researchers from outside iTHEMS, and to explore together new possibilities for the future development of iTHEMS.