I have recently been investigating holography for open systems and have developed a method to compute correlation functions of a CFT governed by the Lindblad equation from its gravitational dual. In an open system, the state of the subsystem of interest cannot remain pure, and one naively expects its entropy to grow over time. It is then natural to expect that this thermalization process is accompanied, on the gravity side, by black hole formation. In this talk, after giving an overview of holography for open systems, I will present a numerical nonperturbative analysis of the dynamics of JT gravity coupled to a scalar field, and show that black holes indeed form in this setup.

15:30–16:00
NOW&NEXT of DEEP-IN WG (Lingxiao Wang)
Self-Introduction of Members
16:00–16:20
AI Team and DEEP-IN (Masato Taki)
16:20–16:40
Inverse Problems in HEP (Tae-Geun Kim, FudanU)
16:40–17:00
Inverse Modeling Distributions (Yang-yang Tan, UTokyo)

Advances in LLMs have led to agents with knowledge and operational capabilities comparable to human scientists, suggesting potential to assist, accelerate, and automate research. Physics, especially theoretical and computational physics, which requires integrating analytical reasoning, code-based computation, and profound domain expertise, is well suited for verifying the end-to-end research capabilities of AI scientists. Accordingly, we construct a general-purpose AI physicist PhysMaster, equipped with a layered academic knowledge base, adapted to the agent skill ecosystem, and adopting an adaptive exploration strategy that balances efficiency and exploration, enabling robust performance in ultra-long-horizon tasks; PhysMaster has been open-sourced. Meanwhile, we introduce PRL-Bench (Physics Research by LLMs), a benchmark with 100 tasks adapted from recent Physical Review Letters papers, covering astrophysics, condensed matter physics, high-energy physics, quantum information, and statistical physics. Evaluation across frontier models shows that failures are dominated by conceptual and formulaic errors, and that exploration and derivations remain unstable over long horizons. In addition, we develop domain-specialized AI scientists, including LQCD Master, which integrates Lattice QCD workflows and expert skills, enabling automated generation and submission of lattice computation scripts from concise physics goals.

The formalism of six operations, pioneered by Grothendieck and Verdier, serves as a unifying framework for studying cohomological phenomena. This language realizes Poincaré-type duality and transfer maps as certain adjunctions between stable $\infty$-categories of sheaves. In this talk, we highlight the theory of six operations in topology and apply it to provide an intrinsic version of the Pontryagin-Thom construction. We then discuss the intrinsic construction of invariants coming from Seiberg-Witten theory, which is based on the speaker's previous work.

[Registration Closed]
Due to high demand and venue capacity limits, registration for this course is now closed as of April 25. If you wish to be placed on a waiting list in case of cancellations, please contact us via the inquiry form at the bottom of this page.

One of the central goals of quantum information theory is to quantitatively clarify the relationship between the performance of quantum information processing and the valuable quantum features that underlie it. In this lecture, we will discuss quantum resource theories, a framework that provides a useful approach to this question. By presenting concrete examples—starting with entanglement theory, the most representative resource theory—as well as recent research results, we will see how perspectives and tools from information theory enable the quantification of quantum resources and the characterization of their convertibility. Beyond entanglement theory, we plan to discuss other key settings such as quantum thermodynamics, resource theory of asymmetry, and quantum magic—relevant resource in fault-tolerant quantum compuation. The overall aim of this lecture is to provide new analytical viewpoints that can be applied to a wide range of systems and quantum information processing tasks.

While we do not plan to change the overall start and end times for each day, the detailed lecture schedule is subject to change. The intensive course will be held over three days. Please register for the course using the form.
The registration deadline is May 7 (Thu).
Please note that the registration form is the same for all three days, so you only need to register once.

The 1st day: May 11 (Mon)
13:30-15:00 Lecture 1
15:00-15:30 Coffee break
15:30-17:00 Lecture 2

This event is in-person only.

[Registration Closed]
Due to high demand and venue capacity limits, registration for this course is now closed as of April 25. If you wish to be placed on a waiting list in case of cancellations, please contact us via the inquiry form at the bottom of this page.

One of the central goals of quantum information theory is to quantitatively clarify the relationship between the performance of quantum information processing and the valuable quantum features that underlie it. In this lecture, we will discuss quantum resource theories, a framework that provides a useful approach to this question. By presenting concrete examples—starting with entanglement theory, the most representative resource theory—as well as recent research results, we will see how perspectives and tools from information theory enable the quantification of quantum resources and the characterization of their convertibility. Beyond entanglement theory, we plan to discuss other key settings such as quantum thermodynamics, resource theory of asymmetry, and quantum magic—relevant resource in fault-tolerant quantum compuation. The overall aim of this lecture is to provide new analytical viewpoints that can be applied to a wide range of systems and quantum information processing tasks.

While we do not plan to change the overall start and end times for each day, the detailed lecture schedule is subject to change. The intensive course will be held over three days. Please register for the course using the form.
The registration deadline is May 7 (Thu).
Please note that the registration form is the same for all three days, so you only need to register once.

The 2nd day: May 12 (Tue)
9:00–10:30 Lecture 3
10:30–11:00 Coffee break
11:00–12:30 Lecture 4
12:30-13:30 Lunch time
13:30-15:00 Lecture 5
15:00-15:30 Coffee break
15:30-17:00 Lecture 6

This event is in-person only.

A doubly stochastic matrix is unistochastic if its entries correspond to the squared moduli of a unitary matrix. Determining which n × n doubly stochastic matrices admit such a representation remains an open problem at the intersection of convex geometry, combinatorics, and quantum information. For 3 × 3 matrices, elegant triangle inequalities provide a complete characterization: the unistochastic set occupies approximately 75% of the Birkhoff polytope and exhibits deltoid cross-sections. For n ≥ 4, the characterization problem remains unresolved and is influenced in unexpected ways by the prime factorization of n via the defect of the Fourier matrix. This presentation surveys these results and then establishes a connection to a second, seemingly unrelated question: given a tripartite quantum state with small conditional mutual information, to what extent can one subsystem be recovered from the others? The Petz recovery map and its rotated variants offer a universal solution. These two topics are linked through coherification, which concerns when a classical stochastic process can be elevated to coherent quantum dynamics, and through the conditional mutual information as a continuous measure of non-unistochasticity. The talk concludes with open problems at this interface, including the star-shapedness conjecture for n = 4 and the pursuit of tighter recovery bounds.

In quantum machine learning (QML), classical data are often encoded as quantum pure states and processed directly as quantum representations, motivating \emph{representation-level generative modeling} that samples new quantum states from an underlying pure-state ensemble rather than re-preparing them from perturbed classical inputs. However, extending \emph{score-based} diffusion models with well-defined reverse-time samplers to quantum pure-state ensembles remains challenging, due to the non-Euclidean geometry of the complex projective space $\mathbb{CP}^{d-1}$ and the intractability of transition densities. We propose \emph{Stochastic Schr\"odinger Diffusion Models} (SSDMs), an intrinsic score-based generative framework on $\mathbb{CP}^{d-1}$ endowed with the Fubini--Study (FS) metric. SSDMs formulate a forward Riemannian diffusion with a stochastic Schr\"odinger equation (SSE) realization, and derive reverse-time dynamics driven by the Riemannian score $\nabla_{\mathrm{FS}} \log p_t$. To enable training without analytic transition densities, we introduce a local-time objective based on a local Euclidean Ornstein--Uhlenbeck approximation in FS normal coordinates, yielding an analytic teacher score mapped back to the manifold. Experiments show that SSDMs faithfully capture target pure-state ensemble statistics, including observable moments, overlap-kernel MMD, and entanglement measures, and that SSDM-generated quantum representations improve downstream QML generalization via representation-level data augmentation.

[Registration Closed]
Due to high demand and venue capacity limits, registration for this course is now closed as of April 25. If you wish to be placed on a waiting list in case of cancellations, please contact us via the inquiry form at the bottom of this page.

One of the central goals of quantum information theory is to quantitatively clarify the relationship between the performance of quantum information processing and the valuable quantum features that underlie it. In this lecture, we will discuss quantum resource theories, a framework that provides a useful approach to this question. By presenting concrete examples—starting with entanglement theory, the most representative resource theory—as well as recent research results, we will see how perspectives and tools from information theory enable the quantification of quantum resources and the characterization of their convertibility. Beyond entanglement theory, we plan to discuss other key settings such as quantum thermodynamics, resource theory of asymmetry, and quantum magic—relevant resource in fault-tolerant quantum compuation. The overall aim of this lecture is to provide new analytical viewpoints that can be applied to a wide range of systems and quantum information processing tasks.

While we do not plan to change the overall start and end times for each day, the detailed lecture schedule is subject to change. The intensive course will be held over three days. Please register for the course using the form.
The registration deadline is May 7 (Thu).
Please note that the registration form is the same for all three days, so you only need to register once.

The 3rd day: May 15 (Fri)
9:00–10:30 Lecture 7
10:30–11:00 Coffee break
11:00–12:30 Lecture 8
12:30-13:30 Lunch time
13:30-15:00 Lecture 9
15:00-15:30 Coffee break
15:30-17:00 Seminar (or Lecture 10)

This event is in-person only.

Singularities are locations where something is exceptional. In particular, singularities of differentiable maps are mathematical concepts corresponding to stationary points of functions and apparent contours of surfaces under projection onto the retina. These are unavoidable in general, but important to study the shape of spaces and behavior of maps. The theory for them was initiated by R. Thom in 1950's, and have been deeply studied by many researchers.

This two-day workshop will bring together leading experts from academia, industry, and national laboratories to explore the rapidly evolving frontiers of quantum computing applications and their integration with high-performance computing (HPC) platforms.
Hosted by RIKEN Quantum, the event will provide a forum for discussing recent advances, practical challenges, and future directions toward achieving utility-scale quantum computations and robust quantum–HPC hybrid workflows.

The workshop is primarily an in-person event, but a special session on quantum computing in chemistry and life sciences will also be accessible via Zoom.

Currently there are hundreds of models describing inflation, a period of accelerated expansion in our universe. Each model lead to different imprints in cosmological observables, and for the purpose of testing the idea of inflation itself, it is essential to understand which predictions are model independent. This lead to the idea of cosmological bootstrap, a set of constraints from physical principles and symmetries alone.
In this talk I will give an overview on the cosmological bootstrap program. I will first explain how locality, unitarity and symmetry can constrain the kinematics of cosmological correlators. I will then talk about some recent progress on constructing positivity bounds on cosmology, which places constraints on the interactions of fields in inflation.

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.