This is a TJR-iTHEMS Joint Seminar supported by ASPIRE Program

ABSTRACT
Neutron stars were first posited in the early thirties, and discovered as pulsars in the late sixties; however we are only recently beginning to understand the matter they contain. I will describe the ongoing development of a consistent picture of the liquid interiors of neutron stars, now driven by ever increasing observations as well as theoretical advances. These include observations of heavy neutron stars of about 2.0 solar masses and higher; ongoing inferences of masses and radii by the NICER telescope; and observations of binary neutron star mergers, through gravitational waves as well as across the electromagnetic spectrum. Theoretically an understanding is emerging in QCD of how nuclear matter can turn into deconfined quark matter, which I will illustrate with modern quark-hadron crossover equations of state.

BRIEF BIO
Gordon Baym is a Professor of Physics at the University of Illinois. Educated at Cornell and Harvard, he spent two years at the Niels Bohr Institute. His interests range from matter under extreme conditions to ultracold atomic physics, astrophysics, and nuclear physics. A pioneer in the study of pulsars and neutron stars, he is a member of the U.S. National Academy of Sciences and received the APS Medal for Exceptional Achievement in Research, the Hans Bethe and Lars Onsager Prizes, and the Eugene Feenberg Memorial Medal.

We investigate the imprints on the angular power spectra of cosmological perturbations of a pre-inflationary bounce phase, as described by the hybrid and dressed metric approaches to loop quantum cosmology. For this purpose, we derive a new parametrization of the primordial power spectrum at the end of the inflationary regime. Apart from slow-roll coefficients and cosmological parameters that are present in the standard cosmological scenario without quantum modifications, this parametrization additionally depends only on pre-inflationary physics. More specifically, we find a dependence on the number of e-folds during the bounce epoch and on a characteristic suppression scale which, given the e-folds accumulated during cosmic evolution, is determined by the energy density at the bounce.

After repeatedly finding errors in experimental data provided by collaborators, my group developed an online tool (midSIN, https://midsin.roadcake.org/) to improve estimating the concentration of infectious viruses in samples. This led to an unexpected new collaboration with researchers working to measure the concentration of aggregating fibrils in samples from patients suffering from neurodegenerative diseases such as Dementia with Lewy Body and Parkinson's. In the first part of my talk, I will introduce the basics of how infectious virions and aggregating fibril concentrations are measured experimentally, and discuss challenges in tackling these assays' limitations to improve their accuracy and sensitivity. In the second part of my talk, I will discuss the challenges we face in trying to identify the type and minimal number of experimental measurements required to predict the severity and transmission efficacy of diverse influenza viruses collected as part of pandemic surveillance efforts. I hope you will join the talk to learn of these challenges and consider contributing new ideas or approaches to overcome them.

Deep generative models are key-enabling technology to computer vision, text generation, and large language models. Generative models for quantum data offer a promising route toward learning and preparing complex quantum-state ensembles. In this talk, I will introduce the quantum denoising diffusion probabilistic model (QuDDPM) [1], which adapts the diffusion-model idea to quantum systems through a forward randomization process and a trainable backward denoising dynamics. I will discuss how this framework enables stepwise learning of target quantum state ensembles and demonstrate its capabilities in various learning tasks. I will then present its extension to mixed states to eliminate the need for scrambling [2]. I will conclude with a brief discussion of recent results on scaling laws of quantum information lifetime in monitored quantum dynamics, emphasizing how mid-circuit measurements can maintain information and provide useful intuition for measurement-assisted quantum machine learning.

This is the self-introduction talk by Pradyot Pritam Sahoo. Pradyot is a Student Trainee in iTHEMS.

Groups need to make decisions, and there are a wide variety of ways this can be done, each maximizing different notions of fairness. Social Choice Theory provides a mathematical framework to investigate these possibilities rigorously. Infamous for its many impossibility results, this topic reveals some fundamental limits to democracy. Beyond this, we'll discuss potential resolutions to these problems, as well as their real world implications.

We'll look at signal detection in noisy data, and at Bayesian inference in astrophysical inverse problems. We'll look at the form these problems take in the context of gravitational-wave astronomy, but we'll focus on where attempts at solutions have gone wrong. The mistakes we make transcend disciplines, and hopefully by shining light on them others can be helped to avoid making them as well.

Mathematical Application Research Team is honored to invite Prof. Shin-ichi Ohta from the University of Osaka to this meeting. Everyone is welcome to join the meeting to listen to his seminar.

Title: Synthetic and comparison Lorentzian geometry

Abstract: In this talk we review recent developments of synthetic geometric approaches to Lorentzian geometry, motivated by the theory of less regular spacetimes in general relativity as well as comparison geometry in the Riemannian setting. Among others, optimal transport theory plays a vital role.

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)

[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.