Speaker:
Yuta Kikuchi

Title:
Simulating Floquet scrambling circuits on trapped-ion quantum computers

Abstract:
Complex quantum many-body dynamics spread initially localized quantum information across the entire system. Information scrambling refers to such a process, whose simulation is one of the promising applications of quantum computing. We demonstrate the Hayden-Preskill recovery protocol and the interferometric protocol for calculating out-of-time-ordered correlators to study the scrambling property of a one-dimensional kicked-Ising model on 20-qubit trapped-ion quantum processors. The simulated quantum circuits have a geometrically local structure that exhibits the ballistic growth of entanglement, resulting in the circuit depth being linear in the number of qubits for the entire state to be scrambled. We experimentally confirm the growth of signals in the Hayden-Preskill recovery protocol and the decay of out-of-time-ordered correlators at late times. As an application of the created scrambling circuits, we also experimentally demonstrate the calculation of the microcanonical expectation values of local operators adopting the idea of thermal pure quantum states.

Speaker:
Ermal Rrapaj

Title:
Exact block encoding of imaginary time evolution with universal quantum neural networks

Abstract:
Quantum computers have been widely speculated to offer significant advantages in obtaining the ground state of difficult Hamiltonian in chemistry and physics. The imaginary-time evolution method is a well-known approach used for obtaining the ground state in quantum many-body problems on a classical computer. In this work we develop a practical method for such purpose. We develop a constructive approach to generate quantum neural networks capable of representing the exact thermal states of all many-body qubit Hamiltonians. The Trotter expansion of the imaginary-time propagator is implemented through an exact block encoding by means of a unitary, restricted Boltzmann machine architecture. Marginalization over the hidden-layer neurons (auxiliary qubits) creates the non-unitary action on the visible layer. Then, we introduce a unitary deep Boltzmann machine architecture, in which the hidden-layer qubits are allowed to couple laterally to other hidden qubits. We prove that this wave function ansatz is closed under the action of the imaginary-time propagator and, more generally, can represent the action of a universal set of quantum gate operations. We provide analytic expressions for the coefficients for both architectures, thus enabling exact network representations of thermal states without stochastic optimization of the network parameters. In the limit of large imaginary time, the ansatz yields the ground state of the system. The number of qubits grows linearly with the system size and total imaginary time for a fixed interaction order. Both networks can be readily implemented on quantum hardware via mid-circuit measurements of auxiliary qubits. If only one auxiliary qubit is measured and reset, the circuit depth scales linearly with imaginary time and system size, while the width is constant. Alternatively, one can employ a number of auxiliary qubits linearly proportional to the system size, and circuit depth grows linearly with imaginary time only.

Disease is caused by genetic factors and environmental factors. Genome-wide association study (GWAS) is a powerful method to find genetic factors associated with disease and human complex traits. One conceptual finding GWAS revealed is that many common diseases are caused by a combination of multiple genetic factors (polygenic), rather than a single causal mutation (monogenic). I have been working on finding genetic factors causing polygenic diseases by developing software that accurately finds sequence insertions and deletions from human population-scale sequencing datasets. In this talk, first, I will introduce some examples of disease-causing variants we recently discovered. Next I will also introduce my current research theme aiming to untangle how multiple genetic factors coordinately change cellular homeostasis, which I would like to have a collaboration with mathematical scientists.

Spin transport phenomena at strongly-correlated interfaces play central roles in fundamental physics as well as spintronic applications. Although the spin-flip tunneling process, a key mechanism of spin transport, has been extensively studied in solid-state systems, its behavior in itinerant Fermi gases remains elusive.
In this regard we study the spin tunneling in a repulsively interacting ultracold Fermi gas based on the conventional quasiparticle tunneling process. we investigate the spin current induced by quasiparticle and spin-flip tunneling processes to see their bias dependence and interaction dependence. To anatomize spin carriers, we propose the detection of the spin current noise in the system. The Fano factor, which is defined as the ratio between the spin current and its noise can serve as a probe of elementary carriers of spin transport. The change of the Fano factor microscopically evinces a crossover from the quasiparticle transport to magnon transport in itinerant fermionic systems.

(This is a joint iTHEMS Biology Seminar)
From social animals to neuronal networks, collective behavior is ubiquitous in living systems. How are these behaviors encoded in interactions, and how do they drive biological functions? Recent insights from statistical physics applied to biological data have offer exciting new perspectives. However, previous research has mostly focused on the statics, i.e. the steady-state distributions of the collective behavior, without taking into consideration of time. In this talk, I will present two recent progresses tapping into the temporal domain. First, I will present a study of collective behavior in social mice from their co-localization patterns. To capture both static and dynamic features of the data, we developed a novel inference method termed the generalized Glauber dynamics (GGD) that can tune the dynamics while keeping the steady state distribution fixed. I will first outline the explanation power of the GGD dynamics, then explain how to infer the dynamics from data. The inferred interactions characterize sociability for different mice strains. In the second example, we studied information flow among neurons in the larval zebrafish hindbrain. By adapting the method of Granger causality to single cell calcium transient data, we were able to detect both a global information flow among neurons, as well as identifying brain regions that are key in locomotion.

I will start my talk with a brief overview of the standard reheating scenario. Then, I will discuss reheating through the evaporation of primordial black holes (PBHs) if one assumes PBHs are formed during the phase of reheating. Depending on their initial mass, abundance, and inflaton coupling with the radiation, I discuss two physically distinct possibilities of reheating the universe. In one possibility, the thermal bath is solely obtained from the decay of PBHs, while inflaton plays the role of the dominant energy component in the entire process. In the other possibility, PBHs dominate the total energy budget of the universe during evolution, and then their subsequent evaporation leads to a radiation-dominated universe. Furthermore, I will discuss the impact of both monochromatic and extended PBH mass functions and estimate the detailed parameter ranges for which those distinct reheating histories are realized. The evaporation of PBHs is also responsible for the production of DM. I will show its parameters in the background of reheating obtained from two chief systems in the early universe: the inflaton and the primordial black holes (PBHs). Then, I will move my discussion towards stable PBHs and discuss the effects of the parameters describing the epoch of reheating on the abundance of PBHs and the fraction of cold dark matter that can be composed of PBHs. If PBHs are produced due to the enhancement of the primordial scalar power spectrum on small scales, such primordial spectra also inevitably lead to strong amplification of the scalar-induced secondary gravitational waves (GWs) at higher frequencies. I will show how the recent detection of the stochastic gravitational wave background (SGWB) by the pulsar timing arrays (PTAs) has opened up the possibility of directly probing the very early universe through the scalar-induced secondary gravitational waves. Finally, I will conclude my talk by elaborating on the effect of quantum correction on the Hawking radiation for ultra-light PBHs and its observational signature through dark matter and gravitational waves.

14:45-15:00 Teatime discussion
15:00-16:00 Talk by Prof. Satoshi Taguchi (Professor, Division of Earth and Planetary Sciences, Graduate School of Science, Kyoto University)
16:15-17:15 Talk by Prof. Yoshihiro Morishita (Professor, Division of Biological Sciences, Graduate School of Science, Kyoto University)
17:15-18:00 Discussion

Humans and other organisms develop collective behaviors through interactions with diverse environments and various species. These behaviors are significant topics across multiple research fields, including evolutionary biology, behavioral ecology, and animal sociology. Unraveling the decision-making mechanisms of individuals in groups within cooperative and competitive contexts has captured the attention of many researchers but remains a complex challenge. This seminar will present research cases that employ multi-agent reinforcement learning, a machine learning technique, to investigate the decision-making processes underlying collective behavior. Through this approach, we aim to provide deeper insights into the dynamics and mechanisms that drive group behaviors in various biological systems.