Keynote Speakers

Prof. Silvano Donati
University of Pavia, Italy

Speech Title: Self-Mixing Interferometry: a Universal Yardstick to Measure Almost Everything
Abstract: In this talk, we describe the principle of operation of self-mixing interferometer, a coherent configuration for the measurement of dimensional and kinematic quantities such as: displacement, distance, vibration amplitude, thickness, angle, and curvature, and also physical quantities like: coupling factors, line width, alfa-factor, and index of refraction. In the measurement arrangement, the laser undergoes self-injection at weak level, leading to an amplitude and frequency modulation driven by external optical path length.  We will describe the development of a straightforward displacement-measuring instrument, based on up/down counting of fringes, and then we extend the measurement to the practical case of a diffuse (non-cooperative) target, introducing the speckle-pattern statistics, corrected by a BST (bright speckle tracking) technique, shown capable of covering displacements up to 1-m with mm-resolution and accuracy. For sub-mm vibrations, we will report on the implementation of two-channel (or, referenced) vibrometer, based on analogue processing of the self-mix signal, removing the speckle-related amplitude errors thanks to a servo-loop concept, so that the instrument is capable of true differential operation, and covers a dynamic range of amplitudes extending from a noise-limited minimum of 100 pm/√Hz to about 500 mm with good linearity. The vibrometer allows the non-contact measurement of the mechanical frequency response and, even more unusual, of the hysteresis cycle (or the stress/strain diagram) of a sample under test. Additional examples of a number of surprising measurements performed by self-mix will be finally surveyed, i.e., the absolute distance meter, the thickness, angles and return echoes measurements as well as that of laser parameters like linewidth and alpha-factor. An outlook on perspectives and future perspectives of self-mix will conclude the talk.

Biodata: Silvano Donati has been Full Professor of University of Pavia (Italy) since 1980 before becoming Emeritus in 2015. He has authored or co-authored 350+ papers and holds a dozen patents, and has written two books, ‘Photodetectors’ (2nd ed.: IEEE_Wiley 2021) and ‘Electro-Optical Instrumentation’ (2nd ed.: CRC 2023), covering the subject of his courses at University of Pavia and abroad. His main achievements have been self-mixing interferometry and chaos-shift-keying cryptography, the topics covered in his Distinguished Lecture talk given in 21 LEOS (now IPS) Chapters 2007-09 and continued as a Traveling Lecturer of OSA and SPIE on Self-Mixing and Lidars to date, covering a total 105 Chapters visited. He has received several awards from the AEIT and IEEE, in particular the Marconi medal, the Aaron Kressel Award and the Distinguished Service Award of the IEEE Photonics Society. He was the founder (1996) and first Chairman (1997-01) of the Italian LEOS Chapter, LEOS VP Region 8 Membership (2002-04) and BoG (2004-06), and the Chairman of the IEEE Italy Section (2008-09). He has spent semesters as Visiting Professor in several Universities of Taiwan: NTU in Taipei, 2005, Sun Yat Sen in Kaohsiung (2007, 2008, 2010), NCKU in Tainan, 2012, NCHU in Taichung, 2013-14, NTUT in Taipei 2015-16 NTU in Taipei 2018-19 and NTUST in Taipei 2023. Prof. Donati is Life Fellow of the IEEE, Optica Emeritus Fellow and SPIE Life Member.

 

Prof. Masaaki Fukasawa
The University of Osaka, Japan

Speech Title: Martingale expansion for stochastic volatility
Abstract: The martingale expansion provides a refined approximation to the marginal distributions of martingales beyond the normal approximation implied by the martingale central limit theorem. We develop a martingale expansion framework specifically suited to continuous stochastic volatility models. Our approach accommodates both small volatility-of-volatility and fast mean-reversion models, yielding first-order perturbation expansions under essentially minimal conditions.

Biodata: Masaaki Fukasawa is Professor at The University of Osaka, charing Stochastic Analysis Group at Graduate School of Engineering Science, since 2016. He has been Co-Editor of Finance and Stochastics since 2018. He has been working on Mathematical Finance and related issues, in particular, Asymptotic Analysis of financial stochastic models.

Invited Speaker

Prof. Hsi-Sheng Goan
National Taiwan University, Taiwan

Speech Title: Quantum Recurrent Unit: A Parameter-Efficient Quantum Neural Network Component
Abstract: The rapid growth of modern machine learning models presents fundamental challenges in parameter efficiency and computational resource requirements. This study introduces the Quantum Recurrent Unit (QRU), a novel quantum neural network architecture specifically designed to address these challenges while remaining compatible with Noisy Intermediate-Scale Quantum (NISQ) devices. QRU leverages quantum controlled-SWAP (C-SWAP, or Fredkin) gates to implement an information selection mechanism inspired by classical Gated Recurrent Units, enabling selective processing of temporal information through quantum operations. Through its innovative recurrent architecture featuring measurement results feedforward state propagation and shared parameters across time steps, QRU achieves constant circuit depth and constant parameter count regardless of input sequence length, effectively circumventing the stringent hardware constraints of NISQ computers. We systematically validate QRU's performance through three progressive experiments: oscillatory behavior prediction tasks, high-dimensional feature classification of the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, and MNIST handwritten digit classification. QRU uses only 72 parameters to match a 197-parameter Gated Recurrent Unit (GRU) in time series prediction, 35 parameters to achieve 96.13% accuracy equivalent to a 167-parameter Artificial Neural Network (ANN) in WDBC classification, and 132 parameters to reach 98.05% accuracy, outperforming a Convolutional Neural Network (CNN) using approximately 27,265 parameters in MNIST handwritten digit classification. These results demonstrate that QRU consistently achieves comparable or superior performance with significantly fewer parameters than classical neural networks, while maintaining constant quantum circuit depth regardless of input sequence length. The architecture's quantum-native design, combining C-SWAP-based information selection with recurrent processing, suggests the potential of QRU as a fundamental building block for next-generation machine learning systems, offering a promising pathway toward more efficient and scalable quantum machine learning architectures.

Biodata: Hsi-Sheng Goan received the Ph.D. degree in physics from the University of Maryland, College Park, USA, in 1999. He then worked as a Postdoctoral Research Fellow at the University of Queensland, Brisbane, Australia, from 1999 to 2001. From 2002 to 2004, he was a Senior Research Fellow at the Center for Quantum Computer Technology at the University of New South Wales, Sydney, Australia, and was awarded the Hewlett-Packard Fellowship. He then took up a faculty position in the Department of Physics at National Taiwan University (NTU) in 2005. He is currently a Professor of Physics at NTU, working in the fields of Quantum Computing and Quantum Information, Quantum Control, Mesoscopic (Nano) Physics, Quantum Optics, and Quantum Optomechanical and Electromechanical Systems. He has served on the Editorial Boards of several international scientific journals, including the EPJ: Quantum Technology, International Journal of Quantum Information, and Chinese Journal of Physics.