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Calendar of Physics Talks Vienna
| Quantum-inspired classification via efficient simulation of Helstrom measurement |
| Speaker: | Carsten Blank ( Data Cybernetics) |
| Abstract: | The Helstrom measurement (HM) is known to be the optimal strategy for distinguishing non-orthogonal quantum states with minimum error. Previously, a binary classifier based on classical simulation of the HM has been proposed. It was observed that using multiple copies of the sample data reduced the classification error. Nevertheless, the exponential growth in simulation runtime hindered a comprehensive investigation of the relationship between the number of copies and classification performance. We present an efficient simulation method for an arbitrary number of copies by utilizing the relationship between HM and state fidelity. Our method reveals that the classification performance does not improve monotonically with the number of data copies. Instead, it needs to be treated as a hyperparameter subject to optimization, achievable only through the method proposed in this work. |
| Date: | Tue, 09.07.2024 |
| Time: | 11:00 |
| Location: | Atominstitut, Seminarraum ZE, 1. Stock, Stadionallee 2, 1020 Wien |
| Contact: | Nayeli Azucena Rodriguez Briones |
| Space-time curvature-induced corrections to Rytov's law in optical fibers |
| Speaker: | Mario Hudelist (University of Vienna) |
| Abstract: | According to Rytov's law, the polarization vector of light follows a Fermi-Walker transport equation in optical fibers. Recent advancements in theory propose a modification to Rytov's law due to fiber bending. The aim of this talk is to further extend these predictions from flat to curved space-time. This involves perturbatively solving Maxwell's equations under the assumption that the wavelength is significantly shorter than the fiber's radius of curvature, as well as the characteristic length-scales of the ambient space-time. This results in a coupling of the polarization vector to the spatial Riemann curvature tensor and second derivatives of the lapse function.
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| Date: | Tue, 09.07.2024 |
| Time: | 12:30 |
| Duration: | 60 min |
| Location: | Library, Waehringer Strasse 17, 2nd Floor |
| Contact: | Piotr T. Chrusciel |
| Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning |
| Speaker: | Daniel K. Park (Yonsei University, Seoul, South Korea ) |
| Abstract: | Quantum embedding is a fundamental prerequisite for applying quantum machine learning techniques to classical data, and has substantial impacts on performance outcomes. In this study, we present Neural Quantum Embedding (NQE), a method that efficiently optimizes quantum embedding beyond the limitations of positive and trace-preserving maps by leveraging classical deep learning techniques. NQE enhances the lower bound of the empirical risk, leading to substantial improvements in classification performance. Moreover, NQE improves robustness against noise. To validate the effectiveness of NQE, we conduct experiments on IBM quantum devices for image data classification, resulting in a remarkable accuracy enhancement from 0.52 to 0.96. In addition, numerical analyses highlight that NQE simultaneously improves the trainability and generalization performance of quantum neural networks, as well |
| Date: | Wed, 10.07.2024 |
| Time: | 11:00 |
| Location: | Atominstitut, Seminarraum ZE, 1. Stock, Stadionallee 2, 1020 Wien |
| Contact: | Nayeli Azucena Rodriguez Briones |
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