Quantum Algorithms, Machine Learning, and Foundations
ORAL · VIR-G01 · ID: VIR-G01
Presentations
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Nonlinear Regression Versus Maximum Likelihood Estimation for State Preparation and Measurement Tomography
Oral-Virtual
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Presenters
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Colton Mikes
Authors
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Christopher Purdy
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Matthew Jankowski
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Colton Mikes
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Austin Thomas
- Booz Allen Hamilton Inc.
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Shawn Wilder
- Booz Allen
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Weak values and multivariate traces: Structure and efficient estimation
Oral-Virtual
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Publication: G. Chiribella, K. Simonov, and X. Zhao, Phys. Rev. Research 6, 043043 (2024).
K. Simonov, R. Wagner, and E. Galvão, arXiv:2505.20208 (2025), submitted to Phys. Rev. A.Presenters
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Kyrylo Simonov
- University of Vienna
Authors
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Kyrylo Simonov
- University of Vienna
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Non-Clifford Gates via Non-Abelian Topological Orders
Oral-Virtual
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Publication: Title: Hybrid Lattice Surgery: Non-Clifford Gates via Non-Abelian Surface Codes
Authors: Sheng-Jie Huang; Alison Warman; Sakura Schafer-Nameki; Yanzhu Chen
arXiv:2510.20890Presenters
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Alison Warman
- University of Oxford
Authors
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Alison Warman
- University of Oxford
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Sheng-Jie Huang
- Max Planck Institute for the Physics of Complex Systems
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Sakura Schafer-Nameki
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Yanzhu Chen
- Florida State University
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Two Fundamental Forms of Quantum Optical Entanglement: Their Relationships, and Their Equivalences in Certain Measurement Contexts
Oral-Virtual
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Publication: From Wavefunctional Entanglement to Entangled Wavefunctional Degrees of Freedom by Aniruddha Bhattacharya.
Current Status: With author, after first round of peer-review; a revised manuscript will be re-submitted, shortly. Journal: Physical Review A. arXiv preprint: https://arxiv.org/abs/2507.04650
Presenters
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Aniruddha Bhattacharya
- Georgia Institute of Technology
Authors
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Aniruddha Bhattacharya
- Georgia Institute of Technology
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Variational Quantum Annealing for Quantum Chemistry
Oral-Virtual
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Publication: Variational Quantum Annealing for Quantum Chemistry (arXiv:2503.15473)
Presenters
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Ka Wa Yip
- Northeastern University
Authors
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Ka Wa Yip
- Northeastern University
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Kubra Yeter-Aydeniz
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Sijia Dong
- Northeastern University
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Clasificador cuántico geométrico informado por gráficos (GGQC) para la predicción de la enfermedad de Alzheimer
Oral-Virtual · Withdrawn
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Publication: Miller, JA., Guillozet-Bongaarts, A., Gibbons, LE., et al. (2017). "Neuropathological and transcriptomic characteristics of the aged brain." eLife 6:e31126.
DOI: https://doi.org/10.7554/eLife.31126
Allen Institute for Brain Science (2016). "Aging, Dementia and Traumatic Brain Injury Study Documentation."URL: https://aging.brain-map.org/
ANÁLISIS BASELINE Y SELECCIÓN DE GENES
RNA-Seq Preprocessing y Control de Calidad
3. Love, MI., Huber, W., Anders, S. (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome Biology 15:550.
DOI: https://doi.org/10.1186/s13059-014-0550-8
Risso, D., Ngai, J., Speed, TP., Dudoit, S. (2014). "Normalization of RNA-seq data using factor analysis of control genes or samples." Nature Biotechnology 32:896–902.
DOI: https://doi.org/10.1038/nbt.2931
CONSTRUCCIÓN DE REDES DE CO-EXPRESIÓN GÉNICA
Langfelder, P., Horvath, S. (2008). "WGCNA: an R package for weighted correlation network analysis." BMC Bioinformatics 9:559.
DOI: https://doi.org/10.1186/1471-2105-9-559
Miller, JA., Oldham, MC., Geschwind, DH. (2008). "A Systems Level Analysis of Transcriptional Changes in Alzheimer's Disease and Normal Aging." Journal of Neuroscience 28(6):1410–1420.
Oldham, MC., Konopka, G., Iwamoto, K., et al. (2008). "Functional organization of the transcriptome in human brain." Nature Neuroscience 11:1271–1282.
DOI: https://doi.org/10.1038/nn.2207
GRAPH NEURAL NETWORKS (GNNs) EN GENÓMICA
Zhang, Xiao-Meng and Liang, Li and Liu, Lin and Tang, Ming-Jing (2021). "Graph Neural Networks and Their Current Applications in Bioinformatics." Frontiers in Genetics 12:690049.
DOI: https://doi.org/10.3389/fgene.2021.690049
QUANTUM MACHINE LEARNING (Componente Cuántico)
Schuld, M., Killoran, N. (2019). "Quantum Machine Learning in Feature Hilbert Spaces." Physical Review Letters 122:040504.
DOI: https://doi.org/10.1103/PhysRevLett.122.040504
Huang, HY., Broughton, M., Mohseni, M., et al. (2021). "Power of data in quantum machine learning." Nature Communications 12:2631.
DOI: https://doi.org/10.1038/s41467-021-22539-9
INTERPRETABILIDAD Y DESCUBRIMIENTO DE BIOMARCADORES
Lundberg, S. M., & Lee, S. (2017). A unified approach to interpreting model predictions. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1705.07874Presenters
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David Castillo Salazar
- Universidad Tecnológica Indoamérica (UTI)
Authors
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David Castillo Salazar
- Universidad Tecnológica Indoamérica (UTI)
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Saravana Prakash Thirumuruganandham
- sit.health
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Enhancing Intrusion Detection Systems through Generative Dataset Balancing with Quantum Boltzmann Machines
Oral-Virtual
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Publication: 1. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=cDOs5BMAAAAJ&sortby=pubdate&citation_for_view=cDOs5BMAAAAJ:ufrVoPGSRksC
2. The initial work was presented at NGISE conference with limited resultsPresenters
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Arati Sahoo
- Unisys India Pvt. Ltd
Authors
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Salvatore Sinno
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Markus Bertl
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Arati Sahoo
- Unisys India Pvt. Ltd
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Bhavika Bhalgamiya
- Unisys
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Thomas Groß
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Nicholas Chancellor
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Arati Sahoo
- Unisys India Pvt. Ltd
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Fixing Divergence in Carleman Linearization via Analytical Continuation
Oral-In-person
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Publication: Higuchi, H., Tseng, S.-Y., Tsutsui, S., Sueishi, N., Iwakiri, H., Zhu, M., "Fixing Divergence in Carleman Linearization via Analytical Continuation," manuscript in preparation, QunaSys Inc. (2025).
Presenters
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Mingshuo Zhu
- QunaSys Inc.
Authors
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Mingshuo Zhu
- QunaSys Inc.
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Hayato Higuchi
- QunaSys Inc.
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Shoichiro Tsutsui
- QunaSys Inc.
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hokuto iwakiri
- QunaSys Inc.
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Shih-Yen Tseng
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Naohisa Sueishi
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Enabling Quantum Machine Learning Simulations
Oral-In-person
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Presenters
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Jovana Gojkovic
- University of Queensland
Authors
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Jovana Gojkovic
- University of Queensland
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