all AI news
Towards Quantum Advantage on Noisy Quantum Computers. (arXiv:2209.09371v2 [quant-ph] UPDATED)
Sept. 28, 2022, 1:13 a.m. | Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson, Mark S. Squillante, Vishnu Jejjala, Yang-Hui He, Kugendran Naidoo, Vasileios Kalantzis,
cs.LG updates on arXiv.org arxiv.org
Topological data analysis (TDA) is a powerful technique for extracting
complex and valuable shape-related summaries of high-dimensional data. However,
the computational demands of classical TDA algorithms are exorbitant, and
quickly become impractical for high-order characteristics. Quantum computing
promises exponential speedup for certain problems. Yet, many existing quantum
algorithms with notable asymptotic speedups require a degree of fault tolerance
that is currently unavailable. In this paper, we present NISQ-TDA, the first
fully implemented end-to-end quantum machine learning algorithm needing only a …
More from arxiv.org / cs.LG updates on arXiv.org
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
2 days, 11 hours ago |
arxiv.org
Bagging Provides Assumption-free Stability
2 days, 11 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Research Scientist, Demography and Survey Science, University Grad
@ Meta | Menlo Park, CA | New York City
Computer Vision Engineer, XR
@ Meta | Burlingame, CA