Summary of Quantum Theory and Application Of Contextual Optimal Transport, by Nicola Mariella et al.
Quantum Theory and Application of Contextual Optimal Transport
by Nicola Mariella, Albert Akhriev, Francesco Tacchino, Christa Zoufal, Juan Carlos Gonzalez-Espitia, Benedek Harsanyi, Eugene Koskin, Ivano Tavernelli, Stefan Woerner, Marianna Rapsomaniki, Sergiy Zhuk, Jannis Born
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET); Quantum Algebra (math.QA); Quantitative Methods (q-bio.QM); Quantum Physics (quant-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel quantum computing formulation for optimal transport (OT) in machine learning. The authors explore the connection between doubly stochastic matrices and unitary operators, allowing them to develop QontOT, an amortized optimization method for contextualized transportation plans. This approach is verified on synthetic and real data, demonstrating its ability to predict variations in cell type distributions conditioned on drug dosage. Notably, a 24-qubit hardware experiment shows that QontOT outperforms classical neural OT methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses quantum computers to help machines learn how things move from one place to another, based on information about those places. It’s like trying to predict where people will go next, but instead of people, it’s cells in the body. The researchers found a way to use special math and computer chips to make this prediction much faster and better than usual computers. |
Keywords
* Artificial intelligence * Machine learning * Optimization
