Analyzing CITE-seq Data via a Quantum Algorithm

Music City Center 

Quantum advantage has been demonstrated in physics-oriented problems. It remains elusive whether quantum advantage can be established for modern computational biology problems. In this talk, I will introduce a new quantum machine learning algorithm for analyzing single-cell multi-omics data. The proposed algorithm takes advantage of quantum parallelism to enable fast computation. Theoretical results are derived to show the advantages of the proposed algorithm in terms of estimation error and computational complexity. Simulation suggests that our algorithm is effective in a wide range of settings.

single-cell experiments

quantum computing

model selection

Grover's algorithm

quantum counting

bioinformatics