168 citations · 241 across the 3 of their papers we have counts for
3 papers
Improved Boltzmann machines with error corrected quantum annealing
Richard Y. Li, Tameem Albash, Daniel A. Lidar
Boltzmann machines are the basis of several deep learning methods that have been successfully applied to both supervised and unsupervised machine learning tasks. These models assum…
Quantum processor-inspired machine learning in the biomedical sciences
Richard Y. Li, Sharvari Gujja, Sweta R. Bajaj +5
Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant mol…
Quantum annealing versus classical machine learning applied to a simplified computational biology problem
Richard Y. Li, Rosa Di Felice, Remo Rohs +1
Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective…