Accelerating COVID-19 scientific discovery with hypothesis generation system

In 2020, the White House released the, “Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset,” wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. 

Ilya Tyagin, Ankit Kulshrestha, Justin Sybrandt, Krish Matta, Michael Shtutman, Ilya Safro "Accelerating COVID-19 research with graph mining and transformer-based learning", Innovative Applications of Artificial Intelligence (AAAI/IAAI), preprint at https://www.biorxiv.org/content/10.1101/2021.02.11.430789v1, 2022

We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and the transformer model. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone.

Transferring QAOA parameters from small instances to large

The Quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for  achieving quantum advantage through quantum-enhanced combinatorial optimization. However, finding optimal parameters to run QAOA is a hard problem that is usually tackled by variational loop, i.e., the circuit is executed with the current parameters on a quantum device, and then the parameters are updated on a classical device. Such variational loops are extremely time consuming. In our recent paper we find the QAOA parameters for small optimization instances and transfer them to the larger ones accelerating the entire process.

Alexey Galda, Xiaoyuan Liu, Danylo Lykov, Yuri Alexeev, Ilya Safro "Transferability of optimal QAOA parameters between random graphs", IEEE International Conference on Quantum Computing and Engineering (QCE), preprint at https://arxiv.org/pdf/2106.07531.pdf, 2021

In a typical  QAOA setup, a set of quantum circuit parameters is optimized to prepare a quantum state used to find the optimal solution of a combinatorial optimization problem. Several empirical observations about optimal parameter concentration effects for special QAOA MaxCut problem instances have been made in recent literature, however, a rigorous study of the subject is still lacking. We show that convergence of the optimal QAOA parameters around specific values and, consequently, successful transferability of parameters between different QAOA instances can be explained and predicted based on the local properties of the graphs, specifically the types of subgraphs (lightcones) from which the graphs are composed. We apply this approach to random regular and general random graphs. For example, we demonstrate how optimized parameters calculated for a 6-node random graph can be successfully used without modification as nearly optimal parameters for a 64-node random graph, with less than 1% reduction in approximation ratio as a result. This work presents a pathway to identifying classes of combinatorial optimization instances for which such variational quantum algorithms as QAOA can be substantially accelerated.

Congratulations to Dr. Joey!

Congratulations to Dr. Xiaoyuan Joey Liu for successfully defending her Ph.D. thesis "Combinatorial Optimization Algorithms on Quantum and Quantum-inspired Devices"! Joey will join quantum computing research group at Fujitsu Research USA.