First prize in the 2019 IBM Q Best Paper Award competition was awarded to graduate student Pranav Gokhale and fellow EPiQC researchers for work on increasing the efficiency of key quantum algorithms. The paper, “Minimizing State Preparations in Variational Quantum Eigensolver (VQE) by Partitioning into Commuting Families,” examined how to reduce measurements, one of the biggest overhead costs in VQE. VQE is a “killer app” for near-term quantum computing, primarily for finding the ground state energy of a molecule, an important and computationally difficult calculation, which consumes a significant fractions of world’s supercomputing resources. (Watch - Gokhale explain their research in this short video. )
With their approach, the authors reduced the computational cost of running the VQE algorithm by 7-12 times. They experimentally validated the approach on one of IBM’s cloud-service 20-qubit quantum computers. The authors have shared their Python and Qiskit code for generating circuits for simultaneous measurement. For more on the research and the IBM Q Best Paper Award, see the IBM Research Blog, as well as coverage by the Uchicago CS Department
IBM Q Best Paper Award for VQE Research
