Research Attempts to Limit Measurements by Implementing Simultaneous Multi-Measurements
Research by PhD Student Pranav Gokhale and EPiQC Wins IBM Q Best Paper
Excerpts and salient points ~
+ A significant gap remains, however, between the capabilities of today’s quantum computers and the algorithms proposed by computational theorists. “VQE can perform some pretty complicated chemical simulations in just 1,000 or even 10,000 operations, which is good,” Gokhale says. “The downside is that VQE requires millions, even tens of millions, of measurements, which is what our research seeks to correct by exploring the possibility of doing multiple measurements simultaneously.”
+ With their approach, the authors reduced the computational cost of running the VQE algorithm by 7-12 times. When they validated the approach on one of IBM’s cloud-service 20-qubit quantum computers, they also found lower error as compared to traditional methods of solving the problem. The authors have shared their Python and Qiskit code for generating circuits for simultaneous measurement, and have already received numerous citations in the months since the paper was published.
+ A new approach for using a quantum computer to realize a near-term “killer app” for the technology received first prize in the 2019 IBM Q Best Paper Award competition, the company announced. The paper, “Minimizing State Preparations in Variational Quantum Eigensolver (VQE) by Partitioning into Commuting Families,” was authored by UChicago CS graduate student Pranav Gokhale and fellow researchers from the Enabling Practical-Scale Quantum Computing (EPiQC) team.
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