Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 2 - Emerging Methods in Quantum Computing, Quantum Information, and Quantum Statistical Learning
Type: Invited
Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #320336
Title: Statistical Computing Meets Quantum Computing
Author(s): Wenxuan Zhong* and Yuan Ke and Ping Ma
Companies: University of Georgia and University of Georgia and University of Georgia
Keywords: variable selection ; quantum information; superposition; least squares; qubit; Grover's algorithm

With the rapid development of quantum computers, quantum computing has been studied extensively. Unlike electronic computers, a quantum computer operates on quantum processing units, or qubits, which can take values 0, 1, or both simultaneously due to the superposition property. The number of complex numbers required to characterize quantum states usually grows exponentially with the size of the system. For example, a quantum system with p qubits can be in any superposition of 2^p orthonormal states simultaneously, while a classical system can only be in one state at a time. Such a paradigm change has motivated significant developments of scalable quantum algorithms in many areas. However, quantum algorithms tackling statistical problems are still lacking.

In this talk, I will present challenges and opportunities for developing quantum algorithms. I will introduce a novel quantum algorithm for a statistical problem.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program