Statistical Computing Meets Quantum Computing

• 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
• 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

Abstract

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.