John Hopkins APL Tests 32-Year-Old Algorithm for Quantum Computing

Researchers from Johns Hopkins University Applied Physics Laboratory have tested a 32-year-old scientific approach for use in quantum computing, a data processing method that breaks down information into quantum bits for faster processing.

Physicist Greg Quiroz leads a team that demonstrated the use of a “simultaneous perturbation stochastic approximation” algorithm to boost the accuracy of quantum computing, JHU APL said Wednesday. Using modern processors, the team tested the method by conducting a numerical simulation.

“Here, we present a method that utilizes feedback from hardware to determine the correct control protocol to perform the correct quantum operation and mitigate noise; thus, improving the computational accuracy of the device,” Quiroz said.

James Spall, who was then a statistician at JHU APL, developed the algorithm in 1987 for use with a wide variety of hardware systems available during that era. Spall is now a member of JHU APL’s principal professional staff.

Dave Clader, experimental and computational physics group supervisor at JHU APL, said the effort demonstrates how concepts tailored for microelectronics and robotics also hold applications in quantum science.