Supartha Podder, an assistant professor in the Department of Computer Sciences, has received a two-year, $400,000 grant from the Department of Energy (DOE) to study the power of quantum witnesses.
The grant is part of national $15 million initiative by the DOE to fund basic research to explore potentially high-impact approaches in scientific computing and extreme-scale science.
Podder studies quantum advantages in solving computational tasks; a witness is a piece of data that certifies the answer to a computation. Some problems are easy to verify once a little help regarding the solution is provided, like the sudoku puzzle, and a witness can be thought of as such help.
Quantum computation is a type of computational method that uses quantum bits or q-bits and harnesses the phenomenon of quantum mechanics such as superposition, interference and entanglement to solve problems. Classical computing is the traditional way computer science was developed using binary numbers and is governed by classical Newtonian mechanics.
“My work looks to see if quantum computing is better than traditional computing types. We will do this by not only comparing quantum with classical in terms of standard resources such as time and space needed for computation but also in terms of broader and more abstract resources such as computational advice and witness,” Podder said. “Think of it as solving one piece of the bigger quantum advantage puzzle. The ultimate overall goal is to understand when and why quantum computation outperforms traditional classical computation.”
The research will examine quantum witnesses through new perspectives to explore and better understand quantum witnesses. To do this involves designing new quantum algorithms, proving optimality of classical witnesses and investigating many different quantum mechanical properties of quantum witnesses.
Podder hopes that this work will shed light on the mystery of quantum advantage, which can ultimately lead to having exponential quantum advantage for certain types of practical computational problems. If proven correct, such extreme-scale computing would ultimately save time, energy and space to solve many of the computational problems worldwide that modern computers have difficulty completing.