In a new demonstration, a U.S. researcher showcased that a quantum computer outperforms supercomputers in approximate optimization tasks.
The University of Southern California-led (USC) study demonstrated the first quantum scaling advantage for approximate optimization problem-solving using a quantum annealer.
Quantum annealing is a specific type of quantum computing that can use quantum physics principles to find high-quality solutions to difficult optimization problems. Rather than requiring exact optimal solutions, the study focused on finding solutions within a certain percentage (≥1%) of the optimal value, according to researchers.
Many real-world problems don’t require exact solutions, making this approach practically relevant. For example, it is often good enough to beat a leading market index in determining which stocks to put into a mutual fund rather than beating every other stock portfolio.
“The way quantum annealing works is by finding low-energy states in quantum systems, which correspond to optimal or near-optimal solutions to the problems being solved,” said Daniel Lidar, corresponding author of the study and professor of electrical and computer engineering, chemistry, and physics and astronomy at the USC Viterbi School of Engineering.
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