Since the 1990s, evidence has been growing that quantum computers should be able to solve a range of particularly complex computational problems, with applications in everything from supply chain management to medicine and beyond.
A new study published this week in Quantum Science and Technology provides a novel blueprint for achieving this, detailing a deep new understanding of how a problem's intrinsic difficulty dictates the quantum computer's ultimate speed—and how we might learn to push that speed to its limit.
"Some mathematical problems are easy. Some mathematical problems are hard, but what makes a problem easy or hard?" asks Achim Kempf, Dieter Schwarz Chair in the Physics of Information and AI at the University of Waterloo and Associate Member at Perimeter Institute.
"It turns out, when you put a problem on a quantum computer, the problem's complexity translates into a need for quantum entanglement. The harder the question, the more complex the entanglement needs to be.
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