Scientists observe quantum speed-up in optimization problems —

A collaboration between Harvard College with scientists at QuEra Computing, MIT, College of Innsbruck and different establishments has demonstrated a breakthrough software of neutral-atom quantum processors to unravel issues of sensible use.

The research was co-led by Mikhail Lukin, the George Vasmer Leverett Professor of Physics at Harvard and co-director of the Harvard Quantum Initiative, Markus Greiner, George Vasmer Leverett Professor of Physics, and Vladan Vuletic, Lester Wolfe Professor of Physics at MIT. Titled “Quantum Optimization of Most Unbiased Set utilizing Rydberg Atom Arrays,” was revealed on Could 5th, 2022, in Science Journal.

Beforehand, neutral-atom quantum processors had been proposed to effectively encode sure laborious combinatorial optimization issues. On this landmark publication, the authors not solely deploy the primary implementation of environment friendly quantum optimization on an actual quantum laptop, but additionally showcase unprecedented quantum {hardware} energy.

The calculations have been carried out on Harvard’s quantum processor of 289 qubits working within the analog mode, with efficient circuit depths as much as 32. In contrast to in earlier examples of quantum optimization, the massive system dimension and circuit depth used on this work made it inconceivable to make use of classical simulations to pre-optimize the management parameters. A quantum-classical hybrid algorithm needed to be deployed in a closed loop, with direct, automated suggestions to the quantum processor.

This mixture of system dimension, circuit depth, and excellent quantum management culminated in a quantum leap: drawback situations have been discovered with empirically better-than-expected efficiency on the quantum processor versus classical heuristics. Characterizing the problem of the optimization drawback situations with a “hardness parameter,” the crew recognized circumstances that challenged classical computer systems, however that have been extra effectively solved with the neutral-atom quantum processor. An excellent-linear quantum speed-up was discovered in comparison with a category of generic classical algorithms. QuEra’s open-source packages GenericTensorNetworks.jl and Bloqade.jl have been instrumental in discovering laborious situations and understanding quantum efficiency.

“A deep understanding of the underlying physics of the quantum algorithm in addition to the elemental limitations of its classical counterpart allowed us to comprehend methods for the quantum machine to realize a speedup,” says Madelyn Cain, Harvard graduate scholar and one of many lead authors. The significance of match-making between drawback and quantum {hardware} is central to this work: “Within the close to future, to extract as a lot quantum energy as doable, it’s crucial to determine issues that may be natively mapped to the particular quantum structure, with little to no overhead,” mentioned Shengtao Wang, Senior Scientist at QuEra Computing and one of many coinventors of the quantum algorithms used on this work, “and we achieved precisely that on this demonstration.”

The “most impartial set” drawback, solved by the crew, is a paradigmatic laborious process in laptop science and has broad purposes in logistics, community design, finance, and extra. The identification of classically difficult drawback situations with quantum-accelerated options paves the trail for making use of quantum computing to cater to real-world industrial and social wants.

“These outcomes characterize step one in direction of bringing helpful quantum benefit to laborious optimization issues related to a number of industries.,” added Alex Keesling CEO of QuEra Computing and co-author on the revealed work. “We’re very completely satisfied to see quantum computing begin to attain the required degree of maturity the place the {hardware} can inform the event of algorithms past what might be predicted upfront with classical compute strategies. Furthermore, the presence of a quantum speedup for laborious drawback situations is extraordinarily encouraging. These outcomes assist us develop higher algorithms and extra superior {hardware} to sort out a few of the hardest, most related computational issues.”

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