The study was led by Mikhail Lukin, George VasmerLeverett Harvard Physics Professor and Harvard Quantum Initiative director Markus Greiner, George VasmerLeverett Physics Professor, and VladanVuletic, Lester Wolfe Professor of Physics at MIT. Entitled “Quantum Optimization of Maximum Independent Set using Rydberg Atom Arrays,” it was published May 5, 2022, in the journal Science.
Previously, neutral atomic quantum atomic processors had been proposed to successfully document specific complex assembly problems. In this historic edition, the authors not only sent the first implementation of quantum efficiency to a real quantum computer, but also demonstrated the unprecedented power of quantum hardware.
QUANTUM
The calculations were performed on Harvard’s quantum processor of 289 qubits operating in analog mode, with an active circuit range of up to 32. Unlike previous examples of quantum design, the large system size and circuit depth used in this work make it impossible to use classical. simulation to pre-configure control parameters. The quantum-classical hybrid algorithm was to be used in a closed loop, with a straight, automatic response to the quantum processor.
This combination of system size, circuit depth, and outstanding quantum control has reached a climax with quantum jumps: problem situations have been achieved with much better performance than expected with a quantum processor against traditional heuristics. Notable for the complexity of the configuration problems with the “hardness parameter,” the team identified cases that challenged older computers, but were best resolved with a neutral-atom quantum processor. The super-linear quantum speedometer was detected compared to the standard algorithms category.
“An in-depth understanding of the basic physics of quantum algorithm and the basic limitations of our classmates has allowed us to recognize quantum machine methods to achieve speed,” said Madelyn Cain, a Harvard graduate student and one of the leading authors. . The importance of balancing the problem with quantum hardware is fundamental to this work: “In the near future, in order to extract as much quantum energy as possible, it is important to identify potential problems mapping a specific quantum architecture area, small or insignificant,” said Shengtao Wang, Scientist A senior at QuEra Computing and one of the creators of the quantum algorithms used in the project, “and we achieved just that in this show.”
The “high set” problem, solved by the team, is a difficult paradigmatic task in computer science and has extensive applications in transportation, network design, finance, and more. The identification of historical challenges with quantum accelerated solutions opens the way to use quantum computing to meet the needs of the real industrial and social world.
“These results represent the first step in bringing quantum profitability to quantum performance problems associated with multiple industries,” added Alex Keesling, CEO of QuEra Computing and co-author of a published work. “We are very pleased to see quantum computing begin to reach the required level of maturity where hardware can inform the development of algorithms beyond what can be predicted by traditional computing methods. In addition, the existence of quantum speedup for complex problems is very encouraging. These results help us we have developed better algorithms and more sophisticated computer systems to deal with more complex, more complex computer problems. “
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