MSGID: 1:317/3 6274a4d9   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Scientists observe quantum speed-up in optimization problems    
      
     Date:   
         May 5, 2022   
     Source:   
         Harvard University   
     Summary:   
         Scientists have demonstrated a breakthrough application of   
         neutral-atom quantum processors to solve problems of practical use.   
      
      
      
   FULL STORY   
   ==========================================================================   
   A collaboration between Harvard University with scientists at QuEra   
   Computing, MIT, University of Innsbruck and other institutions has   
   demonstrated a breakthrough application of neutral-atom quantum processors   
   to solve problems of practical use.   
      
      
   ==========================================================================   
   The study 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 Maximum Independent Set using Rydberg Atom   
   Arrays," was published on May 5th, 2022, in Science Magazine.   
      
   Previously, neutral-atom quantum processors had been proposed to   
   efficiently encode certain hard combinatorial optimization problems. In   
   this landmark publication, the authors not only deploy the first   
   implementation of efficient quantum optimization on a real quantum   
   computer, but also showcase unprecedented quantum hardware power.   
      
   The calculations were performed on Harvard's quantum processor of 289   
   qubits operating in the analog mode, with effective circuit depths up   
   to 32. Unlike in previous examples of quantum optimization, the large   
   system size and circuit depth used in this work made it impossible to   
   use classical simulations to pre- optimize the control parameters. A   
   quantum-classical hybrid algorithm had to be deployed in a closed loop,   
   with direct, automated feedback to the quantum processor.   
      
   This combination of system size, circuit depth, and outstanding   
   quantum control culminated in a quantum leap: problem instances were   
   found with empirically better-than-expected performance on the quantum   
   processor versus classical heuristics. Characterizing the difficulty   
   of the optimization problem instances with a "hardness parameter,"   
   the team identified cases that challenged classical computers, but   
   that were more efficiently solved with the neutral- atom quantum   
   processor. A super-linear quantum speed-up was found compared to a   
   class of generic classical algorithms. QuEra's open-source packages   
   GenericTensorNetworks.jl and Bloqade.jl were instrumental in discovering   
   hard instances and understanding quantum performance.   
      
   "A deep understanding of the underlying physics of the quantum algorithm   
   as well as the fundamental limitations of its classical counterpart   
   allowed us to realize ways for the quantum machine to achieve a   
   speedup," says Madelyn Cain, Harvard graduate student and one of the   
   lead authors. The importance of match- making between problem and quantum   
   hardware is central to this work: "In the near future, to extract as much   
   quantum power as possible, it is critical to identify problems that can   
   be natively mapped to the specific quantum architecture, with little to   
   no overhead," said Shengtao Wang, Senior Scientist at QuEra Computing and   
   one of the coinventors of the quantum algorithms used in this work, "and   
   we achieved exactly that in this demonstration."  The "maximum independent   
   set" problem, solved by the team, is a paradigmatic hard task in computer   
   science and has broad applications in logistics, network design, finance,   
   and more. The identification of classically challenging problem instances   
   with quantum-accelerated solutions paves the path for applying quantum   
   computing to cater to real-world industrial and social needs.   
      
   "These results represent the first step towards bringing useful quantum   
   advantage to hard optimization problems relevant to multiple industries.,"   
   added Alex Keesling CEO of QuEra Computing and co-author on the published   
   work.   
      
   "We are very happy to see quantum computing start to reach the necessary   
   level of maturity where the hardware can inform the development of   
   algorithms beyond what can be predicted in advance with classical compute   
   methods. Moreover, the presence of a quantum speedup for hard problem   
   instances is extremely encouraging. These results help us develop better   
   algorithms and more advanced hardware to tackle some of the hardest,   
   most relevant computational problems."   
      
   ==========================================================================   
   Story Source: Materials provided by Harvard_University. Note: Content   
   may be edited for style and length.   
      
      
   ==========================================================================   
   Journal Reference:   
      1. S. Ebadi, A. Keesling, M. Cain, T. T. Wang, H. Levine, D. Bluvstein,   
      G.   
      
         Semeghini, A. Omran, J.-G. Liu, R. Samajdar, X.-Z. Luo,   
         B. Nash, X. Gao, B. Barak, E. Farhi, S. Sachdev, N. Gemelke,   
         L. Zhou, S. Choi, H. Pichler, S.-T. Wang, M. Greiner, V. Vuletic,   
         M. D. Lukin. Quantum optimization of maximum independent set using   
         Rydberg atom arrays. Science, 2022; DOI: 10.1126/science.abo6587   
   ==========================================================================   
      
   Link to news story:   
   https://www.sciencedaily.com/releases/2022/05/220505150340.htm   
      
   --- up 9 weeks, 3 days, 10 hours, 50 minutes   
    * Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)   
   SEEN-BY: 15/0 106/201 114/705 123/120 129/330 331 153/7715 218/700   
   SEEN-BY: 229/110 111 317 400 426 428 470 664 700 292/854 298/25 305/3   
   SEEN-BY: 317/3 320/219 396/45   
   PATH: 317/3 229/426