{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Programming Tech Brief By HackerNoon","title":"Load Balancing For High Performance Computing\nUsing Quantum Annealing: Grid Based Application","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/47bd891d\"></iframe>","width":"100%","height":180,"duration":720,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/load-balancing-for-high-performance-computing-using-quantum-annealing-grid-based-application.\nExploring quantum annealing's efficacy in load balancing for high-performance computing with grid-based and off-grid simulations on quantum hardware.\nCheck more stories related to programming at: https://hackernoon.com/c/programming.\n            You can also check exclusive content about #load-balancing, #high-performance-computing, #quantum-annealing, #grid-based-simulation, #off-grid-simulation, #computational-physics, #exascale-computing, #parallel-computing,  and more.\nThis story was written by: @loadbalancing. Learn more about this writer by checking @loadbalancing's about page,\n            and for more stories, please visit hackernoon.com.\nQuantum annealing (QA) was used to partition a grid using a round robin protocol. QA and SA appear to surpass SD when computational work is distributed across a higher number of processors. A distinct “kink” is observed when partitioning across four processors. This kink indicates a drop in solution quality compared to the results achieved with Simulated Annealing (SA) and Steepest Descent (SD)","thumbnail_url":"https://img.transistorcdn.com/KhCapPSRkLGL2Xw8888yuChkNRWthaKapLYTvNdu4W4/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMTY2LzE2ODM1/ODIzMzAtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}