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For problems with up to 128 variables, the adiabatic times for the simulated processor architecture are about 4 and 6 orders (10,000 to 1 million times faster) of magnitude shorter than the two classical solvers’ times. This performance difference shows that, even in the potential absence of a scaling advantage, adiabatic quantum optimization may outperform classical solvers
High performance Quantum Monte Carlo simulations on a large-scale Internet-based computing platform were used to compare the median adiabatic times with the median running times of two classical solvers .
10 page paper – Investigating the Performance of an Adiabatic Quantum Optimization Processor
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Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.