A computer science professor at Amherst College who recently devised and conducted experiments to test the speed of a quantum computing system against conventional computing methods will soon be presenting a paper with her verdict: quantum computing is, “in some cases, really, really fast.”
Dwave’s quantum computer system is capable of solving problems thousands of times faster than conventional computing methods can for some problems.
McGeoch, author of A Guide to Experimental Algorithmics, has 25 years of experience setting up experiments to test various facets of computing speed, and is one of the founders of “experimental algorithmics,” which she jokingly calls an “oddball niche” of computer science. Her specialty is, however, proving increasingly helpful in trying to evaluate different types of computing performance.
Her 10-page-paper, titled “Experimental Evaluation of an Adiabiatic Quantum System for Combinatorial Optimization,” was co-authored with Cong Wang, a graduate student at Simon Fraser University.
The calculations the D-Wave excels at involve a specific combinatorial optimization problem, comparable in difficulty to the more famous “travelling salesperson” problem that’s been a foundation of theoretical computing for decades. Questions like this apply to challenges such as shipping logistics, flight scheduling, search optimization, DNA analysis and encryption, and are extremely difficult to answer quickly.
She tested the 439-qubit model against a conventional computer.
The conventional computer was a Lenovo workstation with a 2.4GHz quad core Intel processor and 16GB RAM. A $10 million supercomputer should have about 500 teraflops of compute performance. However, the problem solution time may not be reduced by 1000 times by splitting the problem into parallel solution on the supercomputer.
In two years, a larger Dwave system should have a few thousand qubits and the system will be 500,000 times faster on some problems of this type. In two years conventional computers will be about 4 times faster.
On one problem (unconstrained binary optimization) well-matched to the hard-wired design of the machine’s super-cooled chip, it found the best result about 3,600 times more quickly than the best conventional software solver. It crossed the finish line in just under half a second, while the second finisher took 30 minutes.
ABSTRACT – This paper describes an experimental study of a novel computing system (algorithm plus platform) that carries out quantum annealing, a type of adiabatic quantum computation, to solve optimization problems. We compare this system to three conventional software solvers, using instances from three NP-hard problem domains. We also describe experiments to learn how performance of the quantum annealing algorithm depends on input.
“This type of computer is not intended for surfing the internet, but it does solve this narrow but important type of problem really, really fast,” McGeoch says. “There are degrees of what it can do. If you want it to solve the exact problem it’s built to solve, at the problem sizes I tested, it’s thousands of times faster than anything I’m aware of. If you want it to solve more general problems of that size, I would say it competes – it does as well as some of the best things I’ve looked at. At this point it’s merely above average but shows a promising scaling trajectory.”
And, while conventional approaches to solving these problems will likely continue to improve incrementally, this fast quantum approach has the potential to expand to larger variety of problems than it does now, McGeoch says.
“Within a year or two I think these quantum computing methods will solve more and bigger problems significantly faster than the best conventional computing options out there,” she says.
At the same time, she cautions that her first set of experiments represents a snapshot moment of the state of quantum computing versus conventional computing.
“This by no means settles the question of how fast the quantum computer is,” she says. “That’s going to take a lot more testing and a variety of experiments. It may not be a question that ever gets answered because there’s always going to be progress in both quantum and conventional computing.”
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.
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