From the Dwave presentation at SC08 (Supercomputer conference), they indicated that the current 128 qubit adiabatic quantum computer has sub-PC performance but they project by next year to reach that level with whatever number of qubits are operational then. Assuming the time scale is roughly correct between the 2008 and 2009 points then it will take 4-5 years to reach supercomputer levels of performance and 6-7 years to exceed classical computer performance.
By November, 2009:
• Dwave Quantum computer Systems are targeted to be in top research centers, performance ~ PC
• Extremely compelling scientifically
From Geordie Rose, CTO of Dwave:
It’s not only about the number of qubits. There are a lot of other issues. The level of connectivity in the underlying hardware graph (the number of couplers), algorithms for mapping “real life” problems into hardware, documentation and debugging, increasing mean time between failure, decreasing costs, decreasing 1/f noise from materials science issues in fab, increasing fab yields, etc. etc. etc.
Recently techniques have been introduced for calculating the run time of the quantum adiabatic algorithms for problems up to about 128 variables; see here and here. While this is still too small to ultimately answer questions about the asymptotic scaling of these approaches, it is sufficient to predict the expected performance of adiabatic quantum computers of up to 128 qubits, which perhaps not coincidentally is the number of qubits in the Rainier design.
D-Wave and Dr. Peter Young (a co-author on one of the QMC papers referenced previously) have built a distributed QMC platform to calculate the run time of a quantum adiabatic algorithm relevant to the experimental hardware at D-Wave. The project is called AQUA (Adiabatic QUantum Algorithms), with the distributed version called AQUA@home. The distributed computing technology is based on BOINC, the same technology that enables SETI@home and a host of other large-scale distributed scientific computations.
AQUA is currently running internally at D-Wave. We have set up an external server at http://aqua.dwavesys.com/ that will shortly go live. This server is set up to accept volunteer cycles from individuals who wish to contribute computer time to the AQUA project and make a direct contribution to the advancement of scientific understanding of the quantum adiabatic algorithms. The AQUA@home program runs at low priority in the background of any internet-connected computer. All of the data acquired from AQUA will be published, and everyone who contributes cycles to the project will receive copies of all the publications arising from this work.
The specific project we have been working on internally to test the AQUA system calculates the runtimes for a particular type of problem ideally suited to our hardware. These problems are spin glass problems and are known to be NP-hard. This type of problem will be the first thing we run on AQUA@home to ensure that everything is working properly and will be the basis of the first publication.
After this we plan to run AQUA@home to compute the expected run time of our 128-qubit superconducting adiabatic quantum computing system, running the quantum adiabatic algorithm it enables, on problems generated by the binary classification machine learning application we co-developed with Vasil Dentchev and Hartmut Neven at Google.
Geordie Rose, Dwave CTO, believes that the outcome of this particular project has significant implications for the field of quantum computation, as currently there are no widely accepted meaningful commercial applications of quantum computers. If quantum adiabatic algorithms can solve machine learning problems better than the best known classical approaches that would be a game changer for quantum computation, which currently relies on insufficient amounts of government funding, primarily for its potential role in breaking certain asymmetric cryptosystems, which of course has limited interest to partners and investors in the commercial world.
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.
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