Mark Johnson talks about D-Wave’s processors.
Mark Johnson talks about D-Wave’s processors.
D-Wave’s Murray Thom explaining what’s going on inside the fridge in one of our systems.
Seeing the D-Wave facilities first-hand is a very cool experience. They look a lot like computers did back in the 60s. There are a lot of parallels to back then — we even built our own version of Spacewar! — except you get to play against a quantum computer. (Aside: this game — which was the world’s first quantum computer game — was called MaxCat. I own the only handwritten copy of the rules…. one of my most treasured artifacts!)
Here’s the first part of a series showing what you’d see if you visited D-Wave’s main experimental facility. Shot by D-Wave’s Dom Walliman — experimental physicist, author, and videographer extraordinaire, and starring Jeremy Hilton, D-Wave’s VP of Processor Development. Hope you like it!
Recently the Universities Space Research Association (USRA) announced that they were accepting proposals for computer time on the D-Wave system at the Quantum Artificial Intelligence Lab located at NASA Ames Research Center. Details are as follows, and you can find out more (and download the RFP) at USRA’s website at http://www.usra.edu/quantum/rfp/. We encourage researchers to take advantage of this opportunity.
The Universities Space Research Association (USRA) is pleased to invite proposals for Cycle 1 of the Quantum Artificial Intelligence Laboratory Research Opportunity, which will allocate computer time for research projects to be run on the D-Wave System at NASA Ames Research Center (ARC) for the time period November 2014 through September 2015.
The total allocated computer time for the Cycle 1 research opportunity represents approximately 20% of the total available runtime during the period. Successful projects will be allowed to remotely access the quantum computer, and to run a number of jobs up to a maximum allocated runtime usage.
The Call is open to all qualified researchers affiliated to accredited universities and other research organizations. Exceptions to researchers unaffiliated with universities might be considered in case of proposals of outstanding quality and the desire to publish the results of the investigation. The computer time will be provided free of charge. No financial support is offered for the completion of the project.
Proposals are sought for research on artificial intelligence algorithms and advanced programming (mapping, decomposition, embedding) techniques for quantum annealing, with the objective to advance the state-of-the-art in quantum computing and its application to artificial intelligence.
Colin Williams recently presented some new results in the UK. Here you can see some advance looks at the first results on up to 933 qubits. These are very early days for the Washington generation. Things will get a lot better on this one before it’s released (Rainier and Vesuvius both took 7 generations of iteration before they stabilized). But some good results on the first few prototypes.
One of the interesting things we’re playing with now is the following idea (starts at around 22:30 of the presentation linked to above). Imagine instead of measuring the time to find the ground state of a problem with some probability, instead measure the difference between the ground state energy and the median energy of samples returned, as a function of time and problem size. If we do this what we find is that the median distance from the ground state scales like where is the number of qubits, and is the number of couplers in the instance (proportional to for the current generation). More important, the scaling with time flattens out and becomes nearly constant. This is consistent with the main error mechanism being mis-specification of problem parameters in the Hamiltonian (what we call ICE or Intrinsic Control Errors).
In other words, the first sample from the processor (ie constant time), with high probability, will return a sample no further than from the ground state. That’s pretty cool.
Another paper, demonstrating some interesting techniques for overcoming practical problems in using D-Wave hardware. (Apologies Diana for the continuing lack of interpretation of these results :-) ). These techniques were applied to Low Density Parity Check problems.
This paper discusses techniques for solving discrete optimization problems using quantum annealing. Practical issues likely to affect the computation include precision limitations, finite temperature, bounded energy range, sparse connectivity, and small numbers of qubits. To address these concerns we propose a way of finding energy representations with large classical gaps between ground and first excited states, efficient algorithms for mapping non-compatible Ising models into the hardware, and the use of decomposition methods for problems that are too large to fit in hardware. We validate the approach by describing experiments with D-Wave quantum hardware for low density parity check decoding with up to 1000 variables.