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.
Here are some pictures of the most recent Washington generation chips. These are C16 chips — 16*16*8 = 2,048 physical qubits. Enjoy!
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.
Discrete optimization using quantum annealing on sparse Ising models
- 1D-Wave Systems, Burnaby, BC, Canada
- 2Department of Computer Science, Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD, USA
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.
More science on data from the D-Wave One system at USC.
Reexamining classical and quantum models for the D-Wave One processor
(Submitted on 12 Sep 2014)
We revisit the evidence for quantum annealing in the D-Wave One device (DW1) based on the study of random Ising instances. Using the probability distributions of finding the ground states of such instances, previous work found agreement with both simulated quantum annealing (SQA) and a classical rotor model. Thus the DW1 ground state success probabilities are consistent with both models, and a different measure is needed to distinguish the data and the models. Here we consider measures that account for ground state degeneracy and the distributions of excited states, and present evidence that for these new measures neither SQA nor the classical rotor model correlate perfectly with the DW1 experiments. We thus provide evidence that SQA and the classical rotor model, both of which are classically efficient algorithms, do not satisfactorily explain all the DW1 data. A complete model for the DW1 remains an open problem. Using the same criteria we find that, on the other hand, SQA and the classical rotor model correlate closely with each other. To explain this we show that the rotor model can be derived as the semiclassical limit of the spin-coherent states path integral. We also find differences in which set of ground states is found by each method, though this feature is sensitive to calibration errors of the DW1 device and to simulation parameters.
A new paper from users of the D-Wave Two at USC. Here’s the abstract:
We demonstrate that the performance of a quantum annealer on hard random Ising optimization problems can be substantially improved using quantum annealing correction (QAC). Our error correction strategy is tailored to the D-Wave Two device. We find that QAC provides a statistically significant enhancement in the performance of the device over a classical repetition code, improving as a function of problem size as well as hardness. Moreover, QAC provides a mechanism for overcoming the precision limit of the device, in addition to correcting calibration errors. Performance is robust even to missing qubits. We present evidence for a constructive role played by quantum effects in our experiments by contrasting the experimental results with the predictions of a classical model of the device. Our work demonstrates the importance of error correction in appropriately determining the performance of quantum annealers.
A great blog post by Alexei Andreev, a long-time investor in D-Wave and PhD in condensed matter physics. Excellent insights, recommended reading!