Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July, Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15.

No doubt quantum computing is still in its infancy and remains a mystery to many but D-Wave, founded in 1999, remains committed to being part of the community that brings quantum computing to fruition. Today, it’s still a fairly small community. IBM, of course, is a noteworthy giant in the game. Many significant challenges remain – identifying suitable applications, establishing viable technology, and settling on the best kinds of ‘qubits’ (more seem to appear daily). So far there are few definitive answers.

All of that said, Robert ‘Bo’ Ewald, president of D-Wave and its chief evangelist, has a predictably glass half-full and quickly filling perspective. Yes, agrees Ewald, today’s machines, including D-Wave’s, are research machines hardly ready for prime time. Nevertheless, interest is steadily shifting from seeing quantum computing as an oddity to exploring how it might be practically used.

“We had a meeting yesterday with one of the systems vendors here and one of their customers. They invited us to sit in because they have a problem with a big search and we might be able to help.” This particularly problem involved searching DNA for patterns. More on how D-Wave would help attack such a problem below.

D-Wave’s newest customer, LANL, was so eager to get started that in June, before it received the machine, it issued a rapid response call to scientists to propose projects involving the use of the D-Wave machine. The goal was to expose as many LANL people as possible to D-Wave software development. About twenty proposals were made and eleven were funded.

Ewald said, “They are looking to cover a wide scope of applications covering such things like metallurgy problems to machine learning. Some are very detailed sort of physics and computer science optimization. For example, can you use the D-Wave machine to optimize use of the big supercomputers at Los Alamos because they are so big and complex, maybe it can help to crank a little bit more out of them.”

These projects are now ongoing. Here are three examples with their abstracts, followed by a summary list of the remainder, including links to PDFs of each:

• Constrained Shortest Path Estimation on the D-Wave 2X: Accelerating Ionospheric Parameter Estimation Through Quantum Annealing (Zachary Baker, (PDF)) – “Shortest path computations are a general purpose solution to many problems, but high degrees of connectivity do not map well to the limited connectivity of the D-Wave 2X. By re-casting the problem as a series of 1-of-n choices linked by weighted connections, the shortest path problem is mapped to the quantum machine in a practical and useful way. This approach is demonstrated with an ionospheric parameter estimation problem.”
• Graph Partitioning using the D-Wave for Electronic Structure Problems 
  (Susamn M. Mniszewski, Christian F. A. Negre, and Hayato Ushijima-Mwesigwa, (PDF)) – Graph-based methods are currently being applied to electronic structure problems for quantum molecular dynamics (QMD) simulations. Generating the density matrix as part of a timestep from many small sub-matrices (or sub-graphs) has been shown to be equivalent to more traditional methods (such as diagonalization). We have explored relevant graph partitioning/clustering methods and implementations that run on the D-Wave, 1) partitioning into equal parts minimizing the number of connections between parts and 2) clustering using modularity or community detection. Hierarchical approaches are used for more than two parts/clusters. “Proof of principle” results and comparisons are shown for example benchmark graphs and small material systems on the simulator and D-Wave machine. The DM, ToQ, SAPI, and QBSOLV tools were used in this work.
  
• Generative Modeling for Machine Learning on the D-Wave (Sunil Thulasidasan, (PDF)) – “We will discuss training a generative machine learning model on the D-Wave. This model, known as a Restricted Boltzmann Machine, is often used as a building block for Deep Learning Systems because of its ability to learn features in an unsupervised way. Training such models involve sampling from a Boltzmann distribution at each step, which in theory is achieved by running a Markov Chain to convergence. This is the computational bottleneck in such systems and here we will explore the possibility of using the D-Wave — which is a physical Boltzmann machine — to accelerate this process by using the statistical properties of the energy distribution of states in the D-Wave. We will compare the generative ability of D-Wave to classical methods for a data set of hand-written digits.”
• Efficient Combinatorial Optimization using Quantum Annealing (Hristo Djidjev, Guillaume Chapuis, Georg Hahn, and Guillaume Rizk, (PDF))
• Solving Sparse Representations for Object Classification using the Quantum D-Wave 2X Machine (Garrett Kenyon and Nga Nguyen, (PDF))
• A Programmable Embedder: A Staged Approach for Mapping Problems to the Chimera Graph (Marcus Daniels, (PDF))
• Ising Simulations on the D-Wave QPU (Mike Rogers and Robert Singleton, (PDF))
• D-Wave Quantum Computer as an Efficient Classical Sampler 
  (Michael Chertkov, Aric Hagberg, Andrey Lokhov, Theodor Misiakiewicz, Sidhant Misra, and Marc Vuffray (PDF))
• Challenges and Successes of Solving Binary Quadratic Programming Benchmarks on the DW2X QPU (Carleton Coffrin, Harsh Nagarajan, and Russell Bent (PDF))
• Topological Sphere Packing on the D-Wave (David Nicholaeff)
• Quantum Uncertainty Quantification for Physical Models using ToQ.jl  (Daniel O’Malley and Velimir V. Vesselinov (PDF))

There’s lots of contention around what actually constitutes quantum computing and whose approach will work best, if at all, and for what classes of applications. It’s beyond the scope of this article to examine D-Wave’s adiabatic annealing approach in detail. It relies on low temperature superconductor qubits and the machine must be shielded from a variety of systems and environmental noise.

In brief, D-Wave’s approach is best used for problems that can be described as energy landscapes whose solution is finding the lowest energy state. Think of it as searching for the lowest valley within a mountainous landscape, an analogy favored by Ewald. The key, of course, is that quantum mechanics allows things to be in superposition – two states at the same time. In quantum computing a qubit can be a zero or one simultaneously, collapsing to a single state only when actually looked at. (Remember Schrodinger’s poor cat.)

Quantum theory, of course, is familiar to most in the HPC community and key to semiconductor functionality. Actually building a computable quantum bit is challenging and people argue all the time about whether current efforts actually succeed. D-Wave, with some help from TRW researchers, “came up with the idea of how to build a semiconductor quantum bit that’s really a Josephson junction,” said Ewald.

“In our case it is a loop of niobium, and we are able now to build them in standard CMOS fabs. We worked really hard to eliminate noise but each of those qubits, once we get them down to superconducting temperatures, the current in them is flowing in both directions simultaneously. That’s how we obtain a superposition.”

D-Wave qubits are organized into cells of eight. It’s possible to actually weight individual qubits using a magnetic field to bias them in one direction or the other (zero or one). D-Wave also a developed ‘coupler’ that can be used between qubits and control how the state of one qubit controls another. This is much simplified description. Using these elements it’s possible to program an energy landscape ‘circuit’ which after being excited will settle into its lowest state. Problems whose solutions can be mapped to this process are candidates.

“We are able to create a system which doesn’t add, doesn’t subtract, doesn’t shift left or right, but if you can map the problem onto an energy landscape, it collapses to the low energy solutions which this machine does about 10K per sec. We collapse to the lowest valley in the energy landscape probably. So it is probabilistic. Not deterministic,” said Ewald.

“You don’t run a problem once. You run it 73 times or 100 or 1000 and get a distribution of answers there. So if the energy landscape is like the Alps – steep mountains, narrow valleys, and a low valley someplace – and you run it a 100 times, 92 of the answers will be on that low valley. You can be pretty sure it’s the low valley. But if the problem is the Sahara desert where the elevation is a grain of sand, there are going to be low energies all over the desert and no two will be alike.”

In practice, you set the initial state with some boundary conditions. Excite the machine and let it settle back to its lowest state. Evaluate the results, adjust the boundary conditions, and repeat.

The more qubits you have, the larger energy landscape or problem space you can explore. Ewald is fond of noting D-Wave has been roughly keep pacing with Moore’s Law by doubling the number of qubits every 18-24 months. D-Waves new 2000 qubit processor doubles its previous generation D-Wave 2X system. “The new system also introduces control features that allow users to tune the quantum computational process to solve problems faster and find more diverse solutions when they exist. In early tests these new features have yielded performance improvements of up to 1000 times over the D-Wave 2X system,” according to the company.

Moving back to the SC16 meeting Ewald was invited to. The problem “was more like looking for a needle in a haystack in the lowest valley in Switzerland. We can get you into the lowest valley in Switzerland very fast, but once we are in the low valley, and it’s a flat landscape, you must traditional techniques. We have no clue how to find a needle. So the idea was start with our machine to find a low valley. In this case they are searching for patterns of DNA, and at the start its kind of a rugged landscape. Once you get close there’s more precision involved and traditional HPC resources would work better.”

Hardware development and getting smart people thinking about applications are among the biggest challenges, said Ewald, and the LANL effort is certainly a step forward on the latter problem. Likewise developing software tools for computational scientists to develop applications and ‘compile’ them on D-Wave is critical.

IBM has trumpeted its cloud quantum offering as a place for potential users to play. D-Wave too intends to offer a sandbox of tools. “We have been developing software tools and in the next month or so I think we will put the first incarnation of those out in the open source community and by doing that we are hoping to speed the development.”

Asked what he’d like to be able to report at next year’s SC17, Ewald settled on three areas.

• D-Wave is, after all, a company. He hopes D-Wave will have attracted new customers quantum computing to kick tires and try out the machines.

• Fielded machines. The new 2000-qubit processor is a big step forward. He hopes by next year there are more up and running to maintain momentum and tackle larger problems.

• Software progress. Clearly this is a critical area, encompassing applications and developer tools. He’s hoping for greater community involvement and something to show for it.

Near term, he said, D-Wave systems sales are likely to be once-off machines to organization that want to have them and to get their hands on them and experiment. As software tools improves and become more used by the general community, he expects D-Wave systems to show up in the cloud. For the foreseeable future, you can forget up quantum computers showing in mobile devices and the like because the hardware still need too much specialized care and feeding.