Around May 15th of 2013 Google acquired a system built around a 509-qubit Vesuvius 6 (V6) chip. Since it went online, they have been running it 24/7 at 100% usage. Most of this time has been committed to benchmarking.

Some of these results have been published, and there has been some discussion of what it all means. Here I’d like to provide my own view of where I think we are, and what these results show.

**Nextbigfuture observation**

Dwave now has a 1024 qubit system and they have a deal with Cypress semiconductors where they can fabricate a new chip design about once every month. They should be be able to design in enhanced coupling very quickly. Dwave could have a more highly coupled 1024 qubit system within months and a highly coupled and otherwise improved 2048 qubit designs.

DWave is learning how to make their chips perform better. IF error correction is needed to reach faster speeds they can work those modications into their designs as well.

**Back to Geordie Rose and his observations**

**Interesting finding #1: Dwave V6 is the first superconducting processor competitive with state of the art semiconducting processors.**

The results that were recently published in the Ronnow et. al. paper show that V6 is competitive with what’s arguably the most highly optimized semiconductor based solution possible today, even on a problem type that in hindsight was a bad choice. A fact that has not gotten as much coverage as it probably should is that V6 beats this competitor both in wallclock time and scaling for certain problem types. That is a truly astonishing achievement. Mattias Troyer and his team achieved an incredible level of optimization with his simulated annealing code, achieving 200 spin updates per nanosecond using a GPU based approach. The ‘out of the box’ unoptimized V6 system beats this approach for some problem types, and even for problem types where it doesn’t do so well (like the ones described in the Ronnow paper) it holds its own, and even wins in some cases.

This is a remarkable historic achievement. It’s the first delivery on the promise of superconducting processors.

Regular semiconductors have had $1 trillion in research and superconducting chips have had about $4 billion.

**Interesting finding #2: V6 is the first computing system using ideas from quantum information science competitive with the best classical computing systems.**

Much like in the case of superconducting processors, the field of quantum computing has promised to provide new ways of doing things that are superior to the ways things are now. And much like superconducting processors, the actual delivery on that promise has been virtually non-existent.

The results of the recent studies show that V6 is the first computing system that uses ideas from quantum information science that is competitive with the best classical algorithms known run on the fastest modern processors available.

**Interesting finding #3: The problem type chosen for the benchmarking was wrong.**

The type of problem that the Ronnow paper looked at — random spin glasses — made a lot of sense when the project began. Unfortunately about midway through the project it was discovered that this type of problem was expected theoretically to show no difference in scaling between simulated annealing (SA) and quantum annealing (QA). This analysis showed that it was necessary to add structure to the problem instances to see a scaling difference between the two. So if an analysis of the D-Wave approach has as its objective observing a scaling difference between SA and QA, random spin glass problems are the wrong choice.

UPDATE- Helmut Katzgraber (Texas A&M computational physics researcher)

Spin glasses are likely the best benchmark to run. Just not the standard vanilla random couplings on the Chimera topology. The couplings need to be chosen carefully in an unbiased way such that the problem becomes harder. Please look carefully at the preprint that follows.

Recently, a programmable quantum annealing machine has been built that minimizes the cost function of hard optimization problems by adiabatically quenching quantum fluctuations. Tests performed by different research teams have shown that, indeed, the machine seems to exploit quantum effects. However experiments on a class of random-bond instances have not yet demonstrated an advantage over classical optimization algorithms on traditional computer hardware. Here we present evidence as to why this might be the case. These engineered quantum annealing machines effectively operate coupled to a decohering thermal bath. Therefore, we study the finite-temperature critical behavior of the standard benchmark problem used to assess the computational capabilities of these complex machines. We simulate both random-bond Ising models and spin glasses with bimodal and Gaussian disorder on the D-Wave Chimera topology. Our results show that while the worst-case complexity of finding a ground state of an Ising spin glass on the Chimera graph is not polynomial, the finite-temperature phase space is likely rather simple: Spin glasses on Chimera have only a zero-temperature transition. This means that benchmarking optimization methods using spin glasses on the Chimera graph might not be the best benchmark problems to test quantum speedup. We propose alternative benchmarks by embedding potentially harder problems on the Chimera topology. Finally, we also study the (reentrant) disorder-temperature phase diagram of the random-bond Ising model on the Chimera graph and show that a finite-temperature ferromagnetic phase is stable up to 19.85(15)% antiferromagnetic bonds. Beyond this threshold the system only displays a zero-temperature spin-glass phase. Our results therefore show that a careful design of the hardware architecture and benchmark problems is key when building quantum annealing machines.

**Interesting finding #4: Google seems to love their machine.**

Last week Google released a blog post about their benchmarking efforts that provide an overview of how they feel about what they’ve been seeing. Here are some key points they raise in that post.

In an early test we dialed up random instances and pitted the machine against popular off-the-shelf solvers — Tabu Search, Akmaxsat and CPLEX. At 509 qubits, the machine is about 35,500 times (!) faster than the best of these solvers.

This is an important result. Beating a trillion dollars worth of investment with only the second generation of an entirely new computing paradigm by 35,500 times is a pretty damn awesome achievement. NOTE FOR EXPERTS: CPLEX was NOT run in these tests to global optimality. It was run in a mode where it was timed to the time it found a target solution, and not to the time it took to prove global optimality. In addition, Tabu Search is nearly always the best tool if you don’t know the structure of the QUBO problem you are solving. Beating it by this much is a Big Deal.

For each classical solver, there are problems for which the hardware does much better.

This is extremely cool also. Even though we are now talking about the best solvers we know how to create, our Vesuvius chip, with about 0.001% of the investment of its competitor, is holding its own.

A principal reason the portfolio solver is still competitive right now is actually rather mundane — the qubits in the current chip are still only sparsely connected.

This is really important to understand — making the D-Wave technology better is likely about making the problems being solved more rich by adding more couplers to the chip, which is just an engineering issue that is nearly completely decoupled from other things like the role of quantum mechanics in all of this. It is really straightforward to make this change.

Eyeballing this treasure trove of data, we’re now trying to identify a class of problems for which the current quantum hardware might outperform all known classical solvers.

Now this is really cool. Even for Vesuvius there might be problems for which no known classical computer can compete!

**Interesting finding #5: The system has been running 24/7 with not even a second of downtime for about six months.**

This is also worth pointing out, as it’s quite a complex machine with the business end at or around 10 millikelvin. This aspect of the machine isn’t as sexy as some of the other issues typically discussed, but it’s evidence that the underlying engineering of the system is really pretty awesome.

**Interesting finding #6: The technology has come a long way in a short period of time.**

None of the above points were true last year. The discussion is now about whether we can beat any possible computer — even though it’s really only the second generation of an entirely new computing paradigm, built on a shoestring budget.

The next few generations of chip should push us way past this threshold — this is by far the most interesting time in the 15 year history of this project.

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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.

A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.