# Will Super Cool SQUIDs Make for an Emerging Industry Standard?

D‑Wave had to break new ground in many engineering disciplines.  One of them was the cooling and shielding technology required to operate their chip.

To this end they are now using ANSYS software, which of course makes for very good marketing for this company (h/t Sol Warda). So good, in fact, that I would hope D‑Wave negotiated a large discount for serving as an ANSYS reference customer.

Any SQUID based quantum computing chip will have similar cooling and shielding requirements, i.e. Google and IBM will have to go through a similar kind of rigorous engineering exercise to productize their approach to quantum computing, even though this approach may look quite different.

Until recently, it would have been easy to forget that IBM is another contender in the ring for SQUID based quantum computing, yet the company's researchers have been working diligently outside the limelight - they last created headlines three years ago. And unlike other quantum computing news, that often only touts marginal improvements, their recent results deserved to be called a break-through, as they improved upon the kind of hardware error correction that Google is betting on.

Obviously, the better your error correction, the more likely you will be able to achieve quantum speed-up when you pursue an annealing architecture like D‑Wave, but IBM is not after yet another annealer. Most articles on the IBM program reports that IBM is into building a  "real quantum computer”, and the term clearly originates from within the company, (e.g. this article attributes the term to Scientists at IBM Research in Yorktown Heights, NY). This leaves little doubt about their commitment to universal gate based QC.

The difference in strategy is dramatic. D‑Wave decided to forgo surface code error correction on the chip in order to get a device to the market.  Google, on the other hand, decided to snap up the best academic surface code implementation money could buy, and also emphasized speed-to-market by first going for another quantum adiabatic design.

All the while, IBM researchers first diligently worked through the stability of SQUID based qubits .  Even now, having achieved the best available error correction, they clearly signaled that they don't consider it good enough for scale-up. It may take yet another three years for them to find the optimal number and configuration of logical qubits that achieves the kind of fidelity they need to then tackle an actual chip.

It is a very methodological engineering approach. Once the smallest building block is perfected,  they will have the confidence that they can go for the moonshot. It's also an approach that only a company with very deep pockets can afford, one with a culture that allows for the pursuit of a decades long research program.

Despite the differences, in the end, all SQUID based chips will have to be operated very close to absolute zero.  IBM's error correction may eventually give it a leg-up over the competition, but I doubt that standard liquid helium fridge technology will suffice for a chip that implements dozens or hundreds of qubits.

By the time IBM enters the market there will be more early adopters of the D‑Wave and Google chips, and the co-opetition between these two companies may have given birth to an emerging industry standard for the fridge technology. In a sense, this may lower the barriers of entry for new quantum chips if the new entrant can leverage this existing infrastructure. It would probably be a first for IBM to cater to a chip interfacing standard that the company did not help to design.

So while there's been plenty of news in the quantum computing hardware space to report, it is curious, and a sign of the times, that a recent Washington Post article on the matter opted to headline with a Quantum Computing Software company i.e. QxBranch. (Robert R. Tucci channeled the journalists at the WP when he wrote last week that the IBM news bodes well for software start-ups in this space).

While tech and business journalists may not (and may possibly never) understand what makes a quantum computer tick, they understand perfectly well that any computing device is just dead weight without software, and that the latter will make the value proposition necessary to create a market for these new machines.

# How many social networks do you need?

The proliferation of social networks seems unstoppable now. Even the big ones you can no longer count on one hand: Facebook, LinkedIn, GooglePlus, Twitter, Instagram, Tumblr, Pinterest, Snapchat - I am so uncool I didn't even know about the latter until very recently. It seems that there has to be a natural saturation point with diminishing marginal return of signing up to yet another one, but apparently we are still far from it.

Recently via LinkedIn I learned about a targeted social network that I happily signed up for, which is quite against my character (i.e. I still don't have a Facebook account).

The aptly named International Quantum Exchange for Innovation is a social network set up by DK Matai with the express purpose of bringing together people of all walks of life anywhere on this globe who are interested in the next wave of the coming Quantum Technology revolution. If you are as much interested in this as I am, then joining this UN of Quantum Technology, as DK puts it, is a no-brainer.

The term 'revolution' is often carelessly thrown around, but in this case I think, when it comes to the new wave of quantum technologies, it is more than justified. After all, the first wave of QM driven technologies powered the second leg of the  Industrial Revolution. It started with a bang, in the worst possible manner, when the first nuclear bomb ignited, but the new insights gained led to a plethora of new high tech products.

Quantum physics was instrumental in everything from solar cells, to lasers, to medical imaging (e.g. MRI) and of course, first and foremost, the transistor. As computers became more powerful, Quantum Chemistry coalesced into an actual field, feeding on the ever increasing computational power. Yet Moore's law proved hardly adequate for its insatiable appetite for the compute cycles required by the underlying quantum numerics.

During Richard Feynman's (too short) life span, he was involved in the military as well as civilian application of quantum mechanics, and his famous "there is plenty of room at the bottom" talk can be read as a programmatic outline of the first Quantum Technology revolution.  This QT 1.0 wave has almost run its course. We made our way to the bottom, but there we encountered entirely new possibilities by exploiting the essential, counter-intuitive non-localities of quantum mechanics.  This takes it to the next step, and again Information Technology is at the fore-front. It is a testament to Feynman's brilliance that he anticipated QT 2.0 as well, when suggesting a quantum simulator for the first time, much along the lines of what D-Wave built.

It is apt and promising that the new wave of quantum technology does not start with a destructive big bang, but an intriguing and controversial black box.

Qubit spin states in diamond defects don't last forever, but they can last outstandingly long even at room temperature (measured in microseconds which is a long time when it comes to computing).

So this is yet another interesting system added to the list of candidates for potential QC hardware.

Nevertheless, when it comes to the realization of scalable quantum computers, qubits decoherence time may very well be eclipsed by the importance of another time span: 20 years, the length at which patents are valid (in the US this can include software algorithms).

With D-Wave and Google leading the way, we may be getting there faster than most industry experts predicted. Certainly the odds are very high that it won't take another two decades for useable universal QC machines to be built.

But how do we get to the point of bootstrapping a new quantum technology industry? DK Matai addressed this in a recent blog post, and identified five key questions, which I attempt to address below (I took the liberty of slightly abbreviating the questions, please check at the link for the unabridged version).

The challenges DK laid out will require much more than a blog post (or a LinkedIn comment that I recycled here), especially since his view is wider than only Quantum Information science. That is why the following thoughts are by no means comprehensive answers, and very much incomplete, but they may provide a starting point.

1. How do we prioritise the commercialisation of critical Quantum Technology 2.0 components, networks and entire systems both nationally and internationally?

The prioritization should be based on the disruptive potential: Take quantum cryptography versus quantum computing for example. Quantum encryption could stamp out fraud that exploits some technical weaknesses, but it won't address the more dominant social engineering deceptions. On the upside it will also facilitate iron clad cryptocurrencies. Yet, if Feynman’s vision of the universal quantum simulator comes to fruition, we will be able to tackle collective quantum dynamics that are computationally intractable with conventional computers. This encompasses everything from simulating high temperature superconductivity to complex (bio-)chemical dynamics. ETH’s Matthias Troyer gave an excellent overview over these killer-apps for quantum computing in his recent Google talk, I especially like his example of nitrogen fixation. Nature manages to accomplish this with minimal energy expenditure in some bacteria, but industrially we only have the century old Haber-Bosch process, which in modern plants still results in 1/2 ton of CO2 for each ton of NH3. If we could simulate and understand the chemical pathway that these bacteria follow we could eliminate one of the major industrial sources of carbon dioxide.

2. Which financial, technological, scientific, industrial and infrastructure partners are the ideal co-creators to invent, to innovate and to deploy new Quantum technologies on a medium to large scale around the world?

This will vary drastically by technology. To pick a basic example, a quantum clock per se is just a better clock, but put it into a Galileo/GPS satellite and the drastic improvement in timekeeping will immediately translate to a higher location triangulation accuracy, as well as allow for a better mapping of the earth's gravitational field/mass distribution.

3. What is the process to prioritise investment, marketing and sales in Quantum Technologies to create the billion dollar “killer apps”?

As sketched out above, the real price to me is universal quantum computation/simulation. Considerable efforts have to go into building such machines, but that doesn't mean that you cannot start to already develop software for them. Any coding for new quantum platforms, even if they are already here (as in the case of the D-Wave 2) will involve emulators on classical hardware, because you want to debug and proof your code before submitting it to the more expansive quantum resource. In my mind building such an environment in a collaborative fashion to showcase and develop quantum algorithms should be the first step. To me this appears feasible within an accelerated timescale (months rather than years). I think such an effort is critical to offset the closed sourced and tightly license controlled approach, that for instance Microsoft is following with its development of the LIQUi|> platform.

4. How do the government agencies, funding Quantum Tech 2.0 Research and Development in the hundreds of millions each year, see further light so that funding can be directed to specific commercial goals with specific commercial end users in mind?

This to me seems to be the biggest challenge. The amount of research papers produced in this field is enormous. Much of it is computational theory. While the theory has its merits, I think the governmental funding should try to emphasize programs that have a clearly defined agenda towards ambitious yet attainable goals. Research that will result in actual hardware and/or commercially applicable software implementations (e.g. the UCSB Martinis agenda). Yet, governments shouldn't be in the position to pick a winning design, as was inadvertently done for fusion research where ITER’s funding requirements are now crowding out all other approaches. The latter is a template for how not to go about it.

5. How to create an International Quantum Tech 2.0 Super Exchange that brings together all the global centres of excellence, as well as all the relevant financiers, customers and commercial partners to create Quantum “Killer Apps”?

On a grassroots level I think open source initiatives (e.g. a LIQUiD alternative) could become catalysts to bring academic excellence centers and commercial players into alignment. This at least is my impression based on conversations with several people involved in the commercial and academic realm. On the other hand, as with any open source products, commercialization won’t be easy, yet this may be less of a concern in this emerging industry, as the IP will be in the quantum algorithms, and they will most likely be executed with quantum resources tied to a SaaS offering.

# Quantum Computing Coming of Age

Are We There Yet? That's the name of the talk that Daniel Lidar recently gave at Google (h/t Sol Warda who posted this in a previous comment).

Spoiler alert, I will summarize some of the most interesting aspects of this talk as I finally found the time to watch it in its entirety.

The first 15 min you may skip if you follow this blog, he just gives a quick introduction to QC. Actually, if you follow the discussion closely on this blog, then you will find not much news in most of the presentation until the very end, but I very much appreciated the graph 8 minutes in, which is based on this Intel data:

Daniel, deservedly, spends quite some time on this, to drive home the point that classical chips have hit a wall.  Moving from Silicon to Germanium will only go so far in delaying the inevitable.

If you don't want to sit through the entire talk, I recommend skipping ahead to the 48 minute mark, when error correction on the D-Wave is discussed. The results are very encouraging, and in the Q&A Daniel points out that this EC scheme could be inherently incorporated into the D-Wave design. Wouldn't be surprised to see this happen fairly soon. The details of the EEC scheme are available at arxiv, and Daniel spends some time on the graph shown below. He is pointing out that, to the extent that you can infer a slope, it looks very promising, as it get flatter as the problems get harder, and the gap between non-EEC and the error corrected annealing widens (solid vs. dashed lines). With EEC I would therefore expect D-Wave machines to systematically outperform simulated annealing.

Daniel sums up the talk like this:

1. Is the D-Wave device a quantum annealer?
• It disagrees with all classical models proposed so far. It also exhibits entanglement. (I.e. Yes, as far as we can tell)
2.  Does it implement a programmable Ising model in a transverse field and solve optimization problems as promised?
• Yes
3. Is there a quantum speedup?
• Too early to tell
4. Can we error-correct it and improve its performance?
• Yes

With regard to hardware implemented qubit EEC, we also got some great news from Martinis' UCSB lab, whom Google drafted for their quantum chip. The latest results have just been published in Nature (pre-print available at arxiv).

Martinis explained the concept in a talk I previously reported on, and clearly the work is progressing nicely. Unlike the EEC scheme for the D-Wave architecture, Martinis' approach is targeting a fidelity that not only will work for quantum annealing, but should also allow for non-trivial gate computing sizes.

Quantum Computing may not have fully arrived yet, but after decades of research we clearly are finally entering the stage where this technology won't be just the domain of theorists and research labs, and at this time, D-Wave is poised to take the lead.

# The Year That Was <insert expletive of your choice>

Usually, I like to start a new year on an upbeat note, but this time I just cannot find the right fit. I was considering whether to revisit technology that can clean water - lauding the effort of the Bill Gates foundation came to mind, but while I think this is a great step in the right direction, this water reclaiming technology is still a bit too complex and expensive to become truly transformational and liberating.

At other times, a groundbreaking progress in increasing the efficiency of solar energy would have qualified, the key being that this can be done comparatively cheaply. Alas, the unprecedented drop in the price of oil is not only killing off the fracking industry, but also the economics for alternative energy.  For a planet that has had its fill of CO2, fossil fuel this cheap is nothing but an unmitigated disaster.

So while it was a banner year for quantum computing, in many respects 2014 was utterly dismal, seeing the return of religiously motivated genocide, open warfare in Europe, a resurgence of diseases that could be eradicated by now, and a pandemic that caused knee jerk hysterical reactions that taught us how unprepared we are for these kind of health emergencies. This year was so depressing it makes me want to wail along to my favorite science blogger's song about it (but then again I'd completely ruin it).

And there is another reason to not yet let go of the past, corrections:

With these corrections out of the way I will finally let go of 2014, but with the additional observation that in the world of quantum computing, the new year started very much in the same vein as the old, generating positive business news for D-Wave, which managed to just raise another 29 million dollars, while at the same time still not getting respect from some academic QC researchers.

I.H. Deutsch (please note, not the Deutsch but Ivan) states at the end of this interview:

1. [1]The D-Wave prototype is not a universal quantum computer.
2. [2]It is not digital, nor error-correcting, nor fault tolerant.
3. [3]It is a purely analog machine designed to solve a particular optimization problem.
4. [4]It is unclear if it qualifies as a quantum device."

No issues with [1]-[3].  But how many times do classical algos have to be ruled out before D-Wave is finally universally accepted as a quantum annealing machine?  This is getting into climate change denying territory. It shouldn't really be that hard to define what makes for quantum computation. So I guess we found a new candidate for D-Wave chief critic, after Scott Aaronson seems to have stepped down for good.

Then again, with a last name like Deutsch, you may have to step up your game to get some name recognition of your own in this field.  And there's no doubt that controversy works.

So 2015 is shaping up to become yet another riveting year for QC news. And just in case you made the resolution that, this year, you will finally try to catch that rainbow, there's some new tech for you.

Update: Almost forgot about this epic fail of popular science reporting at the tail end of 2014.  For now I leave it as an exercise to the reader to spot everything that's wrong with it. Of course most of the blame belongs to PLoS ONE which supposedly practices peer review.

# Progressing from the God Particle to the Gay Particle

… and other physics and QC news

The ‘god particle’, aka the Higgs boson, received a lot of attention, not that this wasn’t warranted, but I can’t help but suspect that the justification of the CERN budget is partly to blame for the media frenzy.  The gay particle, on the other hand, is no less spectacular - especially since its theoretical prediction by far pre-dates the Higgs boson.  Of course, what has been discovered is, yet again, not a real particle but ‘only’ a pseudo particle similar to the magnetic monopol that has been touted recently.  And as usual, most pop-science write-ups fail entirely to remark on this rather fundamental aspect (apparently the journalists don’t want to bother their audience with these boring details). In case you want to get a more complete picture this colloquium paper gives you an in-depth overview.

On the other hand, a pseudo particle quantum excitation in a 2d superconductor is exactly what the doctor ordered for topological quantum computing, a field that has seen tremendous theoretical progress as it has been generously sponsored by Microsoft. This research entirely hinges on employing these anyon pseudoparticles as a hardware resource, because they have the fantastic property of allowing for inherently decoherence-resistant qubits.  This is as if theoretical computer science would have started writing the first operating system in the roaring twenties of the last century, long before there was a computer or even a transistor, theorizing that a band gap in doped semiconductors should make it possible to build one. If this analogy was to hold, we’d now be at the stage where a band gap has been demonstrated for the first time.  So here's to hoping this means we may see the first anyon-based qubit within the decade.

In the here and now of quantum computing, D-Wave merrily stays the course despite the recent Google bombshell news.  It has been reported that they now have 12 machines operational, used in a hosted manner by their strategic partners (such as 1Qbit).  They also continue to add staff from other superconducting outfits i.e. recently Bill Blake left Cray to join the company as VP of R&D.

Last but not least, if you are interested in physics you would have to live under a rock not to have heard about the sensational news that numerical calculations presumably proofed that black holes cannot form and hence do not exist.  Sabine Hossenfelder nicely deconstructs this.  The long and short of it is that this argument has been going on for a long time, that the equations employed in this research has some counter-intuitive properties, and that the mass integral employed is not all that well-motivated.

Einstein would have been happy if this pans out, after all this research claims to succeed where he failed, but the critical reception of this numerical model has just begun. It may very well be torn apart like an unlucky astronaut in a strongly in-homogeneous gravitational field.

This concludes another quick round-up post. I am traveling this week and couldn't make the time for a longer article, but I should find my way back to a more regular posting schedule next week.

# What Defines a Quantum Computer?

Recently a friend of mine observed in an email discussion "I must admit I find it a little difficult to keep up with the various definitions of quantum computing."

A healthy sign for an enlightened confusion, because this already sets him apart from most people who still have yet to learn about this field, and at best think that all quantum computers are more or less equivalent.

As computers became an integral part of peoples everyday lives, they essentially learn the truth of Turing completeness - even if they have never heard the term.  Now, even a child exposed to various computing devices will quickly develop a sense that whatever one computer can do, another should be able to perform as well, with some allowance for the performance specs of the machine.  Older, more limited machines may not be able to run a current software for compatibility or memory scaling reasons, but there is no difference in principle that would prevent any computer from executing whatever has already been proven to work on another machine.

In the quantum computing domain, things are less clear cut. In my earlier post where I tried my hand at a quantum computing taxonomy, I focused on maturity of the technology, less so on the underlying theoretical model. However, it is the dichotomy in the latter that has been driving the heated controversy of D-Wave's quantumness.

When David Deutsch wrote his seminal paper, he followed in Turing's footsteps, thinking through the consequences of putting a Turing machine into quantum superposition. This line of inquiry eventually gave rise to the popular gate model of quantum computing.

D-Wave, on the other hand, gambled on adiabatic quantum computing, and more specifically, an implementation of quantum annealing.  In preparation for this post I sought to look up these terms in my copy of Nielsen and Chuang's 'Quantum Computation and Quantum Information' textbook.  To my surprise, neither term can be found in the index, and this is the 2010 anniversary edition.  Now, this is not meant to knock the book, and if you want to learn about the gate model I think you won't find a better one. It just goes to show that neither the adiabatic nor annealing approach was on the academic radar when the book was originally written - the first paper on adiabatic quantum computation (Farhi et al.) was published the same year as the first edition of this standard QIS textbook.

At the time it was not clear how the computational powers of the adiabatic approach compared to the quantum gate model. Within a year, Vazirani et al. published a paper that showed that Grover Search can be implemented on this architecture with quantum speed-up.  And although the notoriety of Shore's algorithm overshadows Grover's, the latter has arguably much more widespread technological potential. The Vazirani et al. paper also demonstrated that there will be problem instances that this QC model will not be able to solve efficiently, even though they can be tackled classically.

In 2004 a paper was submitted with a title that neatly sums it up: "Adiabatic Quantum Computation is Equivalent to Standard Quantum Computation" (Lloyd et al.)

If D-Wave had aimed for universal adiabatic quantum computation, maybe it would not have experienced quite as much academic push-back, but they pragmatically went after some lower hanging fruit i.e, quantum annealing. (Notwithstanding, this doesn't stop  MIT's Seth Lloyd from claiming that the company uses his ideas when pitching his own QC venture).

An adiabatic quantum computing algorithm encodes a problem into a cost, or in this case energy function, that is then explored for its absolute minimum. For instance, if you try to solve the traveling salesman problem your cost function would simply be distance traveled for each itinerary. A simple classical gradient descent algorithm over this energy 'landscape' will quickly get stuck in a local minimum (for an analog think of balls rolling down the hilly landscape collecting at some bottom close to were they started and you get the idea).  A truly quantum algorithm, on the other hand, can exploit the 'spooky' quantum properties, such as entanglement and the tunnel effect . In essence, it is as if our rolling balls could somehow sense that there is a deeper valley adjacent to their resting place and "tunnel through" the barrier (hence the name).  This gives these algorithms some spread-out look-ahead capabilities.  But depending on your energy function, this may still not be enough.

The graph bellow illustrates this with a completely made-up cost function, that while entirely oversimplified, hopefully still somewhat captures the nature of the problem. To the extent that the look-ahead capabilities of an adiabatic algorithm are still locally limited, long flat stretches with a relative minimum (a 'plain' in the energy landscape)  can still defeat it. I threw in some arbitrary Bell curves as a stand in for this local quantum 'fuzziness' (the latter incidentally the correct translation for what Heisenberg called his famous relation).

To the left, this fuzzy width doesn't stretch outside the bounds of the flat stretch (or rather, it is negligibly small outside any meaningful neighborhood of this local minimum).

On the other hand, further to the right there is some good overlap between the local minimum closest to the absolute one (overlayed with the bell curve in green).  This is where the algorithm will perform well.D-Wave essentially performs such an algorithm with the caveat that it does not allow completely arbitrary energy functions, but only those that can be shoe-horned into the Ising model.

This was a smart pragmatic decision on their part because this model was originally created to describe solid state magnets that were imagined as little coupled elementary magnetic dipoles, and the latter map perfectly to the superconducting magnetic fluxes that are implemented on the chip.

In terms of complexity, even in a simple classical 2-d toy model, the amount of possible combinations is pretty staggering as the video below nicely demonstrates. The corresponding energy function (Hamiltonian in QM) is surprisingly versatile an can encode a large variety of problems.

# The Google-Martinis Chip Will Perform Quantum Annealing

Ever since the news that John M. Martinis will join Google to develop a chip based on the work that has been performed at UCSB, speculations abound as to what kind of quantum architecture this chip will implement.  According to this report, it is clear now that it will be adiabatic quantum computing:

But examining the D-Wave results led to the Google partnership. D-Wave uses a process called quantum annealing. Annealing translates the problem into a set of peaks and valleys, and uses a property called quantum tunneling to drill though the hills to find the lowest valley. The approach limits the device to solving certain kinds of optimization problems rather than being a generalized computer, but it could also speed up progress toward a commercial machine. Martinis was intrigued by what might be possible if the group combined some of the annealing in the D-Wave machine with his own group's advances in error correction and coherence time.
"There are some indications they're not going to get a quantum speed up, and there are some indications they are. It's still kind of an open question, but it's definitely an interesting question," Martinis said. "Looking at that, we decided it would be really interesting to start another hardware approach, looking at the quantum annealer but basing it on our fabrication technology, where we have qubits with very long memory times."

This leads to the next question: Will this Google chip be indeed similarly restricted to implementing the Ising model like D-Wave, or strive for more universal adiabatic quantum computation? The later has theoretically been shown to be computationally equivalent to gate based QC. It seems odd to just aim for a marginal improvement of the existing architecture as this article implicates.

At any rate, D-Wave may retain the lead in qubit numbers for the foreseeable future if it sticks to no, or less costly, error correction schemes (leaving it to the coders to create their own). It will be interesting to eventually compare which approach will offer more practical benefits.

In light of the recent news that John Martinis is joining Google, it is worthwhile to check out this Google talk from last year:

It is an hour long talk but very informative. John Martinis does an excellent job at explaining, in very simple terms, how hardware-based surface code error correction works.

Throughout the talk he uses the Gate model formalism.  Hence it is quite natural to assume that this is what the Google chip will aim for. This is certainly reinforced by the fact that other publications, such as from the IEEE, have also drawn a stark contrast between the Martinis approach, and D-Wave's quantum annealing architecture. This is certainly how I interpreted the news as well.

But on second thought, and careful parsing of the press releases, the case is not as clear cut. For instance, Technology Review quotes Martinis in this fashion:

“We would like to rethink the design and make the qubits in a different way,” says Martinis of his effort to improve on D-Wave’s hardware. “We think there’s an opportunity in the way we build our qubits to improve the machine.”

This sounds more like Martinis wants to build a quantum annealing chip based on his logical, error corrected qubits.  From an engineering stand-point this would make sense, as this should be easier to achieve than a fully universal gate-based architecture, and it will address the key complaint that I heard from developers programming the D-Wave chip i.e. that they really would like to see error correction implemented on the chip.

On the other hand, in light of Martinis presentation, I presume that he will regard such an architecture simply as another stepping stone towards universal quantum computation.

# News Roundup

As school starts, I should find my way back to a regular blogging schedule. I usually drive my kids to German Saturday school and then pass the time at a nearby Starbucks updating this blog.

Job and family demanded too much of my time this summer. The former has gotten very interesting, as I am documenting a bank stress testing system, but the learning curve is steep. And while I just had a pleasant one week vacation at a pristine Northern lake, it very much lacked in Wifi connectivity and was not conducive to blogging. Yet, I had plenty of time to read up on material that will make for future posts.

Back home, my kids incidentally watched the Nova episode that features D-Wave and Geordie Rose, which prompted my mother-in-law to exclaim that she wants stock in this company. Her chance to act on this may come in the not too distant future. Recently, D-Wave's CEO hinted for the first time that there may be an IPO in the offing (h/t Rolf D).

Readers who follow the QC blogs have undoubtedly already learned about an interesting paper that supports D-Wave’s approach, since Geordie highlighted it on the company’s blog. The fact that Robert R. Tucci is looking for an experienced business partner to start a QC algorithm venture with may also already qualify as old news - Bob is mostly focused on the Gate model, but is agnostic about the adiabatic approach, and certainly displays an impressive grit and track record in consistently turning out patents and papers.

When it comes to love and business, timing is everything. The US allows for software patent protection of up to 20 years. This is a sufficiently long time frame to bet on Gate QC becoming a reality. But there is still a bit of a chicken and egg problem associated with this technology. After all, it is much more difficult (Geordie Rose would argue unrealistically so) then what D-Wave is doing. Shor’s algorithm alone cannot justify the necessary R&D expense to develop and scale up the required hardware, but other commercially more interesting algorithms very well may. Yet you only invest in developing those if there is a chance that you’ll eventually (within 20 years) have hardware to run them on. Currently, it still falls to academia to breach the gap, e.g. such as these Troyer et al. papers that make hope that quantum chemistry could see tangible speed-up from even modestly sized gate based quantum computers.

While quantum computing will remain a main theme of this blog, I intend to also get back to some more biographical posts that reflect on how the history of physics has evolved. Just as any human history, it is full of the oddest turns and twists that are more often than not edited out of the mainstream narrative. And just to be clear, this is not to suggest some grand conspiracy, but just another expression of the over-simplification that afflicts most popular science writing. Writing for the least common denominator makes often for rather poor results, but just as Sabine observes

The “interested public” is perfectly able to deal with some technical vocabulary as long as it comes with an explanation.

In the same vein, the intricacy of how scientific discovery progresses deserves some limelight as it illuminates the roads less traveled. It also makes for interesting thought experiments, imagining how physics may have developed if certain experiments or math had been discovered earlier, or one scientist's life hadn't been cut too short.

My next post will deal in some such idle speculation.

Update: This just in, Google sets out on its own (h/t bettinman), planning to put $8B into its proprietary QC hardware effort. which makes me wonder if the investment will match IBM's$3B to reach the post silicon area.  Not clear yet what this will mean for their relationship with D-Wave.