Tag Archives: Hartmut Neven

Exotic magnetism: a quantum simulation from D-Wave Sytems

Vancouver (Canada) area company, D-Wave Systems is trumpeting itself (with good reason) again. This 2021 ‘milestone’ achievement builds on work from 2018 (see my August 23, 2018 posting for the earlier work). For me, the big excitement was finding the best explanation for quantum annealing and D-Wave’s quantum computers that I’ve seen yet (that explanation and a link to more is at the end of this posting).

A February 18, 2021 news item on phys.org announces the latest achievement,

D-Wave Systems Inc. today [February 18, 2021] published a milestone study in collaboration with scientists at Google, demonstrating a computational performance advantage, increasing with both simulation size and problem hardness, to over 3 million times that of corresponding classical methods. Notably, this work was achieved on a practical application with real-world implications, simulating the topological phenomena behind the 2016 Nobel Prize in Physics. This performance advantage, exhibited in a complex quantum simulation of materials, is a meaningful step in the journey toward applications advantage in quantum computing.

A February 18, 2021 D-Wave Systems press release (also on EurekAlert), which originated the news item, describes the work in more detail,

The work by scientists at D-Wave and Google also demonstrates that quantum effects can be harnessed to provide a computational advantage in D-Wave processors, at problem scale that requires thousands of qubits. Recent experiments performed on multiple D-Wave processors represent by far the largest quantum simulations carried out by existing quantum computers to date.

The paper, entitled “Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets”, was published in the journal Nature Communications (DOI 10.1038/s41467-021-20901-5, February 18, 2021). D-Wave researchers programmed the D-Wave 2000Q™ system to model a two-dimensional frustrated quantum magnet using artificial spins. The behavior of the magnet was described by the Nobel-prize winning work of theoretical physicists Vadim Berezinskii, J. Michael Kosterlitz and David Thouless. They predicted a new state of matter in the 1970s characterized by nontrivial topological properties. This new research is a continuation of previous breakthrough work published by D-Wave’s team in a 2018 Nature paper entitled “Observation of topological phenomena in a programmable lattice of 1,800 qubits” (Vol. 560, Issue 7719, August 22, 2018). In this latest paper, researchers from D-Wave, alongside contributors from Google, utilize D-Wave’s lower noise processor to achieve superior performance and glean insights into the dynamics of the processor never observed before.

“This work is the clearest evidence yet that quantum effects provide a computational advantage in D-Wave processors,” said Dr. Andrew King, principal investigator for this work at D-Wave. “Tying the magnet up into a topological knot and watching it escape has given us the first detailed look at dynamics that are normally too fast to observe. What we see is a huge benefit in absolute terms, with the scaling advantage in temperature and size that we would hope for. This simulation is a real problem that scientists have already attacked using the algorithms we compared against, marking a significant milestone and an important foundation for future development. This wouldn’t have been possible today without D-Wave’s lower noise processor.”

“The search for quantum advantage in computations is becoming increasingly lively because there are special problems where genuine progress is being made. These problems may appear somewhat contrived even to physicists, but in this paper from a collaboration between D-Wave Systems, Google, and Simon Fraser University [SFU], it appears that there is an advantage for quantum annealing using a special purpose processor over classical simulations for the more ‘practical’ problem of finding the equilibrium state of a particular quantum magnet,” said Prof. Dr. Gabriel Aeppli, professor of physics at ETH Zürich and EPF Lausanne, and head of the Photon Science Division of the Paul Scherrer Institute. “This comes as a surprise given the belief of many that quantum annealing has no intrinsic advantage over path integral Monte Carlo programs implemented on classical processors.”

“Nascent quantum technologies mature into practical tools only when they leave classical counterparts in the dust in solving real-world problems,” said Hidetoshi Nishimori, Professor, Institute of Innovative Research, Tokyo Institute of Technology. “A key step in this direction has been achieved in this paper by providing clear evidence of a scaling advantage of the quantum annealer over an impregnable classical computing competitor in simulating dynamical properties of a complex material. I send sincere applause to the team.”

“Successfully demonstrating such complex phenomena is, on its own, further proof of the programmability and flexibility of D-Wave’s quantum computer,” said D-Wave CEO Alan Baratz. “But perhaps even more important is the fact that this was not demonstrated on a synthetic or ‘trick’ problem. This was achieved on a real problem in physics against an industry-standard tool for simulation–a demonstration of the practical value of the D-Wave processor. We must always be doing two things: furthering the science and increasing the performance of our systems and technologies to help customers develop applications with real-world business value. This kind of scientific breakthrough from our team is in line with that mission and speaks to the emerging value that it’s possible to derive from quantum computing today.”

The scientific achievements presented in Nature Communications further underpin D-Wave’s ongoing work with world-class customers to develop over 250 early quantum computing applications, with a number piloting in production applications, in diverse industries such as manufacturing, logistics, pharmaceutical, life sciences, retail and financial services. In September 2020, D-Wave brought its next-generation Advantage™ quantum system to market via the Leap™ quantum cloud service. The system includes more than 5,000 qubits and 15-way qubit connectivity, as well as an expanded hybrid solver service capable of running business problems with up to one million variables. The combination of Advantage’s computing power and scale with the hybrid solver service gives businesses the ability to run performant, real-world quantum applications for the first time.

That last paragraph seems more sales pitch than research oriented. It’s not unexpected in a company’s press release but I was surprised that the editors at EurekAlert didn’t remove it.

Here’s a link to and a citation for the latest paper,

Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets by Andrew D. King, Jack Raymond, Trevor Lanting, Sergei V. Isakov, Masoud Mohseni, Gabriel Poulin-Lamarre, Sara Ejtemaee, William Bernoudy, Isil Ozfidan, Anatoly Yu. Smirnov, Mauricio Reis, Fabio Altomare, Michael Babcock, Catia Baron, Andrew J. Berkley, Kelly Boothby, Paul I. Bunyk, Holly Christiani, Colin Enderud, Bram Evert, Richard Harris, Emile Hoskinson, Shuiyuan Huang, Kais Jooya, Ali Khodabandelou, Nicolas Ladizinsky, Ryan Li, P. Aaron Lott, Allison J. R. MacDonald, Danica Marsden, Gaelen Marsden, Teresa Medina, Reza Molavi, Richard Neufeld, Mana Norouzpour, Travis Oh, Igor Pavlov, Ilya Perminov, Thomas Prescott, Chris Rich, Yuki Sato, Benjamin Sheldan, George Sterling, Loren J. Swenson, Nicholas Tsai, Mark H. Volkmann, Jed D. Whittaker, Warren Wilkinson, Jason Yao, Hartmut Neven, Jeremy P. Hilton, Eric Ladizinsky, Mark W. Johnson, Mohammad H. Amin. Nature Communications volume 12, Article number: 1113 (2021) DOI: https://doi.org/10.1038/s41467-021-20901-5 Published: 18 February 2021

This paper is open access.

Quantum annealing and more

Dr. Andrew King, one of the D-Wave researchers, has written a February 18, 2021 article on Medium explaining some of the work. I’ve excerpted one of King’s points,

Insight #1: We observed what actually goes on under the hood in the processor for the first time

Quantum annealing — the approach adopted by D-Wave from the beginning — involves setting up a simple but purely quantum initial state, and gradually reducing the “quantumness” until the system is purely classical. This takes on the order of a microsecond. If you do it right, the classical system represents a hard (NP-complete) computational problem, and the state has evolved to an optimal, or at least near-optimal, solution to that problem.

What happens at the beginning and end of the computation are about as simple as quantum computing gets. But the action in the middle is hard to get a handle on, both theoretically and experimentally. That’s one reason these experiments are so important: they provide high-fidelity measurements of the physical processes at the core of quantum annealing. Our 2018 Nature article introduced the same simulation, but without measuring computation time. To benchmark the experiment this time around, we needed lower-noise hardware (in this case, we used the D-Wave 2000Q lower noise quantum computer), and we needed, strangely, to slow the simulation down. Since the quantum simulation happens so fast, we actually had to make things harder. And we had to find a way to slow down both quantum and classical simulation in an equitable way. The solution? Topological obstruction.

If you have time and the inclination, I encourage you to read King’s piece.

Quantum supremacy

This supremacy, refers to an engineering milestone and a October 23, 2019 news item on ScienceDaily announces the milestone has been reached,

Researchers in UC [University of California] Santa Barbara/Google scientist John Martinis’ group have made good on their claim to quantum supremacy. Using 53 entangled quantum bits (“qubits”), their Sycamore computer has taken on — and solved — a problem considered intractable for classical computers.

An October 23, 2019 UC Santa Barbara news release (also on EurekAlert) by Sonia Fernandez, which originated the news item, delves further into the work,

“A computation that would take 10,000 years on a classical supercomputer took 200 seconds on our quantum computer,” said Brooks Foxen, a graduate student researcher in the Martinis Group. “It is likely that the classical simulation time, currently estimated at 10,000 years, will be reduced by improved classical hardware and algorithms, but, since we are currently 1.5 trillion times faster, we feel comfortable laying claim to this achievement.”

The feat is outlined in a paper in the journal Nature.

The milestone comes after roughly two decades of quantum computing research conducted by Martinis and his group, from the development of a single superconducting qubit to systems including architectures of 72 and, with Sycamore, 54 qubits (one didn’t perform) that take advantage of the both awe-inspiring and bizarre properties of quantum mechanics.

“The algorithm was chosen to emphasize the strengths of the quantum computer by leveraging the natural dynamics of the device,” said Ben Chiaro, another graduate student researcher in the Martinis Group. That is, the researchers wanted to test the computer’s ability to hold and rapidly manipulate a vast amount of complex, unstructured data.

“We basically wanted to produce an entangled state involving all of our qubits as quickly as we can,” Foxen said, “and so we settled on a sequence of operations that produced a complicated superposition state that, when measured, returns bitstring with a probability determined by the specific sequence of operations used to prepare that particular superposition. The exercise, which was to verify that the circuit’s output correspond to the equence used to prepare the state, sampled the quantum circuit a million times in just a few minutes, exploring all possibilities — before the system could lose its quantum coherence.

‘A complex superposition state’

“We performed a fixed set of operations that entangles 53 qubits into a complex superposition state,” Chiaro explained. “This superposition state encodes the probability distribution. For the quantum computer, preparing this superposition state is accomplished by applying a sequence of tens of control pulses to each qubit in a matter of microseconds. We can prepare and then sample from this distribution by measuring the qubits a million times in 200 seconds.”

“For classical computers, it is much more difficult to compute the outcome of these operations because it requires computing the probability of being in any one of the 2^53 possible states, where the 53 comes from the number of qubits — the exponential scaling is why people are interested in quantum computing to begin with,” Foxen said. “This is done by matrix multiplication, which is expensive for classical computers as the matrices become large.”

According to the new paper, the researchers used a method called cross-entropy benchmarking to compare the quantum circuit’s output (a “bitstring”) to its “corresponding ideal probability computed via simulation on a classical computer” to ascertain that the quantum computer was working correctly.

“We made a lot of design choices in the development of our processor that are really advantageous,” said Chiaro. Among these advantages, he said, are the ability to experimentally tune the parameters of the individual qubits as well as their interactions.

While the experiment was chosen as a proof-of-concept for the computer, the research has resulted in a very real and valuable tool: a certified random number generator. Useful in a variety of fields, random numbers can ensure that encrypted keys can’t be guessed, or that a sample from a larger population is truly representative, leading to optimal solutions for complex problems and more robust machine learning applications. The speed with which the quantum circuit can produce its randomized bit string is so great that there is no time to analyze and “cheat” the system.

“Quantum mechanical states do things that go beyond our day-to-day experience and so have the potential to provide capabilities and application that would otherwise be unattainable,” commented Joe Incandela, UC Santa Barbara’s vice chancellor for research. “The team has demonstrated the ability to reliably create and repeatedly sample complicated quantum states involving 53 entangled elements to carry out an exercise that would take millennia to do with a classical supercomputer. This is a major accomplishment. We are at the threshold of a new era of knowledge acquisition.”

Looking ahead

With an achievement like “quantum supremacy,” it’s tempting to think that the UC Santa Barbara/Google researchers will plant their flag and rest easy. But for Foxen, Chiaro, Martinis and the rest of the UCSB/Google AI Quantum group, this is just the beginning.

“It’s kind of a continuous improvement mindset,” Foxen said. “There are always projects in the works.” In the near term, further improvements to these “noisy” qubits may enable the simulation of interesting phenomena in quantum mechanics, such as thermalization, or the vast amount of possibility in the realms of materials and chemistry.

In the long term, however, the scientists are always looking to improve coherence times, or, at the other end, to detect and fix errors, which would take many additional qubits per qubit being checked. These efforts have been running parallel to the design and build of the quantum computer itself, and ensure the researchers have a lot of work before hitting their next milestone.

“It’s been an honor and a pleasure to be associated with this team,” Chiaro said. “It’s a great collection of strong technical contributors with great leadership and the whole team really synergizes well.”

Here’s a link to and a citation for the paper,

Quantum supremacy using a programmable superconducting processor by Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando G. S. L. Brandao, David A. Buell, Brian Burkett, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Andrew Dunsworth, Edward Farhi, Brooks Foxen, Austin Fowler, Craig Gidney, Marissa Giustina, Rob Graff, Keith Guerin, Steve Habegger, Matthew P. Harrigan, Michael J. Hartmann, Alan Ho, Markus Hoffmann, Trent Huang, Travis S. Humble, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Paul V. Klimov, Sergey Knysh, Alexander Korotkov, Fedor Kostritsa, David Landhuis, Mike Lindmark, Erik Lucero, Dmitry Lyakh, Salvatore Mandrà, Jarrod R. McClean, Matthew McEwen, Anthony Megrant, Xiao Mi, Kristel Michielsen, Masoud Mohseni, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles Neill, Murphy Yuezhen Niu, Eric Ostby, Andre Petukhov, John C. Platt, Chris Quintana, Eleanor G. Rieffel, Pedram Roushan, Nicholas C. Rubin, Daniel Sank, Kevin J. Satzinger, Vadim Smelyanskiy, Kevin J. Sung, Matthew D. Trevithick, Amit Vainsencher, Benjamin Villalonga, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Hartmut Neven & John M. Martinis. Nature volume 574, pages505–510 (2019) DOI: https://doi.org/10.1038/s41586-019-1666-5 Issue Date 24 October 2019

This paper appears to be open access.

Google announces research results after testing 1,097-qubit D-Wave 2X™ quantum computers

If you’ve been reading this blog over the last few months, you’ll know that I’ve mentioned D-Wave Systems, a Vancouver (Canada)-based quantum computing company, frequently. The company seems to be signing all kinds of deals lately including one with Google (my Oct. 5, 2015 posting). Well, a Dec. 9, 2015 news item on Nanotechnology Now sheds more light on how Google is using D-Wave’s quantum computers,

Harris & Harris Group, Inc. (NASDAQ: TINY), an investor in transformative companies enabled by disruptive science, notes that yesterday [Dec. 8, 2015] NASA, Google and the Universities Space Research Association (USRA) hosted a tour of the jointly run Quantum Artificial Intelligence Laboratory located at the NASA’s Ames Research Center which houses one of D-Wave’s 1,097-qubit D-Wave 2X™ quantum computers. At this event, Google announced that D-Wave’s quantum computer was able to find solutions to complicated problems of nearly 1,000 variables up to 108 (100,000,000) times faster than classical computers.

A Dec. 8, 2015 posting by Hartmut Neven for the Google Research blog describes the research and the results (Note: Links have been removed),

During the last two years, the Google Quantum AI [artificial intelligence] team has made progress in understanding the physics governing quantum annealers. We recently applied these new insights to construct proof-of-principle optimization problems and programmed these into the D-Wave 2X quantum annealer that Google operates jointly with NASA. The problems were designed to demonstrate that quantum annealing can offer runtime advantages for hard optimization problems characterized by rugged energy landscapes. We found that for problem instances involving nearly 1000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing. It is more than 108 times faster than simulated annealing running on a single core. We also compared the quantum hardware to another algorithm called Quantum Monte Carlo. This is a method designed to emulate the behavior of quantum systems, but it runs on conventional processors. While the scaling with size between these two methods is comparable, they are again separated by a large factor sometimes as high as 108.

For anyone (like me) who needs an explanation of quantum annealing, there’s this from its Wikipedia entry (Note: Links have been removed),

Quantum annealing (QA) is a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete (combinatorial optimization problems) with many local minima; such as finding the ground state of a spin glass.[1] It was formulated in its present form by T. Kadowaki and H. Nishimori in “Quantum annealing in the transverse Ising model”[2] though a proposal in a different form had been proposed by A. B. Finilla, M. A. Gomez, C. Sebenik and J. D. Doll, in “Quantum annealing: A new method for minimizing multidimensional functions”.[3]

Not as helpful as I’d hoped but sometimes its necessary to learn a new vocabulary and a new set of basic principles, which takes time and requires the ability to ‘not know’ and/or ‘not understand’ until one day, you do.

In the meantime, here’s more possibly befuddling information from the researchers in the form of a paper on arXiv.org,

What is the Computational Value of Finite Range Tunneling? by Vasil S. Denchev, Sergio Boixo, Sergei V. Isakov, Nan Ding, Ryan Babbush, Vadim Smelyanskiy, John Martinis, Hartmut Neven. http://arxiv.org/abs/1512.02206

This paper is open access.

D-Wave passes 1000-qubit barrier

A local (Vancouver, Canada-based, quantum computing company, D-Wave is making quite a splash lately due to a technical breakthrough.  h/t’s Speaking up for Canadian Science for Business in Vancouver article and Nanotechnology Now for Harris & Harris Group press release and Economist article.

A June 22, 2015 article by Tyler Orton for Business in Vancouver describes D-Wave’s latest technical breakthrough,

“This updated processor will allow significantly more complex computational problems to be solved than ever before,” Jeremy Hilton, D-Wave’s vice-president of processor development, wrote in a June 22 [2015] blog entry.

Regular computers use two bits – ones and zeroes – to make calculations, while quantum computers rely on qubits.

Qubits possess a “superposition” that allow it to be one and zero at the same time, meaning it can calculate all possible values in a single operation.

But the algorithm for a full-scale quantum computer requires 8,000 qubits.

A June 23, 2015 Harris & Harris Group press release adds more information about the breakthrough,

Harris & Harris Group, Inc. (Nasdaq: TINY), an investor in transformative companies enabled by disruptive science, notes that its portfolio company, D-Wave Systems, Inc., announced that it has successfully fabricated 1,000 qubit processors that power its quantum computers.  D-Wave’s quantum computer runs a quantum annealing algorithm to find the lowest points, corresponding to optimal or near optimal solutions, in a virtual “energy landscape.”  Every additional qubit doubles the search space of the processor.  At 1,000 qubits, the new processor considers 21000 possibilities simultaneously, a search space which is substantially larger than the 2512 possibilities available to the company’s currently available 512 qubit D-Wave Two. In fact, the new search space contains far more possibilities than there are particles in the observable universe.

A June 22, 2015 D-Wave news release, which originated the technical details about the breakthrough found in the Harris & Harris press release, provides more information along with some marketing hype (hyperbole), Note: Links have been removed,

As the only manufacturer of scalable quantum processors, D-Wave breaks new ground with every succeeding generation it develops. The new processors, comprising over 128,000 Josephson tunnel junctions, are believed to be the most complex superconductor integrated circuits ever successfully yielded. They are fabricated in part at D-Wave’s facilities in Palo Alto, CA and at Cypress Semiconductor’s wafer foundry located in Bloomington, Minnesota.

“Temperature, noise, and precision all play a profound role in how well quantum processors solve problems.  Beyond scaling up the technology by doubling the number of qubits, we also achieved key technology advances prioritized around their impact on performance,” said Jeremy Hilton, D-Wave vice president, processor development. “We expect to release benchmarking data that demonstrate new levels of performance later this year.”

The 1000-qubit milestone is the result of intensive research and development by D-Wave and reflects a triumph over a variety of design challenges aimed at enhancing performance and boosting solution quality. Beyond the much larger number of qubits, other significant innovations include:

  •  Lower Operating Temperature: While the previous generation processor ran at a temperature close to absolute zero, the new processor runs 40% colder. The lower operating temperature enhances the importance of quantum effects, which increases the ability to discriminate the best result from a collection of good candidates.​
  • Reduced Noise: Through a combination of improved design, architectural enhancements and materials changes, noise levels have been reduced by 50% in comparison to the previous generation. The lower noise environment enhances problem-solving performance while boosting reliability and stability.
  • Increased Control Circuitry Precision: In the testing to date, the increased precision coupled with the noise reduction has demonstrated improved precision by up to 40%. To accomplish both while also improving manufacturing yield is a significant achievement.
  • Advanced Fabrication:  The new processors comprise over 128,000 Josephson junctions (tunnel junctions with superconducting electrodes) in a 6-metal layer planar process with 0.25μm features, believed to be the most complex superconductor integrated circuits ever built.
  • New Modes of Use: The new technology expands the boundaries of ways to exploit quantum resources.  In addition to performing discrete optimization like its predecessor, firmware and software upgrades will make it easier to use the system for sampling applications.

“Breaking the 1000 qubit barrier marks the culmination of years of research and development by our scientists, engineers and manufacturing team,” said D-Wave CEO Vern Brownell. “It is a critical step toward bringing the promise of quantum computing to bear on some of the most challenging technical, commercial, scientific, and national defense problems that organizations face.”

A June 20, 2015 article in The Economist notes there is now commercial interest as it provides good introductory information about quantum computing. The article includes an analysis of various research efforts in Canada (they mention D-Wave), the US, and the UK. These excerpts don’t do justice to the article but will hopefully whet your appetite or provide an overview for anyone with limited time,

A COMPUTER proceeds one step at a time. At any particular moment, each of its bits—the binary digits it adds and subtracts to arrive at its conclusions—has a single, definite value: zero or one. At that moment the machine is in just one state, a particular mixture of zeros and ones. It can therefore perform only one calculation next. This puts a limit on its power. To increase that power, you have to make it work faster.

But bits do not exist in the abstract. Each depends for its reality on the physical state of part of the computer’s processor or memory. And physical states, at the quantum level, are not as clear-cut as classical physics pretends. That leaves engineers a bit of wriggle room. By exploiting certain quantum effects they can create bits, known as qubits, that do not have a definite value, thus overcoming classical computing’s limits.

… The biggest question is what the qubits themselves should be made from.

A qubit needs a physical system with two opposite quantum states, such as the direction of spin of an electron orbiting an atomic nucleus. Several things which can do the job exist, and each has its fans. Some suggest nitrogen atoms trapped in the crystal lattices of diamonds. Calcium ions held in the grip of magnetic fields are another favourite. So are the photons of which light is composed (in this case the qubit would be stored in the plane of polarisation). And quasiparticles, which are vibrations in matter that behave like real subatomic particles, also have a following.

The leading candidate at the moment, though, is to use a superconductor in which the qubit is either the direction of a circulating current, or the presence or absence of an electric charge. Both Google and IBM are banking on this approach. It has the advantage that superconducting qubits can be arranged on semiconductor chips of the sort used in existing computers. That, the two firms think, should make them easier to commercialise.

Google is also collaborating with D-Wave of Vancouver, Canada, which sells what it calls quantum annealers. The field’s practitioners took much convincing that these devices really do exploit the quantum advantage, and in any case they are limited to a narrower set of problems—such as searching for images similar to a reference image. But such searches are just the type of application of interest to Google. In 2013, in collaboration with NASA and USRA, a research consortium, the firm bought a D-Wave machine in order to put it through its paces. Hartmut Neven, director of engineering at Google Research, is guarded about what his team has found, but he believes D-Wave’s approach is best suited to calculations involving fewer qubits, while Dr Martinis and his colleagues build devices with more.

It’s not clear to me if the writers at The Economist were aware of  D-Wave’s latest breakthrough at the time of writing but I think not. In any event, they (The Economist writers) have included a provocative tidbit about quantum encryption,

Documents released by Edward Snowden, a whistleblower, revealed that the Penetrating Hard Targets programme of America’s National Security Agency was actively researching “if, and how, a cryptologically useful quantum computer can be built”. In May IARPA [Intellligence Advanced Research Projects Agency], the American government’s intelligence-research arm, issued a call for partners in its Logical Qubits programme, to make robust, error-free qubits. In April, meanwhile, Tanja Lange and Daniel Bernstein of Eindhoven University of Technology, in the Netherlands, announced PQCRYPTO, a programme to advance and standardise “post-quantum cryptography”. They are concerned that encrypted communications captured now could be subjected to quantum cracking in the future. That means strong pre-emptive encryption is needed immediately.

I encourage you to read the Economist article.

Two final comments. (1) The latest piece, prior to this one, about D-Wave was in a Feb. 6, 2015 posting about then new investment into the company. (2) A Canadian effort in the field of quantum cryptography was mentioned in a May 11, 2015 posting (scroll down about 50% of the way) featuring a profile of Raymond Laflamme, at the University of Waterloo’s Institute of Quantum Computing in the context of an announcement about science media initiative Research2Reality.