Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that simulating physical systems could be done most effectively on quantum computers. In this instance, the project was the simulation of a quantum magnetism problem called the transverse field Ising model (TFIM) that has potential practical application in materials science research.

Using a standard D-Wave 2,048-quibit processor, the researchers simulated interacting Ising spins on 3D cubic lattices up to dimensions of 8x8x8. In some sense, the lattice represents an imaginary ‘substance’ comprised solely of magnetic moments; put another way, you are simulating correlated electron systems.

As the authors explain, “By tuning the amount of disorder within the lattice and varying the effective transverse magnetic field, we demonstrate phase transitions between a paramagnetic (PM), an ordered anti-ferromagnetic (AFM), and a spin-glass (SG) phase. The experimental results compare well with theory for this particular SG problem, thus validating the use of a probabilistic quantum computer to simulate materials physics. This represents an important step forward in the realization of integrated quantum circuits at a scale that is relevant for condensed matter research.”

In essence they fiddled with the simulation dials to watch how nature would unfold under different conditions. Using D-Wave’s quantum annealing technology meant, in effect, that each simulation evolved just as it would naturally. D-Wave’s usual programming tools were used.

At least one observer calls the research ground-breaking. “Characterization of the phase behavior of a genuinely new material not found in nature by a precisely controlled quantum computer used as a simulator…[is] the first truly useful application of a quantum computer. [I]t shows us how to explore the behavior of novel system designs without having to completely understand them first, as we must to write a useful digital simulation code,” said Ned Allen, chief scientist and corporate senior fellow at Lockheed Martin – admittedly a D-Wave customer – in the official announcement.

D-Wave CEO Vern Brownell told HPCwire, “One of the slight nuances here is in order to do this type of modeling you actually have to take advantage of the quantum mechanical effects of the machine. If you were to simulate this on a classical machine like a large HPC cluster, the only way to do that is to simulate the quantum mechanics and there are ways to do that; Monte Carlo simulation is probably the most common way of doing that. That’s incredibly intensive computationally. The advantage that this machine has is actually leveraging those quantum mechanical effects to do a more efficient modeling.”

D-Wave, of course, has been in the thick of the race to develop quantum computers. Its approach – quantum annealing – has advocates and skeptics. Unlike a traditional gate model, D-Wave system architecture relies on the tendency of quantum systems to find low-energy states. Here’s the company’s summary for its most current machine:

A lattice of 2,000 tiny superconducting devices, known as qubits, is chilled close to absolute zero to harness quantum effects.

A user models a problem into a search for the “lowest energy point in a vast landscape”.

The processor considers all possibilities simultaneously to determine the lowest energy and the values that produce it.

Multiple solutions are returned to the user, scaled to show optimal answers.

In last week’s paper (Phase transitions in a programmable quantum spin glass simulator), researchers emphasized, “[The] structure of the magnetic system studied was vastly different from the physical layout of qubits within the QPU.”

Said Brownell, “There are certainly many ways you can build a quantum computer. You can build quantum annealers [like] we build. You can build a gate model, which is what most of the other large companies are trying to build. Then there’s a topological model which Microsoft is trying to build. They’re all quantum computers. The differences are the relative exposure or susceptibility to error. The gate model to quantum computing is the most susceptible to errors, so you’ll need tens of thousands of qubits to simulate one logical qubit and there’s a huge overhead to that. That’s why gate model computers are 5- or 10- or 15 years away from being able to do useful applications. Certainly very far away from the scale of being able to do anything like what we have demonstrated here. Maybe a decade away.”

No doubt D-Wave’s rivals would disagree. To a significant extent D-Wave has always been a small player jostling with giants. It’s often received faint praise designed to spotlight perceived weaknesses of its quantum annealing technology. That hasn’t stopped the Canada-based quantum computing pioneer from punching above its weight in terms of actually selling systems (Lockheed and NASA, for example). The company is perhaps understandably sensitive to criticism.

Brownell points to a report from Jülich Supercomputing Center, Germany, presented at a D-Wave User meeting last April. “They use IBM’s and our system and have done a comparison. On a scale of 1-to-9 – what they call the quantum technology readiness (QTR, detailed at end of article) – we are at level 8 and they have IBM at 5 along with Google and pretty much everybody else in quantum computing. It’s good to see these reports. There’s a lot of talk from the other folks and a lot of bluster about what their quantum computers can do, but here they have to expose their quantum computers to third party scrutiny and people can now make fair comparisons.”

The first D-Wave system was a 128-qubit machine introduced in 2010 with larger systems introduced roughly every two years. The current state of the art is the D-Wave 2000Q, announced in September 2016 and officially launched in early 2017. While a new machine is not expected soon, Brownell promises more important news towards the end of the summer, likely a large-scale cloud program and new tools. He also said another landmark paper is in the works.

Given the tremendous noise surrounding quantum computing currently Brownell is determined that D-Wave not be lost in the din. Earlier this month, D-Wave hired Jennifer Houston as SVP, marketing. “We had effectively no marketing or very little marketing going on,” said Brownell. A year ago, the company hired Alan Baratz as SVP of software and applications. Previously president of JavaSoft (Sun Microsystems), Baratz is charged with ecosystem development and presumably we will see the fruits of his efforts in the cloud/tool rollout.

Last week’s paper, though important, doesn’t mean quantum computing of any sort is suddenly ready for real-world materials science applications. Brownell agreed, “It’s certainly scientifically relevant to materials science research but you would have to work with very deep scientists in order to take advantage of this capability. [But] it is the start of the ability to use a quantum computer to do something useful.”

Jülich Quantum Computing Technology Readiness Level (source: Forschungszentum Jülich)

A quantum computing technology is at QTRL1 when the theoretical framework for quantum computing (annealing) is formulated. Theoretical studies of the basic properties of the quantum computing (annealing) devices move towards applied research and development. The technology reaches QTRL2 once the basic device principles have been studied and applications or technologically relevant algorithms are formulated. QTRL2 quantum computing technology is speculative, as there are little to no experimental results supporting the theoretical studies.

Fabricated imperfect physical qubits, the basic building blocks of quantum computing devices, are at QTRL3. Laboratory studies aim to validate theoretical predictions of qubit properties. Theoretical and laboratory studies are required to determine whether these basic elements of the quantum computing technology are ready to proceed further through the development process.

During QTRL4, multi-qubit systems are fabricated and classical devices for qubit manipulation are developed. Both components of the quantum computing technology are tested with one another. QTRL5 quantum computing technology comprises components integrated in a small quantum processor without error correction. Quantum computing devices labeled as QTRL5 must undergo rigorous testing including running of various algorithms for benchmarking. Components integrated in a small quantum processor with error correction are at QTRL6. Rigorous testing and running algorithms is repeated for the QTRL6 quantum computing technology.

QTRL7 quantum computing technology is a prototype quantum computer (annealer) solving small but user-relevant problems. The prototype is demonstrated in a user environment. A scalable version of a quantum computer (annealer) completed and qualified through test and demonstration is at QTRL8. Once quantum computers (annealers) exceed the computational power of classical computers for general (specific) problems the quantum computing technology can be labeled with QTRL9.

Link to paper: http://science.sciencemag.org/content/361/6398/162

Link to release: https://www.dwavesys.com/press-releases/d-wave-demonstrates-large-scale-programmable-quantum-simulation