Google: Our quantum computer is 100 million times faster than a conventional system
Google: Our quantum computer is 100 million times faster than a conventional system
By Joel Hruska
Ever since quantum computer manufacturer D-Wave announced that it had created an actual system, there have been skeptics.
The primary concern was that D-Wave hadn’t built a quantum computer as
such, but instead constructed a system that happened to simulate
a quantum annealer — one specific type of quantum computing that the
D-Wave performs — more effectively than any previous architecture.
Earlier reports suggested this was untrue,
and Google has now put such fears to rest. The company has presented
findings conclusively demonstrating the D-Wave does perform quantum
annealing, and is capable of solving certain types of problems up to 100
million times faster than conventional systems. Over the past two
years, D-Wave and Google have worked together to test the types of
solutions that the quantum computer could create and measure its
performance against traditional CPU and GPU compute clusters. In a new blog post,
Hartmut Neven, director of engineering at Google, discusses the
proof-of-principle problems the company designed and executed to
demonstrate “quantum annealing can offer runtime advantages for hard
optimization problems characterized by rugged energy landscapes.” He
writes:
“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.”
Neven
goes on to write that while these results prove, unequivocally, that
D-Wave is capable of performance that no modern system can match —
optimizations are great and all, but a 100-million speed-up is tough to
beat in software — the practical impact of these optimizations is
currently limited. The problem with D-wave’s current generation of
systems is that they are sparsely connected. This is illustrated in the
diagram below:
D-Wave 2’s connectivity tree
Each
dot in this diagram represents a qubit; the number of qubits in a
system controls the size and complexity of the problems it can perform.
While each group of qubits is cross-connected, there are relatively few
connections between the groups of qubits. This limits the kinds of
computation that the D-Wave 2 can perform, and simply scaling out to more sparsely-connected qubit clusters
isn’t an efficient way to solve problems (and can’t work in all cases).
Because of this, simulated annealing — the version performed by
traditionally designed CPUs and GPUs — is still regarded as the gold
standard that quantum annealing needs to beat.
How much of an
impact quantum computing will have on traditional markets is still
unknown. Current systems rely on liquid nitrogen cooling and incredibly
expensive designs. Costs may drop as new techniques for building more
efficient quantum annealers are discovered, but so long as these systems
require NO2 to function, they’re not going to be widely used. Google,
NASA, the NSA, and other supercomputer clusters may have specialized
needs that are best addressed by quantum computing
in the long-term, but silicon and its eventual successors are going to
be the mainstay of the general-purpose computing market for decades to
come. At least for now, quantum computers will tackle the problems our
current computers literally can’t solve — or can’t solve before the heat-death of the universe, which is more-or-less the same thing.
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