At 2000 to 4000 qubits, Dwave Systems adiabatic quantum computer should become faster than classical computers for discrete optimization problems. Dwave should reach that level of qubits by about 2015.
Steve Jurvetson Describes How the Scaling Works if the Early Data Extrapolates
If we suspend disbelief for a moment, and use D-Wave’s early data on processing power scaling (see below), then the very near future should be the watershed moment, where quantum computers surpass conventional computers and never look back. Moore’s Law cannot catch up. A year later, it outperforms all computers on Earth combined. Double qubits again the following year, and it outperforms the universe. What the???? you may ask… Meaning, it could solve certain problems that could not be solved by any non-quantum computer, even if the entire mass and energy of the universe was at its disposal and molded into the best possible computer.
It is a completely different way to compute — as David Deutsch posits — harnessing the refractive echoes of many trillions of parallel universes to perform a computation.
First the caveat (the text in white letters on the graph). D-Wave has not built a general-purpose quantum computer. Think of it as an application-specific processor, tuned to perform one task — solving discrete optimization problems. This happens to map to many real world applications, from finance to molecular modeling to machine learning, but it is not going to change our current personal computing tasks. In the near term, assume it will apply to scientific supercomputing tasks and commercial optimization tasks where a heuristic may suffice today, and perhaps it will be lurking in the shadows of an Internet giant’s data center improving image recognition and other forms of near-AI magic. In most cases, the quantum computer would be an accelerating coprocessor to a classical compute cluster.
Second, the assumptions. There is a lot of room for surprises in the next three years. Do they hit a scaling wall or discover a heretofore unknown fracturing of the physics… perhaps finding local entanglement, noise, or some other technical hitch that might not loom large at small scales, but grows exponentially as a problem just as the theoretical performance grows exponentially with scale. I think the risk is less likely to lie in the steady qubit march, which has held true for a decade now, but in the relationship of qubit count to performance.
Proofs of Quantumness
Arxiv – Experimental signature of programmable quantum annealing. Quantum annealing is a general strategy for solving diﬃcult optimization problems with the aid of quantum adiabatic evolution. Both analytical and numerical evidence suggests that under idealized, closed system conditions, quantum annealing can outperform classical thermalization-based algorithms such as simulated annealing. Do engineered quantum annealing devices eﬀectively perform classical thermalization when coupled to a decohering thermal environment? To address this we establish, using superconducting ﬂux qubits with programmable spin-spin couplings, an experimental signature which is consistent with quantum annealing, and at the same time inconsistent with classical thermalization, in spite of a decoherence timescale which is orders of magnitude shorter than the adiabatic evolution time. This suggests that programmable quantum devices, scalable with current superconducting technology, implement quantum annealing with a surprising robustness against noise and imperfections.
Patents and Early Research Efficiency
3 months ago, D-Wave holds 93 U.S. patents and has 107 patent applications under way globally. Its IP portfolio will make it very difficult for competitors to design a similar machine, at least for 15 years or so, Dr. Rose predicts.
When D-Wave launched, the field of quantum computing was largely theoretical, Dr. Rose recalls. “It was very early, but it wasn’t so early that the science hadn’t been in some ways proven out,” he says. “There were enough results that had been garnered from the scientific study of these things to think that there was nothing written in the laws of physics that prevented you from trying to build one.” So he and his colleagues mapped out the couple of dozen areas that someone would need to understand in order to build a real quantum computer, from the user experience to the physical devices inside the chips. Then they looked for scientists around the world with expertise in those areas and asked if they would be interested in helping D-Wave.
The company gave its researchers funding and access to the rest of the network it was building. In exchange, it got control of the intellectual property they produced and the right to file patents before they published their findings.
Over the next five years, D-Wave’s network expanded to include groups with ties to 10 academic institutions in Canada, Germany, the Netherlands, the Slovak Republic, Sweden, Britain and the Ukraine. As early as 2001, the start-up had access to $440-million worth of equipment.
D-Wave took an entrepreneurial approach to running tests that would otherwise have been very expensive, says Ajay Agrawal, Peter Munk Professor of Entrepreneurship at the University of Toronto’s Rotman School of Management. “If you had all of the equipment in-house, you’d have to be a very large company, like an IBM,” explains Dr. Agrawal, who co-authored a 2004 Harvard Business School case study on D-Wave. “So how does an entrepreneur do it? By leveraging the assets that are out there and in many cases underutilized, and rather than paying the full cost, paying only the marginal cost.”
SOURCES- Steve Jurvetson on Flickr, Globe and Mail, Nature journal, Arxiv