Strategy

Strategy: Use Your Quantum Computer Lab to Tell Intentional Blinks from Involuntary Blinks

High Scalability

Oct 30, 2013 — 2 min read

Oh, you don't have a Quantum Computer Lab staffed with researchers? Well, Google does. Here they are on G+. To learn what they are up to the Verge has A first look inside Google's futuristic quantum lab. The lab is partnership between NASA, Google, and a 512-qubit D-Wave Two quantum computer.

One result from the lab is:

The first practical application has been on Google Glass, as engineers put the quantum chips to work on Glass's blink detector, helping it to better distinguish between intentional winks and involuntary blinks. For engineering reasons, the quantum processor can never be installed in Glass, but together with Google's conventional server centers, it can point the way to a better blink-detecting algorithm. That would allow the Glass processor to detect blinks with better accuracy and using significantly less power. If successful, it could be an important breakthrough for wink-triggered apps, which have struggled with the task so far.

Google thinks quantum computing has a major role in machine learning:

We believe quantum computing may help solve some of the most challenging computer science problems, particularly in machine learning. Machine learning is all about building better models of the world to make more accurate predictions. If we want to cure diseases, we need better models of how they develop. If we want to create effective environmental policies, we need better models of what’s happening to our climate. And if we want to build a more useful search engine, we need to better understand spoken questions and what’s on the web so you get the best answer.

We’ve already developed some quantum machine learning algorithms. One produces very compact, efficient recognizers -- very useful when you’re short on power, as on a mobile device. Another can handle highly polluted training data, where a high percentage of the examples are mislabeled, as they often are in the real world. And we’ve learned some useful principles: e.g., you get the best results not with pure quantum computing, but by mixing quantum and classical computing

OK, I know this is not nearly as cool as a new iPad, but it is pretty cool, it being the future and all.

Johnathan Chung has a comment nicely summing up what it all means:

I thought it was pretty amazing in John Preskill's talk to hear about the comparison of a classical vs quantum computer for what it would take to factor a 2048-bit number.

He said the classical algorithm would require a server farm covering 1/4 of the land in North America and cost a million-trillion dollars consuming 100,000 times the current energy output of the entire world, which is the equivalent of using the world's supply of fossil fuels in a single day -- and it would still take 10 years to do it.

He continued to contrast that with a quantum algorithm using current technology, which would require 10 trillion times less energy (at 10 megawatts), cost ~100 billion dollars at current prices (which needs to be lowered over time), and finish in just 16 hours.

Improving logistics, scalability, execution, and cost are still a ways off in the future, but its exciting to know it will open up a whole new area of problem-solving capability and advancements, including aspects we haven't even thought of yet.

Perhaps the most interesting part of what was talked about is that Google, NASA, and D-Wave admit that the hardest challenge is knowing what to ask the computer. The same issue one has when wondering what to ask a Genie for your three wishes.