Quantum Computing for Google Goggles
Kicking off the D-Wave Board meeting over lunch today at Goldman Sachs… with new news from Google that they demonstrated the use of D-Wave’s quantum computer to deliver photo-driven search (and improve on classical machine learning).
Here is a summary from the Google Reseach blog:
“Many Google services we offer depend on sophisticated artificial intelligence technologies such as machine learning or pattern recognition. If one takes a closer look at such capabilities one realizes that they often require the solution of what mathematicians call hard combinatorial optimization problems. It turns out that solving the hardest of such problems requires server farms so large that they can never be built. A new type of machine, a so-called quantum computer, can help here.
Today, at the Neural Information Processing Systems conference (NIPS 2009), we show the progress we have made. We demonstrate a detector that has learned to spot cars by looking at example pictures. It was trained with adiabatic quantum optimization using a D-Wave C4 Chimera chip. There are still many open questions but in our experiments we observed that this detector performs better than those we had trained using classical solvers running on the computers we have in our data centers today. Besides progress in engineering synthetic intelligence we hope that improved mastery of quantum computing will also increase our appreciation for the structure of reality as described by the laws of quantum physics.”
Quantum Computing for Google Goggles
Kicking off the D-Wave Board meeting over lunch today at Goldman Sachs… with new news from Google that they demonstrated the use of D-Wave’s quantum computer to deliver photo-driven search (and improve on classical machine learning).
Here is a summary from the Google Reseach blog:
“Many Google services we offer depend on sophisticated artificial intelligence technologies such as machine learning or pattern recognition. If one takes a closer look at such capabilities one realizes that they often require the solution of what mathematicians call hard combinatorial optimization problems. It turns out that solving the hardest of such problems requires server farms so large that they can never be built. A new type of machine, a so-called quantum computer, can help here.
Today, at the Neural Information Processing Systems conference (NIPS 2009), we show the progress we have made. We demonstrate a detector that has learned to spot cars by looking at example pictures. It was trained with adiabatic quantum optimization using a D-Wave C4 Chimera chip. There are still many open questions but in our experiments we observed that this detector performs better than those we had trained using classical solvers running on the computers we have in our data centers today. Besides progress in engineering synthetic intelligence we hope that improved mastery of quantum computing will also increase our appreciation for the structure of reality as described by the laws of quantum physics.”