Intel Quantum AI Breakthrough

We wouldn't use the word "breakthrough" in the headline except for the fact the writer of this piece, Tiernan Ray seems to think this is a big deal. Long-time readers may remember Mr. Ray from his days at Barron's where he ran the Tech Trader and Tech Trader Daily outposts and for his uncanny ability to get NVIDIA's CEO to answer questions.

From ZD Net, March 14:

Intel offers AI breakthrough in quantum computing

Intel's senior vice president and head of Mobileye, Amnon Shashua, on Wednesday unveiled new research done with colleagues at Hebrew University that both establishes important proof for capabilities of deep learning, and also offers a way forward for computing some commonly intractable problems in quantum physics.

We don't know why deep learning forms of neural networks achieve great success on many tasks; the discipline has a paucity of theory to explain its empirical successes. As Facebook's Yann LeCun has said, deep learning is like the steam engine, which preceded the underlying theory of thermodynamics by many years.   

But some deep thinkers have been plugging away at the matter of theory for several years now. 

On Wednesday, the group presented a proof of deep learning's superior ability to simulate the computations involved in quantum computing. According to these thinkers, the redundancy of information that happens in two of the most successful neural network types, convolutional neural nets, or CNNs, and recurrent neural networks, or RNNs, makes all the difference. 

Amnon Shashua, who is the president and chief executive of Mobileye, the autonomous driving technology company bought by chip giant Intel last year for $14.1 billion, presented the findings on Wednesday at a conference in Washington, D.C. hosted by The National Academy of Sciences called the Science of Deep Learning Conference. 

In addition to being a senior vice president at Intel, Shashua is a professor of computer science at the Hebrew University in Jerusalem, and the paper is co-authored with colleagues from there, Yoav Levine, the lead author, Or Sharir, and with Nadav Cohen of the Institute for Advanced Study in Princeton, New Jersey. 

The report, "Quantum Entanglement in Deep Learning Architectures," was published this week in the prestigious journal Physical Review Letters.  

The work amounts to both a proof of certain problems deep learning can excel at, and at the same time a proposal for a  promising way forward in quantum computing.  

The team of Amnon Shashua and colleagues created a "CAC," or, "convolutional arithmetic circuit," which replicates the re-use
of information in a traditional CNN, while making it work with the "Tensor Network" models commonly used in physics, Mobileye.

In quantum computing, the problem is somewhat the reverse of deep learning: lots of compelling theory, but as yet few working examples of the real thing. For many years, Shashua and his colleagues, and others, have pondered how to simulate quantum computing of the so-called many-body problem. 

Physicist Richard Mattuck has defined the many-body problem as "the study of the effects of interaction between bodies on the behaviour of a many-body system," where bodies have to do with electrons, atoms, molecules, or various other entities.

What Shashua and team found, and what they say they've proven, is that CNNs and RNNs are better than traditional machine learning approaches such as the "Restricted Boltzmann Machine," a neural network approach developed in the 1980s that has been a mainstay of physics research, especially quantum theory simulation...MORE