This blog have been posting quite heavily on the topics of Machine Learning and Quantum Computing. It is obviously related with the increased interest in those fields, both from the academic community and the business community, and for good reasons, as such fields of study keep showing signs of promising breakthroughs. This will potentially unleashing innovative new research major events, as well as new business or commercial applications.
And this is a blog about broader issues in Information Technology and Computing. These topics are deeply related with Machine Learning and Quantum Computing. Quantum Computing has revealed in the recent past years to be a cutting-edge area of Computing in general. But what is not often mentioned is that it is based in Quantum Physics and generally in Physics’ topics such as Condensed Matter Physics.
Having said this I would like to share with the viewers of this blog a post that I had the pleasure to read in the website Motherboard. This website presents a nice collection of posts about numerous scientific and technological issues in a modern appealing design setting. The post is titled:
it is a good article about current developments in applying Machine Learning techniques and algorithms to enhance understanding of Quantum Physics, Condensed Matter Physics and ultimately Quantum Computing.
It is appropriately pointed in the article the motivation for trying to apply Machine Learning to Quantum Physics problems: the sheer complexity of even small Quantum systems renders the techniques of conventional mathematics/computer science unable to sort it out, forever covering a better understanding of those systems. Machine Learning techniques such as Deep Reinforcement Learning and deep convolutional neural networks might aid in this because of their ability to accept large and complex data sets, and perform by themselves computations with many layers of complexity:
“The thing about quantum physics is it’s highly complex in a very precise mathematical sense. A big problem we face when we study these quantum systems [without machine learning] is how to deal with this complexity,” Melko told me.
“DeepMind winning this game of Go kind of crystallized some of our thinking. Go is a very complex game, but there was a solution that came from machine learning,” he continued. “So we thought, why can’t we employ similar solutions to tackle quantum physics complexity problems?”
As an example, Melko cites his own work, which focuses on condensed matter physics—basically the science of interactions between many quantum particles in various solids or liquids. As Melko wrote in a recent article for Quartz, condensed matter physics “deals with the most complex concept in nature: the quantum wave function of a many-particle system.” The quantum wave function of a particle mathematically describes all of its possible states, or as Melko describes it to me, it is the “infinitely complex…reality of the particle.”
To get a sense about the complexity of Quantum systems this passage conveys the message perfectly:
While “infinitely complex” might seem like a bit of an overstatement, according to Melko, just modeling the wave function of a nanometer-scale mote of dust would require a computer whose hard drive contained more magnetic bits than there are atoms in the universe. As for trying to compute the wave functions of several of these dust particles at once with a classical computer? Forget about it.
Quantum wave-funtion complexity have proved an insurmountable computational task for conventional computers without machine learning algorithms. Researchers began to ponder the possibility of algorithms with embedded machine learning techniques would fare better. They were in for a very positive surprise, indeed:
The question posed by Melko and other pioneers of the field of quantum machine learning was whether neural nets could perform tasks that are beyond the capacity of algorithms which don’t incorporate machine learning, like modeling the wave function of a multi-particle system—and they didn’t have to wait long for an answer.
According to a study published last week in Science, two physicists that were not affiliated with Melko created a relatively simple neural network that was able to reconstruct the wave function of a multi-particle system, and it did so better than any previous technique that did not use machine learning. As Giuseppe Carleo, a physicist at ETH Zurich and co-author of the study, told New Scientist, “It’s like having a machine learning how to crack quantum mechanics, all by itself.”
“Condensed matter has its own set of benchmark problems and there are parts of our theories that we don’t understand,” Melko said. “So we applied machine learning to standard problems of condensed matter physics that we might already have solutions for, basically to see how neural networks handle these high levels of complexity that happen in condensed matter.”
The main technique in this part of Melko’s research efforts is the inevitable deep convolutional neural network of image classification fame and state-of-the-art:
As detailed in a paper published Monday in Nature Physics, Melko’s neural network is only a slightly modified version of an AI software used to identify numbers written by humans. Remarkably, this relatively basic machine learning algorithm was nevertheless capable of recognizing different phases of matter in a quantum system, with minimal adjustments. After running the algorithm on some standard condensed matter problems, Melko ratcheted up the complexity of what was being fed to the machine learning algorithm to see how far he could push it before it broke.
“We can’t fully mathematically describe the wave function, it’s too complex,” Melko said. “But by studying how algorithms respond to different complexities of condensed matter problems, we’re also studying what makes these algorithms tick. That is what the field of quantum machine learning is, I think: trying to understand whether or not machine learning helps us in some fundamental way on these quantum problems.”
And this is the main message I wanted to convey to the readers: the kind of mutual beneficial relationship between two strands of research & development that might lead in the end to the enhancement of both fields. And, not least, the holistic final result may well be breakthroughs impacting other fields or finding business applications. The setting of a perfect partnership.
Both Melko and Carleo’s studies bode well for the future of quantum machine learning. As demonstrated by a conference hosted by the Perimeter Institute last year on the subject, there is already quite a bit of interest in how machine learning can be applied to quantum physics—the conference was attended by leading academics in the fields of quantum physics and artificial intelligence, as well as researchers from companies like Google and Intel.
“Before that conference we had never thought about applying machine learning to these many-body quantum physics problems,” Melko said. “But now people are running with the idea of using neural networks to create efficient representations of quantum systems. This field is just starting to take off.”
Moreover, advances in machine learning may very well lead to advances in quantum computing. Although we are still years away from building the first large-scale quantum computer, the computing power of such a device would revolutionize machine learning, which could in turn improve quantum systems.
“The ultimate goal of this type of machine learning is to help the scientific and industrial fabrication process of a quantum computer,” said Melko. “We’re in the early stages because we don’t have the hardware, but in the future if you have the quantum computer hardware, you could potentially solve some very complex problems with a quantum computer built by an intelligent machine. It’s fascinating: we’re basically bootstrapping ourselves into this world of quantum computing.”
Further new episodes and stories on this exciting partnership might come sooner rather than later. The Information Age is here to witness, comment and…tentively providing insights and directions.
featured image: What can we expect from quantum machine learning?