A computer science research group from the Hebrew University of Jerusalem has mathematically proven that artificial intelligence (AI) can help us understand quantum physics phenomena.

Intel Senior Vice President and Mobileye CEO Prof. Amnon Shashua shared the group’s findings during a keynote Wednesday at the Science of Deep Learning Conference hosted by The National Academy of Sciences in Washington, D.C.

Despite the surge of AI across nearly every industry, it has not been widely applied to the world of quantum physics. Doctoral students Yoav Levine, Or Sharir and Nadav Cohen, led by Shashua, aim to change that by proving how recent developments in machine learning can help us study some computationally difficult areas of quantum physics. Using the latest advancement in deep neural networks to conduct proper simulations faster and more thoroughly, these researchers argue, will provide new insight into the smallest of particles and how they interact.

A deeper view into this area of physics has the potential to unlock the next revolutions in computing, energy and transportation.

The team showed that algorithms based on deep neural networks – algorithms that have revolutionized AI – can be applied to the world of quantum physics. These algorithms, which have already endowed computers with facial- and voice-recognition capabilities, will now be able to refine our understanding of the quantum behavior of nature.

Understanding phenomena in systems of many interacting quantum particles (particles of miniscule size, such as electrons) is one of the most popular and intriguing subfields in current physics research. It studies how particles in nature “come together” and bring forth surprising properties, such as electrical conductivity and magnetism, among others. As has happened in the technological revolutions of the 20th century, a deeper understanding in this domain can greatly affect various aspects of modern life, as it bears potential to enable the next revolutions in computing, energy, transportation – and the limits can only be imagined. The connection of AI to this field promises fascinating developments in upcoming years.

Machine Learning on Path to Quantum Advantage

There are high hopes that quantum computing’s tremendous processing power will someday unleash exponential advances in artificial intelligence. AI systems thrive when the machine learning algorithms used to train them are given massive amounts of data to ingest, classify and analyze. The more precisely that data can be classified according to specific characteristics, or features, the better the AI will perform.

In a new Nature research paper entitled “Supervised learning with quantum enhanced feature spaces,” researchers from IBM describe developing and testing a quantum algorithm with the potential to enable machine learning on quantum computers in the near future. The researchers have shown that as quantum computers become more powerful in the years to come, and their Quantum Volume increases, they will be able to perform feature mapping, a key component of machine learning, on highly complex data structures at a scale far beyond the reach of even the most powerful classical computers.

Their methods were also able to classify data with the use of short-depth circuits, which opens a path to dealing with decoherence. Just as significantly, their feature-mapping worked as predicted: no classification errors with our engineered data, even as the IBM Q systems’ processors experienced decoherence.

Feature mapping is a way of disassembling data to get access to finer-grain aspects of that data. Both classical and quantum machine learning algorithms can break down a picture, for example, by pixels and place them in a grid based on each pixel’s color value. From there the algorithms map individual data points non-linearly to a high-dimensional space, breaking the data down according to its most essential features. In the much larger quantum state space, the researchers can separate aspects and features of that data better than they could in a feature map created by a classical machine-learning algorithm. Ultimately, the more precisely that data can be classified according to specific characteristics, or features, the better the AI will perform.

The goal is to use quantum computers to create new classifiers that generate more sophisticated data maps. In doing that, researchers will be able to develop more effective AI that can, for example, identify patterns in data that are invisible to classical computers.

IBM researhers developed a blueprint with new quantum data classification algorithms and feature maps. That’s important for AI because, the larger and more diverse a data set is, the more difficult it is to separate that data out into meaningful classes for training a machine learning algorithm. Bad classification results from the machine learning process could introduce undesirable results; for example, impairing a medical device’s ability to identify cancer cells based on mammography data.

The researchers found that even in the presence of noise, they could consistently classify their engineered data with perfect accuracy during their tests. Today’s quantum computers struggle to keep their qubits in a quantum state for more than a few hundred microseconds even in a highly controlled laboratory environment. That’s significant because qubits need to remain in that state for as long as possible in order to perform calculations.

Their algorithms demonstrating how entanglement can improve AI classification accuracy will be available as part of IBM’s Qiskit Aqua, an open-source library of quantum algorithms that developers, researchers and industry experts can use to access quantum computers via classical applications or common programming languages such as Python.

"We are still far off from achieving Quantum Advantage for machine learning—the point at which quantum computers surpass classical computers in their ability to perform AI algorithms. Our research doesn’t yet demonstrate Quantum Advantage because we minimized the scope of the problem based on our current hardware capabilities, using only two qubits of quantum computing capacity, which can be simulated on a classical computer. Yet the feature mapping methods we’re advancing could soon be able to classify far more complex datasets than anything a classical computer could handle. What we’ve shown is a promising path forward," IVBM researchers said.