Machine learning can get a boost from quantum physics.
For certain types of machine learning tasks, quantum computers have an exponential advantage on standard calculation, scientists report in the June 10 Science† The researchers proved that, according to quantum mathematics, the advantage applies when using machine learning to understand quantum systems. And the team showed that the advantage holds up in real-world testing.
“People are really excited about the potential of using quantum technology to improve our learning ability,” said theoretical physicist and computer scientist Hsin-Yuan Huang of Caltech. But it was not entirely clear whether machine learning could benefit from quantum physics in practice.
In certain machine learning tasks, scientists try to collect information about a quantum system — say, a molecule or group of particles — by conducting repeated experiments and analyzing data from those experiments to learn more about the system.
Huang and colleagues studied several such tasks. In one, scientists are trying to discern properties of the quantum system, such as the position and momentum of particles within. Quantum data from multiple experiments could be fed into the memory of a quantum computer and the computer would process the data together to learn the characteristics of the quantum system.
The researchers theoretically proved that performing the same characterization using standard or classical techniques would require exponentially more experiments to learn the same information. Unlike a classical computer, a quantum computer can exploit entanglement – ethereal quantum couplings – to better analyze the results of multiple experiments.
But the new work goes beyond the theoretical. “It’s crucial to understand whether this is realistic, whether this is something we could see in the lab or whether this is just theoretical,” said Dorit Aharonov of the Hebrew University in Jerusalem, who was not involved in the study.
So the researchers tested machine learning tasks with: Google’s quantum computerSycamore (SN: 23-10-19† Rather than measuring a real quantum system, the team used simulated quantum data and analyzed it using quantum or classical techniques.
Quantum machine learning won there too, though Google’s quantum computer is noisy, meaning errors can slip into calculations. Ultimately, scientists plan to build quantum computers that can do that correct their own mistakes †SN: 22/6/20† But for now, even without that error correction, quantum machine learning has prevailed.