Nitrogen vacancy centre set-up used for the first experimental demonstration of QETLabs’ algorithm. Image: Andreas Gentile/QETLabs
Researchers at the University of Bristol have developed a machine learning algorithm that sheds light on the physics underlying quantum systems.
A machine learning algorithm developed by University of Bristol’s Quantum Engineering Technology Labs (QETLabs) could help deepen our understanding of how quantum systems work.
The algorithm uses machine learning to reverse engineer Hamiltonian models, which are often used to describe how systems of particles interact with each other at the quantum mechanical level.
The process of formulating Hamiltonian models from observations is difficult due to the nature of quantum states, which collapse when scientists try to inspect them. According to the researchers, their new algorithm could overcome this and pave the way for “significant advances in quantum computation and sensing”.
Their paper, published in Nature Physics, describes the algorithm as an “autonomous agent”.
It designs and performs experiments on targeted quantum systems and feeds the resulting data back into the algorithm before proposing candidate Hamiltonian models to describe that system. It distinguishes between these using statistical metrics in the form of Bayes factors.
Researchers said the process was successfully demonstrated using a real-life quantum experiment on defect centres in a diamond.
“Combining the power of today’s supercomputers with machine learning, we were able to automatically discover structure in quantum systems,” said QETLabs’ Brian Flynn.
“As new quantum computers and simulators become available, the algorithm becomes more exciting. First it can help to verify the performance of the device itself, then exploit those devices to understand ever-larger systems.”
Anthony Laing, co-director of QETLabs and associate professor in Bristol’s School of Physics, added: “In the past we have relied on the genius and hard work of scientists to uncover new physics.
“Here, the team have potentially turned a new page in scientific investigation by bestowing machines with the capability to learn from experiments and discover new physics. The consequences could be far reaching indeed.”
QETLabs’ researchers said their algorithm could be used to aid automated characterisation of new devices, such as quantum sensors. The next step in their study will apply the algorithm to larger systems and different classes of quantum models.
