Protocol to reverse engineer Hamiltonian models advances automation of quantum devices

Scientists from the University of Bristol's Quantum Engineering Technology Labs (QETLabs) have developed a machine learning algorithm that provides valuable insights into the physics underlying quantum systems - paving the way for significant advances in quantum computation and sensing and potentially turning a new page in a scientific investigation.

In physics, systems of particles and their evolution are described by mathematical models, requiring the successful interplay of theoretical arguments and experimental verification. Even more complex is the description of systems of particles interacting with each other at the quantum mechanical level, which is often done using a Hamiltonian model. The process of formulating Hamiltonian models from observations is made even harder by the nature of quantum states, which collapse when attempts are made to inspect them. 

In the paper, Learning models of quantum systems from experiments, quantum mechanics from Bristol's QET Labs describe an algorithm that overcomes these challenges by acting as an autonomous agent, using machine learning to reverse engineer Hamiltonian models.

The team developed a new protocol to formulate and validate approximate models for quantum systems of interest. Their algorithm works autonomously, designing and performing experiments on the targeted quantum system, with the resultant data being fed back into the algorithm. It proposes candidate Hamiltonian models to describe the target system and distinguishes between them using statistical metrics, namely Bayes factors.

Excitingly, the team successfully demonstrated the algorithm's ability on a real-life quantum experiment involving defect centers in a diamond, a well-studied platform for quantum information processing and quantum sensing.

The algorithm could be used to aid automated characterization of new devices, such as quantum sensors. This development, therefore, represents a significant breakthrough in the development of quantum technologies.

"Combining the power of today's supercomputers with machine learning, we were able to automatically discover structure in quantum systems. As new quantum computers/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," said Brian Flynn from the University of Bristol's QETLabs and Quantum Engineering Centre for Doctoral Training.

"This level of automation makes it possible to entertain myriads of hypothetical models before selecting an optimal one, a task that would be otherwise daunting for systems whose complexity is ever-increasing," said Andreas Gentile, formerly of Bristol's QETLabs, now at Qu & Co.

"Understanding the underlying physics and the models describing quantum systems, help us to advance our knowledge of technologies suitable for quantum computation and quantum sensing," said Sebastian Knauer, also formerly of Bristol's QETLabs and now based at the University of Vienna's Faculty of Physics.

Anthony Laing, co-Director of QETLabs and Associate Professor in Bristol's School of Physics, and an author on the paper praised the team: "In the past, we have relied on the genius and hard work of scientists to uncover new physics. Here the team has potentially turned a new page in the scientific investigation by bestowing machines with the capability to learn from experiments and discover new physics. The consequences could be far-reaching indeed."

The next step for the research is to extend the algorithm to explore larger systems and different quantum models that represent different physical regimes or underlying structures.

Nanochannels have important applications in biomedicine, sensing, and many other fields. Though engineers working in the field of nanotechnology have been fabricating these tiny, tube-like structures for years, much remains unknown about their properties and behavior.

Now, University of Maryland mechanical engineering associate professor Siddhartha Das and a group of his Ph.D. students have published surprising new findings in the journal ACS Nano. Using atomic-level simulations, Das and his team were able to demonstrate that charge properties as well as charge-induced fluid flow within a functionalized nanochannel does not always behave as expected.

"We've discovered a new context for nanochannels functionalized by grafting their inner walls with charged polymer molecules (also known as polyelectrolytes or PEs)," Das said, referring to the process of grafting polymers or other substances onto the nanochannel in order to cause it to function in a certain way. "The functionalization of nanochannels is not new. But we've come up with a paradigm shift in terms of understanding the behavior and properties of such systems in the context of their charge properties and their ability to regulate fluid flow. 

"For example," Das said, "we've discovered a new type of flow behavior in such functionalized nanochannels; by increasing the magnitude of the electric field applied to a nanochannel, the direction of this electric-field-driven flow (often known as electroosmotic flow) can be reversed."

The paper by Das and his students details three specific discoveries. Firstly, they showed that, when polyelectrolytes (PEs) are grafted in the form of a layer on the inner wall of the nanochannel, this PE layer will, under certain conditions, undergo a surprising reversal of electrical charge. Normally, if negative PE molecules have been attached to the nanochannel, the PE layer nearby should have a net negative charge. Das and his students, however, identified situations in which the charge becomes inverted and the net charge within the layer is positive due to the attraction of more number of positive ions (than needed to screen the charge of the PE layer) within the layer--this phenomenon is known as "overscreening."

The team then investigated how this overscreening affects the external electric field driven flow (known as the electroosmotic or EOS flow) within the nanochannel. They found, surprisingly, that in such situations the flow is driven by ions having the same charge as the Pes grafted onto the channel walls; thus, a negatively charged polymer creates a net positive field in its vicinity, but the flow is driven by the negative ions.

"We call this 'co-ion driven electro-osmosis,' and our paper marks the first time this phenomenon has been identified," Das said.

Finally, the team demonstrated the unexpected results of ramping up the magnitude of the electric field: the PE molecules attached to the nanochannel become deformed, and the ions that caused the instance of overscreening start to escape from the PE layer. This causes the overscreening to stop, and also reverses the direction of flow in the channel: if it was moving left to right, for instance, it switches to right-left. "No one predicted this," Das said.

The findings are significant, Das said, because much of the interest in nanochannels relates from their ability to transport molecules. "Since flow is so important, a new discovery in this area allows us to build on our understanding of how nanochannels work and what we can do with them," Das said. "There are other methods of reversing flow, but until now it was not known that we can accomplish this by increasing field strength."