How Diamonds Could Unlock the Secrets of Quantum Physics

What makes diamonds so valuable? Most of us would point to their brilliance, clarity and beauty. But Christopher Smallwood, assistant professor of physics and astronomy at San José State, has a different answer: He looks to diamonds as the key to unearthing the secrets of quantum physics.

Smallwood and his collaborators are examining silicon-vacancy centers, which are a type of atom-sized flaw, in diamonds in order to better understand quantum physics. Illustration by Pourya Nadimi

Smallwood and his collaborators are using diamonds to better understand how the world works at the scale of a single atom. His recent findings, “Hidden Silicon-Vacancy Centers in Diamonds,” were published in May in the journal Physical Review Letters.

In the jewelry store, people typically look for the diamond with the fewest flaws. But Smallwood explained that, in the lab, these flaws are exactly what can make diamond samples so special.

He and fellow researchers create atom-sized flaws in the diamonds. Then, using a laser with pulses of light less than a trillionth of a second, they can observe the details and properties of those flaws “in a way we never had been able to before,” Smallwood said.

Exploring new territory

Christopher Smallwood

Christopher Smallwood, assistant professor of physics and astronomy

Why diamonds? To start, their crystal clear makeup allows scientists to easily access flaws with laser technology, Smallwood explained.

But what’s more, they contain a treasure trove of quantum secrets for physicists to uncover.

“There are literally books about optical resonances in diamonds for which no one understands the underlying origin,” he said. By resonances, he means physical responses in the diamonds to outside stimuli, such as light.

“From an experimental physicist’s point of view, it’s really great to have so much left to explore.”

Smallwood noted that his research takes place amid Silicon Valley’s push toward quantum engineering — that is, applying quantum physics to technology. Currently, IBM and Google, for example, are building quantum computers, which will have the power to apply quantum physics knowledge to solve today’s most pressing issues, like creating sustainable energy, reducing emissions and developing more helpful artificial intelligence.

Smallwood’s research demonstrates how San José State could become a key player in this process.

“I’ve seen a number of companies pop up in and around Silicon Valley in recent years aiming to make new inroads in quantum technology, and SJSU is well-positioned to help train the workforce required to make these technological dreams a reality,” Smallwood explained. “The publication of this paper helps underscore this potential.”

Shining light at San José State

Smallwood’s recent findings tie closely with his project funded by a grant from the National Science Foundation he received in 2020. The grant has allowed Smallwood to advance San José State’s capabilities of studying the properties of diamonds and other materials through light.

“SJSU is great because of the ways it allows me to directly work with undergraduate and master’s students and stay active in the laboratory,” he said. “Student participation in these research efforts is crucial. And I’ve got some extraordinary graduate and undergraduate students currently working in my group.”

One of those students is Tommy Wen Chin, ’22 Physics, who is helping Smallwood to better understand the recent findings. Together, he and Smallwood will work on another manuscript that explores the theory behind the work, which they will submit for publication.

Tommy Wen Chin, '22 Physics

Tommy Wen Chin, ’22 Physics

Chin said he’s gaining valuable experience for the future: He plans to pursue a PhD in physics and a career in academic research.

“This experience will give me a significant head start in that process, as I learn not only to perform research, but also to formally report it through publications. Being a first author on a publication as an undergraduate student is very rare within academic circles, and this will enhance my credibility as I apply for programs.”

But most importantly, Chin said he is getting to explore his passion and advance his knowledge of quantum physics.

“The opportunity to learn something new in physics is what drives me,” he shared. “The process of research projects often involves learning bits and pieces of the physics here and there. The most interesting and exciting part for me is when all these little pieces fit together seamlessly and tell a cohesive story.”

Smallwood, of course, understands Chin’s passion for quantum physics and the research process as a whole.

“There’s something really aesthetically beautiful about the theoretical side of the work, and on the experimental side, you get to build things with your own hands,” Smallwood said. “I enjoy working with lasers and shining light on things because — even at the level of high-level physics experiments — seeing is believing.”

View Smallwood’s published study in the Physical Review Letters journal.

Physics Professor’s Latest Findings on AI in Quantum Physics Published in Nature

Fully-connected artificial neural networks are used to analyze ripple in electronic density in experimental images. Image courtesy of Cornell University.

Fully-connected artificial neural networks are used to analyze ripple in electronic density in experimental images. Image courtesy of Cornell University.

Ehsan Khatami, a professor of physics and astronomy in the College of Science, has his latest research published in the journal Nature today in an article entitled “Machine Learning in Electronic-Quantum-Matter Imaging Experiments.” The article shares research that is a collaboration between Khatami and colleagues at Cornell University as well as other institutions on AI-assisted discovery in images of an electronic order in a superconducting material.

A former student of Khatami’s, Kelvin Chng, is cited as an author on the paper and is now working for an AI company in the Bay Area.

“When I first heard about preliminary applications of machine learning methods in condensed matter physics at a conference in spring of 2016, I did not know anything about them,” he said. “I came back from the conference with some ideas on how to use them for quantum problems and quickly found out that Kelvin, who was working on a different project at the time, already knew a lot about artificial intelligence and their applications in industry.”

The pair began working and by the end of the summer had completed a paper that was published a year later in Physical Review X and highlighted in the American Physical Society News. They began collaborating with the Cornell group on designing and testing machine learning algorithms to categorize quantum electronic images of superconducting materials called cuprates.

The images were taken at Cornell using a method called scanning tunnelling microscopy, which maps out real-space patterns of electrons that have self organized into complex quantum states. The images are so noisy and naturally chaotic that conventional methods, like the Fourier analysis, have not been able to decisively pinpoint the type of electronic order found in samples that are close to becoming superconductors in a state dubbed by some physicists the ‘dark matter’ of cuprates.

The team used machine learning for the first time to make sense of data in this mysterious region. They trained a group of artificial neural networks using images that were generated via computer models based on a set of hypotheses, and found that the networks consistently discover the predominant features of a specific ordering pattern whose description dates back to the 1990s.

“It took a long time, many trials, and months of hard work for the collaboration at the beginning to carve out the best strategy to approach and solve the problem, but it all paid off over two years later.”