The quiet winner of the Nobel Prizes in science

Every October, the Nobel Prizes honor science's greatest minds — including this year’s winners in physics, John Clarke, Michel H. Devoret, and John M. Martinis, who demonstrated that ‘quantum character’ can be observed at the macro-scale using a circuit made of superconductors.

Throughout history, laureates have given us the blue LEDs that light our phones, fiber optic cables that carry internet traffic, and neural networks that power artificial intelligence. They have detected ripples in spacetime, developed life-saving vaccines, and revealed how stars forge the elements from which we are made.

But there's another unsung winner: federal funding.

Since World War II, the federal government has funded basic research across universities and labs. This investment has paid dividends. The National Science Foundation has supported 271 Nobel Prize winners throughout their careers; the National Institutes of Health has supported at least 174.

Below are the stories of a few of these laureates, including this year’s winners.

2025: Clarke, Devoret, and Martinis quantize a macro-circuit with public dollars

In the International Year of Quantum Science and Technology, perhaps it’s no surprise that the recipients of this year’s Nobel Prize in Physics received the honor for experimental work demonstrating ‘quantum character’ — quantized energy states and tunneling behavior — in an electrical circuit.

But their circuit was nothing like the microcircuits in a cell phone, composed of nanoscale transistors with layers so thin an electron could almost tunnel right through. By contrast, this year’s winners built a simple circuit big enough to hold in the hand. Instead of using common circuit components like wires and resistors, they used superconductors — materials with no electrical resistance when cooled to their unique critical temperatures.

In September 1984, the team of three — Clarke, the research group lead at the University of California, Berkeley; his postdoc Devoret; and his grad student Martinis — published their observations of “resonant activation” in the special circuit design in APS’ journal Physical Review Letters. “None of this work would have happened without the two of them,” Clarke said. All three scientists are APS Fellows and members.

A year after that discovery, the trio reported additional findings, including the measurement of macro-scale quantum tunneling behavior in the same circuit design. This work was largely supported by the Office of Basic Energy Sciences in the U.S. Department of Energy.

Today, macroscopic quantum tunneling has moved from basic physics to widespread application. It forms the basis of ultraprecise measurements in meteorology, neuroscience, and the geosciences; specialized imaging technologies in ultra-low-field MRI machines; and quantum circuits that power advanced computing. The National Science Foundation — which awards billions of dollars every year to basic research — remains one of the biggest investors in quantum computing.

More than a decade after the 1984 paper, Clarke leveraged that work and a 1997 NSF grant of just $281,897 to craft an experiment capable of searching for axions — hypothetical fundamental particles believed to hold the clue to dark matter.

Then, in 2002, Clarke partnered with a team of multi-disciplinary UC-Berkeley scientists and engineers to explore a new type of quantum computing processor design. The $4.6 million in NSF funding led to 55 publications, revealing deep insights into quantum dots, fullerenes, superconductors, and qubits.

Devoret, originally from France, has a continent-spanning career. But in his first U.S. faculty position at Yale in 2010, he secured $315,000 in NSF funding to extend his earlier work to superconducting nanowires. In 2021, he partnered with colleagues from four American universities, leveraging $1.8 million in NSF support to found a multi-institutional center for the study of quantum devices — bridging the gap between theory and real-world applications.

Martinis’ research program at the University of California, Santa Barbara, has also benefited from NSF funding. In 2005, Martinis secured $1.2 million for deeper study of metal-dielectric interfaces, critical for confining charge carriers in circuits in quantum information and microwave devices. The collaborative effort culminated in 12 publications in a few years, revealing insights into electron tunneling behavior, entanglement phenomena of superconducting qubits, and more.

Interviewed about his prize, Martinis said the field’s growth is “most exciting.” With “a thousand or more scientists who are working on quantum computing and superconducting qubits,” he says, we are one step closer to commercializing quantum computing technology — a reality made possible by federal funding.

2024: With NSF support, Hopfield lays the groundwork for AI

The 2024 Nobel Prize in Physics recognized physicists John J. Hopfield and Geoffrey E. Hinton for their work on artificial neural networks. Hopfield’s contribution was his 1982 invention of a computer network architecture that could save and replicate patterns, like a simple brain.

That breakthrough emerged from years of NSF support: The agency ultimately awarded Hopfield and collaborators more than $2.5 million in grants over nearly three decades, funding his work on the physics of biology.

Hopfield began his career at AT&T’s Bells Labs, where colleagues in the theory-focused department encouraged him to work with Bell’s experimental groups. This laid the foundations for Hopfield’s work with David G. Thomas on compound semiconductors, which earned the duo the 1969 APS Buckley Prize and Hopfield an election to APS Fellowship.

By then, Hopfield had departed Bell Labs for academia, first at UC-Berkeley and then Princeton. By the mid-1970s, he felt he had “run out of problems” in condensed matter physics and pivoted to the inner workings of the brain, including the mechanisms of memory and learning.

Over the next 11 years, from 1975 through 1986, NSF awarded Hopfield five grants totaling more than $800,000 to support his research on neural networks.

After a stint at Caltech, Hopfield settled back at Princeton, where he joined forces with Leif Finkel at the University of Pennsylvania. In 1998, they secured a $1.6 million award from NSF for the study of “neuromorphic knowledge systems.”

But Hopfield — now 93 — led not only great science. He also supported great scientists. At least four of his doctoral students are APS Fellows, elected for contributions ranging from advances in supercomputer architectures to protein folding theory. One of these four, Bertrand Halperin, has followed particularly closely in Hopfield’s footsteps, having won the Buckley Prize and APS Medal and secured nearly $4.5 million in NSF funding for studies focused on electron behavior in confined geometries, like quantum dot nanoparticles.

Another graduate student, David J. C. MacKay, was the chief scientific advisor to the U.K. Department of Energy and Climate Change from 2010 to 2014. Erik Winfree received the 2006 Feynman Prize in Nanotechnology for his work in DNA-based computing.

Today, Hopfield’s seminal work on neural networks has captured 29,267 citations and inspired a wave of technological innovation. But these impacts were never imagined from the beginning, when federal agencies first funded Hopfield.

As Hopfield said in 2024 after his Nobel win, “the science which advances technology is the science that gets done for curiosity’s sake much earlier.”

2024: The bucks behind Baker’s computational protein design

Also in 2024, half of the Nobel Prize in Chemistry was awarded to David Baker, an American biochemist, for the use of computational methods in protein design.

Proteins — biological structures sequenced from combinations of 20 molecular building blocks known as amino acids — form the basis of all known life. Sometimes, proteins can be disrupted by DNA mutations, causing disease. Humans have also harnessed proteins synthesized by other organisms, like yeast or E. coli, for food or medicine.

But the story of Baker’s work with proteins starts with a computer, not a lab.

In 1999, he received his first NSF grant. With an award of just $312,500, Baker studied how two proteins ‘fold’ from their amino acid sequence into their biochemically active structure. After four years, Baker’s team at the University of Washington had learned so much about these folding mechanisms that they were able to design the first synthetic protein, Top7, with a novel topology — the initial demonstration of a scientist’s ability to design a protein with a specific 3D structure from scratch.

By 2019, Baker’s team had received nine more NSF awards, totaling $5.2 million over two decades.

During that time, his research group developed an algorithm for predicting the 3D structures of proteins, known as Rosetta. From that, they created Rosetta@home, software that allowed users to donate their own computers’ processing power for number-crunching toward the design of new protein structures. To make it more interactive for users, the team created a gamified version known as Foldit.

In 2011, Foldit players helped decode the structure of an HIV-like virus that affects monkeys. In 2012, they re-engineered an enzyme to drastically accelerate its rate of activity in synthetic reactions. By 2019, players had designed four enzymes that researchers were able to grow in the lab.

This work ushered in a new paradigm: the use of algorithms, software, and (increasingly) artificial intelligence to predict and discover new proteins capable of doing whatever their designers want them to do, like treat cancer or clean up environmental contaminants.

To date, Baker has authored over 600 scientific papers as indexed by Google Scholar and co-founded more than 20 startups, which have delivered new vaccines, cancer treatments, and medical therapies. Icosavax, founded in 2017, was acquired by vaccine-maker AstraZeneca in 2023 for $1.1 billion, and Sana Biotechnology, founded in 2018 to use engineered cells as a form of medicine, is now worth over $800 million.

“I think protein design has huge potential to make the world a better place,” Baker said after his Nobel win. “And I think we’re just at the very beginning.”

Indeed, the therapeutic tools that Baker’s work has inspired began with a simple idea — that the amino acid sequence of a protein could predict its structure — and one NSF grant.