Duke Quantum Center researchers win $1 million grant to plan world’s most powerful quantum computer

Researchers at the Duke Quantum Center received a $1 million grant to plan a 256-qubit quantum computer, kicking off an effort to build the most powerful computer ever of its kind.

The production of the new quantum computer — called the Quantum Advantage-Class Trapped Ion system — is supported by the National Science Foundation’s National Quantum Virtual Laboratory program, which offered a $1 million dollar grant for a one-year pilot project. Once completed, the quantum computer could be the first with the ability to outperform classical computers in areas of scientific application.

According to Kenneth Brown, Michael J. Fitzpatrick distinguished professor of engineering, the first step for the DQC and its collaborators will be to develop a design for the quantum computer. If the design is accepted by the NSF, the project will enter an implementation phase which could potentially last for 12 years. In this phase, Brown noted, the grant could increase to $10 million per year. 

This venture builds upon the Software-Tailored Architectures for Quantum co-design (STAQ) project, a collaboration between Duke, the University of Chicago, Tufts University and North Carolina State University. In 2024, the STAQ project received $17 million in funding for continued work through 2029, building on its initial $15 million in NSF funding from 2018.

The STAQ-collaborating institutions, as well as North Carolina Agricultural and Technical State University, form the team for the NQVL project.

For many tasks, quantum computers are more powerful than classical computers. They are made of qubits — quantum bits which can exist in superposition states and experience entanglement — opening the door to new computational abilities.

Researchers at the DQC focus on creating qubits out of trapped ions. The STAQ project achieved control of a chain of 23 qubits, with plans to reach over 50. The NQVL project seeks to eventually achieve control of 256 qubits.

“The problem is, on your laptop, you can easily fake a 12-qubit quantum computer,” said Kenneth Brown, Michael J. Fitzpatrick distinguished professor of engineering. “... On the world’s best supercomputer, you could get to around 50. So that’s the thing which is exciting about this project –– it’s really trying to push to where you can’t emulate things.” 

The DQC developed a system to use trapped ions as qubits, which they control with lasers. However, longer chains of qubits are more difficult to control.

“A laser beam will only be so small at that ion,” Brown said. “If I squish the ions too far together, then that laser beam will basically hit many of them.”

Another problem with quantum systems is that they can be “noisy,” which refers to when information gets distorted.

Balint Pato, a doctoral student who works with Brown, equates this noise to an image that is blurry or pixelated. According to Plato, the sources of “noise” will be different in a larger quantum system compared to those in a smaller computer, and he believes that this will result in much bigger challenges. 

While some DQC researchers focus on the computer’s hardware, others focus on quantum programs and the tools that compile them.

Aniket Dalvi, a doctoral candidate in the Brown Lab, builds software stacks, which convert quantum programs into hardware instructions. Dalvi works on problems in scalability, as he considers how a program which functions on a quantum computer with less than 30 qubits could function on a 256-qubit computer.

“Given that this is one of the largest [quantum computing systems] that has been built in an academic facility, scalability is going to be a big challenge,” Dalvi said. “... Depending on how [the quantum computer] is built — and what kind of decisions they make in building a big system — those decisions will inform how a program is converted to instructions on this hardware.”

In describing applications of a large quantum computer, Brown cited the ability to run chemical and physical simulations that require quantum mechanics, as well as new opportunities in quantum machine learning — sharing that “if you build a large enough quantum computer, you could actually steal all of the early Bitcoin.”

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