Quantum Computing Paves the Way for a Clean Energy Future

NYU Center for Data Science
4 min readMay 17, 2024

The looming threat of climate change has spurred a global race to decarbonize power grids and transition to 100% clean energy. However, the complexity of optimizing power systems for reliability and affordability while integrating millions of new, flexible devices such as electric vehicles and transitioning to renewable energy sources has pushed even the most advanced supercomputers to their limits. Now, a new avenue for tackling this challenge has emerged: quantum computing.

In a recent paper published in the journal Joule, Xiangyue (Max) Wang, a recent MS in Data Science graduate of CDS, and his soon-to-be PhD advisor Thomas Morstyn, Associate Professor at Oxford University, provide an overview of the promising applications of quantum algorithms in solving optimization problems for net-zero power systems. The paper explores how quantum computers, with their fundamentally different computational capabilities, could potentially speed up calculations needed to ensure reliable electricity delivery in a grid powered entirely by clean energy.

“To tackle climate change, we need a low-cost clean power system that a fully electrified society can rely on,” Wang explained. “In order to operate the grid, there are a set of optimization problems that are NP-hard to begin with, and we simply cannot meet the challenges we face in the coming years with classical computers and algorithms alone.”

Quantum computing, although still in its early stages of development, has already demonstrated practical advantages over classical computing for specific applications. Wang and Morstyn’s paper reviews the latest work on quantum computing for combinatorial power system optimization problems, such as unit commitment, grid-edge flexibility coordination, and network expansion planning. They also map state-of-the-art theoretical work to applications where quantum computing is underexplored, including convex and machine learning-based optimization.

These innovations may seem like a distant dream, but quantum computers are closer to industry-scale applications than many would expect. The National Renewable Energy Laboratory (NREL) is collaborating with Atom Computing, a quantum computing company, to develop a testbed that integrates a quantum computer with a real-time digital simulator of the power grid. This Quantum-in-the-loop simulation, called ARIES, allows researchers to test quantum algorithms on a digital twin of the national grid. Additionally, industry partners like E.ON, IBM, and Phasecraft are exploring the use of quantum computing for various applications in the energy sector, such as optimizing vehicle-to-grid interactions and improving power system planning. Wang’s aim is to make industry-scale applications a reality in less than a decade.

Wang’s journey into climate solutions began during the COVID-19 pandemic, a time of introspection and reevaluation for many. “I was suddenly in my bedroom finishing my last year in college,” he recalled. “I always wanted to do a PhD in physics and study dark matter and dark energy to understand the rest of the universe. But during COVID, I had an epiphany: I cannot study the sky if my house is on fire. I found it too distracting.”

Driven by a newfound sense of urgency, Wang decided to dedicate his career to climate solutions. “I asked myself, ‘How can I decarbonize as much of the world as possible?” he said. “What should I study and do for a living so that I can eliminate as much of the 37 billion tons of carbon dioxide that the world annually emits as possible?’”

This commitment led him to a Masters degree in data science at CDS, where he designed a self-directed curriculum that specifically focused on courses with applications in climate solutions. Wang said the flexibility of the program and the vibrant climate tech community in New York City provided him with ample opportunities to work on impactful projects, such as mapping the carbon emissions of buildings in the city and starting a climate tech startup, NARA, focused on decarbonizing buildings using machine learning. He was also an Admissions Ambassador during his time at CDS.

As Wang gears up for his PhD, he remains committed to pushing the boundaries of quantum computing for power system optimization. He plans to focus on the intersection of quantum physics and machine learning, utilizing hybrid classical-quantum algorithms to tackle complex optimization problems. The Joule paper, he said, is essentially a roadmap for the work he plans to undertake at Oxford.

On the climate crisis and quantum computing, Wang said there is no one “silver bullet” that will fix everything, and significant policy changes and immediate action are crucial. However, he believes that by leveraging his technical background, he can at least make a contribution to the solution.

By Stephen Thomas



NYU Center for Data Science

Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.