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DOE OSTI · 2323254

Establishing capabilities for quantum computing and simulations for energy applications

Alfonso, Dominic·Avramidis, Benjamin·Paudel, Hari P.·Lee, Yueh-Lin·Nguyen, Manh Tien·Crawford, Scott·Jordan, Kenneth·Duan, Yuhua
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Abstract

Quantum information science (QIS) is creating potential transformative opportunities to exploit intricate quantum mechanical phenomena in new ways for obtaining and processing information to advance many areas of science and engineering. Since the National Quantum Initiative Act was signed into law in 2018, developing QIS capability and competency is one of the most urgent tasks of DOE to make sure the US win the quantum race. The QIS contains four pillars: quantum computing, quantum simulations, quantum sensing, and quantum networking. To apply QIS in energy related applications, the key is to develop the capability of quantum computing & simulation tools. In this project, we propose to develop the capability of quantum computing and simulations at NETL to target fossil energy related problems. We will install a simulator (e. g. IBM qiskit) on the NETL supercomputer to simulate the environments of quantum computer. Based on the current available quantum algorithms for quantum chemistry, we will develop quantum computing codes to perform simulation which will focus on fossil energy sector challenges, including CO2 capture & conversion, sensing, and fuel conversion. We then will seek opportunities to run our codes on real quantum computers (such as IBM-Q, google Sycamore, etc.). Through this project, the new capability of quantum computing and simulations will be established at NETL. In addition, the NETL workforce in this area will be trained ready to conduct more complicated tasks in line with NETL missions to enhance the nation’s energy foundation.

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97 MATHEMATICS AND COMPUTING

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Alfonso, Dominic, Avramidis, Benjamin, Paudel, Hari P., Lee, Yueh-Lin, Nguyen, Manh Tien, Crawford, Scott, Jordan, Kenneth, Duan, Yuhua. 2024-01-31. Establishing capabilities for quantum computing and simulations for energy applications. https://doi.org/10.2172/2323254

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