- System Design
- Machine Learning
Postdoctoral Researcher: Quantum Computing for Clean Energy Systems
CO - Golden
Postdoc (Fixed Term)
Hours Per Week
The Complex Systems Simulation and Optimization Group in the NREL Computational Science Center has an opening for a full-time postdoctoral researcher in quantum computing for energy systems, with special emphasis on formulations and implementations targeting near-term quantum computing hardware. We believe that quantum computing has the potential to enable algorithms with fundamentally different scaling properties that break the curse of dimensionality, ushering in a new era of energy- and time- efficient computing that enables optimization and control of energy systems at an unprecedented scale. We are looking for a dynamic researcher with a strong technical background to help pursue this goal.
Combinatorial optimization problems arise in a variety of ways in renewable energy research, including power systems design and operation, materials discovery and modelling, chemistry, artificial intelligence (AI) control, etc. Quantum computing, due to the unique scaling properties resulting from entanglement and superposition, is a leading candidate for solving classically-intractable, combinatorially complicated problems. Noisy intermediate scale quantum (NISQ) computers, currently being brought online, represent the vanguard of machines that will usher in this new paradigm of quantum-enhanced optimization. The prospects for how and when NISQ machines will concretely affect the tenor and pace of renewable energy research, however, remain largely unexplored.
The successful candidate will collaborate to develop, adapt, improve, and scale cutting edge quantum computing methods to real-world projects in support of the NREL and EERE clean energy mission. In addition, the candidate will collaborate with NREL staff and researchers, other national labs, and universities on efforts to develop and apply quantum computing to real-world problems in renewable energy research, with specific emphasis on modernization of the nation’s grid, building, and transportation infrastructure and operation to support 100% renewable energy scenarios. In particular, the candidate will participate in a large interdisciplinary effort to reformulate and implement solutions to combinatorially difficult optimization and AI learning problems on both gate-model and annealer-model hardware within the NREL “Quantum Computing Algorithms for Clean Energy Systems Research” initiative.
Collaborate with NREL and partner researchers to develop and apply quantum computing for use in real-world energy systems. Primary challenges include identification of appropriate application areas, adaptation of methods to these areas, and scaling of methods to solve relevant problems. Key areas of study include the optimal incorporation of existing domain knowledge into quantum computing methods, the development of scalable algorithms for discrete combinatorial spaces, the development of principles and algorithm for converting classical objective functions to quantum Hamiltonians, and development of hybrid quantum/classical methods.
Evaluate and track the state of quantum computing research, especially applications and algorithms that can be implemented on near-term quantum hardware.
Formulate and solve energy-system relevant combinatorial optimization problems on existing and near-term quantum hardware.
Formulate and solve quantum reinforcement learning applications in a context particular to NISQ hardware.
Author, present and assist in the preparation of technical papers, reports and conference proceedings on topics related to modern quantum computing methods and their application to energy systems.
Must be a recent PhD graduate within the last three years.
Expertise in quantum information theory and both gate-model and annealer-model quantum computing.
Solid fundamental intuition regarding uniquely quantum phenomena such as superposition and entanglement.
Expertise in NISQ-era quantum computing algorithms and applications, including mathematical understanding of quantum annealing, quantum approximate optimization (QAOA), and variational quantum eigensolver (VQE).
Expertise in deep reinforcement learning, including working knowledge of neural networks, Markov Decision Processes, reinforcement learning, and their application to discrete, continuous, and/or large state-action spaces.
Strong background in mathematics, statistics, and probability; ability to think in terms of probability distribution functions.
Strong background in physics and engineering, esp. power systems, grid-integrated buildings, transportation systems, quantum physics.
Strong programming skills. Sample code from previous related projects/courses is a plus.
Experience programming existing and near-term quantum computers; hands-on experience reformulating classical cost functions as quantum Hamiltonians.
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