# Quantum computing for energy systems optimization: Challenges and opportunities

arXiv: https://arxiv.org/abs/2003.00254 · HTML: https://mlsanity.com/paper/2003.00254/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 15 of 15)

- [A Leap among Quantum Computing and Quantum Neural Networks: A Survey](https://mlsanity.com/paper/2107.03313/) (2021)
- [Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers](https://mlsanity.com/paper/2403.15114/) (2024)
- [Learning To Optimize Quantum Neural Network Without Gradients](https://mlsanity.com/paper/2304.07442/) (2023)
- [Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning](https://mlsanity.com/paper/2312.11126/) (2023)
- [QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems](https://mlsanity.com/paper/2404.15852/) (2024)
- [Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach](https://mlsanity.com/paper/2308.02787/) (2023)
- [Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations](https://mlsanity.com/paper/2506.14795/) (2025)
- [Quantum Speedup of Natural Gradient for Variational Bayes](https://mlsanity.com/paper/2106.05807/) (2021)
- [Quantization-Based Optimization: Alternative Stochastic Approximation of Global Optimization](https://mlsanity.com/paper/2211.03972/) (2022)
- [Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment](https://mlsanity.com/paper/2504.14412/) (2025)
- [Classi|Qrangle Towards a Translation Framework To Bridge The Classical-Quantum Programming Gap](https://mlsanity.com/paper/2406.06764/) (2024)
- [Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems](https://mlsanity.com/paper/2601.00873/) (2025)
- [A Unified Generative-AI Framework for Smart Energy Infrastructure: Intelligent Gas Distribution, Utility Billing, Carbon Analytics, and Quantum-Inspired Optimisation](https://mlsanity.com/paper/2605.16232/) (2026)
- [A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation](https://mlsanity.com/paper/2605.16250/) (2026)
- [A Quantum-Assisted Agentic Distributed Artificial Intelligence Framework for Deadline-Bounded Orchestration of Hybrid Renewable Microgrids](https://mlsanity.com/paper/2606.20667/) (2026)

## Abstract (arXiv)

The purpose of this paper is to explore the applications of quantum computing to energy systems optimization problems and discuss some of the challenges faced by quantum computers with techniques to overcome them. The basic concepts underlying quantum computation and their distinctive characteristics in comparison to their classical counterparts are also discussed. Along with different hardware architecture description of two commercially available quantum systems, an example making use of open-source software tools is provided as a first step for diving into the new realm of programming quantum computers for solving systems optimization problems. The trade-off between qualities of these two quantum architectures is also discussed. Complex nature of energy systems due to their structure and large number of design and operational constraints make energy systems optimization a hard problem for most available algorithms. Problems like facility location allocation for energy systems infrastructure development, unit commitment of electric power systems operations, and heat exchanger network synthesis which fall under the category of energy systems optimization are solved using both classical algorithms implemented on conventional CPU based computer and quantum algorithm realized on quantum computing hardware. Their designs, implementation and results are stated. Additionally, this paper describes the limitations of state-of-the-art quantum computers and their great potential to impact the field of energy systems optimization.