Abstract Quantum computing and quantum-inspired technologies are set to significantly impact the energy sector, enhancing optimisation and simulation processes to enable studies that cannot be performed today. At Siemens Energy, our studies have shown potential to address complex challenges in grid management, power distribution, and scarce resource allocation. One of the important examples of where quantum computing holds promise is in the field of optimisation, where physical systems are used to find low energy (i.e. high quality) states (i.e. solutions) to systems that could support many configurations (i.e. combinatorial optimisation problems). The integration of quantum computing into the energy industry promises to deliver improvements in several fields, including cost reduction, increased system reliability, and importantly to be able to make better use of existing resources. Currently in the early stages of research and development, Siemens Energy’s work is showing that combinatorial optimisation problems are all around us in the energy sector. Practical quantum-inspired and hybrid approaches can be used today to solve some of these problems and bring benefits in areas which would have previously not employed optimisation traditionally. This gives us the opportunity and tools to attempt new and innovative approaches to decarbonisation and attempt previously insoluble challenges in the industry.
Abstract Quantum computing and quantum-inspired technologies are set to significantly impact the energy sector, enhancing optimisation and simulation processes to enable studies that cannot be performed today. At Siemens Energy, our studies have shown potential to address complex challenges in grid management, power distribution, and scarce resource allocation. One of the important examples of where quantum computing holds promise is in the field of optimisation, where physical systems are used to find low energy (i.e. high quality) states (i.e. solutions) to systems that could support many configurations (i.e. combinatorial optimisation problems). The integration of quantum computing into the energy industry promises to deliver improvements in several fields, including cost reduction, increased system reliability, and importantly to be able to make better use of existing resources. Currently in the early stages of research and development, Siemens Energy’s work is showing that combinatorial optimisation problems are all around us in the energy sector. Practical quantum-inspired and hybrid approaches can be used today to solve some of these problems and bring benefits in areas which would have previously not employed optimisation traditionally. This gives us the opportunity and tools to attempt new and innovative approaches to decarbonisation and attempt previously insoluble challenges in the industry.
This chapter explores the transformative potential of quantum computing across various industries, with a focus on its applications in energy and financial services. Quantum computing, leveraging principles of quantum physics, promises to revolutionize problem-solving and optimization processes that are beyond the capabilities of classical computing. In the energy sector, quantum machine learning enhances supply and demand forecasting, while Quantum Annealing optimizes energy production schedules through the Unit Commitment Problem. These quantum approaches address the complexities of volatile energy markets and the integration of renewable energy sources, providing innovative solutions for cost-effective and reliable energy management. In financial services, quantum computing offers advancements in portfolio optimization, risk analysis, and fraud detection, enabling faster and more efficient data processing. The chapter also highlights the necessity for financial institutions to develop post-Quantum cryptography strategies to secure cryptographic systems against emerging quantum threats. Despite its potential, the widespread adoption of quantum computing faces significant challenges, including the high cost of quantum machines, integration issues with classical systems, and the nascent stage of the technology. Nonetheless, preparing business structures for the forthcoming quantum era and leveraging current capabilities are essential steps for organizations. The chapter concludes with an outlook on the future applications of quantum computing in sectors such as healthcare, pharmaceuticals, and defense, underscoring its role as a catalyst for a sustainable and technologically advanced future.
Quantum computing holds promise for addressing previously unsolvable problems, particularly within complex energy systems driven by big data. This research employs a semi-systematic literature analysis to identify and categorise popular quantum algorithms with potential applications in these systems. The algorithms are divided into two main groups: quantum chemical simulation algorithms and quantum optimisation algorithms. Quantum chemical simulations can model molecules, facilitating the discovery of advanced materials and technologies for complex energy systems. Meanwhile, quantum optimisation algorithms aim to enhance energy production efficiency by optimising the grid's energy flow and smart energy storage. A significant challenge across all algorithms is the current hardware limitations, as they require more processing power than is presently available. Additionally, these algorithms necessitate precise initial parameter settings, necessitating accurate representation of real-world scenarios. Overcoming these challenges could enable quantum computing to enhance the efficiency and effectiveness of complex energy systems significantly.
Quantum computing innovations have garnered significant attention for their potential to revolutionize industries, with the energy sector being one of the most promising areas for application. As global energy demand increases and sustainability becomes more critical, computational technologies offer groundbreaking solutions for energy production, storage, and distribution. In this landscape, quantum computing plays a crucial role in unlocking the full potential of artificial intelligence and machine learning as research and development in the quantum machine learning field grows constantly. We here present a scoping review of early quantum machine learning applications within the energy industry value chain. Starting from 34 sources, we analyze and discuss 22 use cases in the energy sector, thoroughly examining each to understand its potential applications and impact. We then evaluate these early-stage quantum applications to determine their feasibility and benefits, offering insights into their relevance and effectiveness in the context of the industry’s evolving landscape. This is done by introducing a novel framework: the Assessment Model for Innovation Management (AMIM). Our research highlights the opportunities that quantum innovations present for the energy sector and offers actionable insights into which applications are the best investments and why. Overall, the feasibility and technological maturity of quantum machine learning use cases are still in the early stages, though their market compatibility and potential benefits are mostly relatively high. This indicates that while quantum machine learning holds immense potential, further development is necessary to fully realize its benefits in the energy sector.
Industrial engineering increasingly depends on solving large-scale optimization problems involving scheduling, routing, resource allocation, inventory control, energy management, and supply-chain coordination. Classical exact methods often become computationally expensive when problem size, uncertainty, and combinatorial complexity grow rapidly. Although practical quantum computers are still developing, quantum-inspired computing approaches have emerged as powerful alternatives that adapt principles from quantum mechanics to run on classical hardware. This paper investigates quantum-inspired computing approaches for complex optimization problems in industrial engineering. It examines methodologies including simulated annealing, tensor-network optimization, Ising formulations, quantum- inspired evolutionary algorithms, digital annealers, and hybrid classical-intelligent heuristics. Applications in production scheduling, facility layout, vehicle routing, maintenance planning, portfolio optimization, warehouse operations, and smart manufacturing are analyzed. Particular attention is given to solution quality, scalability, stochastic decision-making, real-time adaptability, and integration with Industry 4.0 data systems. Benefits include faster near-optimal solutions, improved operational efficiency, reduced energy consumption, and better responsiveness under uncertainty. Major challenges include formulation difficulty, parameter tuning, benchmarking inconsistency, interpretability, and transition barriers within traditional industrial workflows. A future roadmap is proposed involving hybrid quantum-classical ecosystems, AI- guided optimization engines, digital twins, and autonomous decision platforms. The paper concludes that quantum-inspired computing can deliver practical industrial value today by bridging the gap between conventional optimization and future quantum advantage, especially in environments where speed, flexibility, and complexity management are essential.
Traditional computers, using “classical” computing concept, are unable to execute certain forms of information processing that are possible with quantum computing. Quantum computing is based on the law of quantum physics. As a consequence, quantum computers implement strategies for the resolution of problems, unavailable to traditional computers. Mostly, quantum problems are solvable in physics, and so, the mismatch is found between the traditional research and the practical challenges that need to be solved. Renewable energy is a potential area with ample scope for contribution and exploration for further research. Considering the distributed resources management and that of fluctuation of weather-dependent generation flows, the adoption and scaling up of renewable resources is supposed to be present in an electrical grid with a number of new issues. So, future challenges lie with control management of these resources. The major issues are therefore some difficulties associated with renew-able sources of energy, simulation, scheduling, dispatch, and reliability 144 evaluations. The crucial challenge is concerned with potential future problems with the scalable technologies to overcome using quantum computing. This chapter will provide an overview of the potential research areas focusing on connection between the quantum computing and renewable energies highlighting the future applications of dual fields.
Quantum Green Computing represents a transformative paradigm that merges the power of quantum computational techniques with the principles of sustainability to drive innovation in renewable resource management.This paper investigates the interplay between quantum algorithms, energy-efficient system architectures, and advanced renewable energy technologies-including solar photovoltaics, wind energy systems, and bioenergy solutions.By leveraging the unique capabilities of quantum computing, such as superior optimization, high-dimensional modeling, and rapid problem-solving, the study explores novel strategies for addressing the inherent complexities of renewable energy integration.The research emphasizes three core dimensions: (1) the application of quantum optimization algorithms to enhance grid stability, load balancing, and real-time decision-making; (2) the development of energy-efficient quantum-inspired hardware architectures that align with green computing objectives; and (3) the formulation of predictive and adaptive models for renewable resource allocation using quantum-inspired approaches.Furthermore, the paper highlights case studies that demonstrate how quantum techniques can significantly improve grid management, predictive maintenance of renewable infrastructures, and long-term forecasting of resource availability under dynamic environmental conditions.By critically evaluating these emerging pathways, the study outlines a vision where quantum-enabled sustainability not only reduces carbon emissions but also maximizes energy utilization efficiency.This integration holds the potential to revolutionize the global renewable energy landscape, enabling resilient, lowcarbon ecosystems capable of supporting the growing demands of a sustainable future.