This bachelor’s thesis examines the applicability of quantum computing to the complex optimization problems inherent in energy systems. The transition toward decentralized production and renewable energy sources has increased computational complexity to a degree that challenges the performance of traditional classical optimization methods. In particular, combinatorial challenges—such as unit commitment and power grid management—have been identified as areas where traditional algorithms reach their limits as the number of variables increases exponentially. The thesis introduces the physical and computational foundations of quantum computing, including superposition, entanglement, and quantum parallelism, and analyzes the theoretical advantage they offer relative to classical bit-based computing. The analytical focus is on quantum optimization methods, such as Quadratic Unconstrained Binary Optimization (QUBO), quantum annealing, and variational hybrid algorithms (QAOA). Additionally, the role of quantum simulations in material physics, such as the development of battery materials and catalysts, is examined. Based on the literature review, quantum computing offers promising opportunities to support energy efficiency and the green transition. However, the technological limitations of current hardware (the NISQ era), such as the low number of qubits and sensitivity to noise, still necessitate the extensive use of quantum simulators and classical-quantum hybrid models. This work synthesizes current research in the field and identifies critical research gaps that must be addressed before the full extent of quantum advantage can be realized in practical energy engineering solutions.
This bachelor’s thesis examines the applicability of quantum computing to the complex optimization problems inherent in energy systems. The transition toward decentralized production and renewable energy sources has increased computational complexity to a degree that challenges the performance of traditional classical optimization methods. In particular, combinatorial challenges—such as unit commitment and power grid management—have been identified as areas where traditional algorithms reach their limits as the number of variables increases exponentially. The thesis introduces the physical and computational foundations of quantum computing, including superposition, entanglement, and quantum parallelism, and analyzes the theoretical advantage they offer relative to classical bit-based computing. The analytical focus is on quantum optimization methods, such as Quadratic Unconstrained Binary Optimization (QUBO), quantum annealing, and variational hybrid algorithms (QAOA). Additionally, the role of quantum simulations in material physics, such as the development of battery materials and catalysts, is examined. Based on the literature review, quantum computing offers promising opportunities to support energy efficiency and the green transition. However, the technological limitations of current hardware (the NISQ era), such as the low number of qubits and sensitivity to noise, still necessitate the extensive use of quantum simulators and classical-quantum hybrid models. This work synthesizes current research in the field and identifies critical research gaps that must be addressed before the full extent of quantum advantage can be realized in practical energy engineering solutions.
The rapid growth of data-intensive applications and cloud computing has significantly increased the energy consumption of modern computing infrastructures, particularly data centers, raising critical sustainability and environmental concerns. Green computing seeks to address these challenges by improving energy efficiency and reducing carbon footprints without sacrificing performance. Quantum computing has emerged as a promising paradigm due to its ability to exploit quantum mechanical principles such as superposition and entanglement to achieve computational speedups beyond classical limits. This paper examines the role of quantum computing in advancing green computing technologies by analyzing its algorithmic capabilities, application domains, and hardware-related energy implications. Key quantum algorithms, including Shor’s algorithm, Grover’s search, the Quantum Approximate Optimization Algorithm (QAOA), and quantum simulation methods, are reviewed with respect to their potential to reduce execution time and energy consumption for computationally intensive tasks such as cryptography, optimization, and system modeling. The study further explores sustainability-oriented applications, including energy systems optimization, materials discovery for renewable technologies, and quantum machine learning for resource forecasting and management. Comparative analysis indicates that quantum approaches may offer substantial energy-efficiency advantages over classical methods as hardware matures. However, significant challenges remain, including qubit decoherence, error correction overhead, cryogenic cooling requirements, and scalability limitations. The paper concludes that continued advances in hybrid quantum–classical architectures and fault-tolerant hardware are essential for realizing quantum computing’s potential contribution to sustainable and energy-efficient computing.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
Quantum algorithms offer promising advancements in solving complex optimization problems that are critical in various fields, including logistics, finance, and machine learning. Classical optimization techniques, while effective, often struggle with large-scale and high-dimensional data sets, leading to inefficiencies and long processing times.Quantum algorithms, leveraging the principles of superposition, entanglement, and quantum parallelism, provide a potential solution by exponentially speeding up certain computational tasks. This paper explores key quantum algorithms for optimization, including Grover's algorithm, the Quantum Approximate Optimization Algorithm (QAOA), and the Quantum Annealing method. We analyze their theoretical foundations, implementation challenges, and practical applications. Special attention is given to the performance comparisons between quantum and classical approaches in solving combinatorial optimization problems, such as the traveling salesman problem and portfolio optimization. We also discuss the limitations of current quantum hardware and the prospects for future improvements that could make quantum optimization algorithms more practical for real-world use.Our findings suggest that, while quantum algorithms are still in their early stages, they hold immense potential for transforming optimization in a variety of domains, offering new pathways toward more efficient problem-solving in both theoretical and applied contexts. we explore how hybrid quantum-classical approaches can enhance optimization results by combining the strengths of both paradigms. Future research directions are highlighted, focusing on improving quantum error correction, scaling quantum systems, and developing more robust quantum algorithms to tackle increasingly complex optimization problems.
Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.
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.
Advanced functionalities incorporated in the energy systems include the two-way communication feature, decentralized power generation, and the possibility to monitor operating conditions in real-time, which have already been achieved with the rapid deployment of smart grids. Nonetheless, current classical computing methods have reached severe constraints in facing the increase in complexity, massive volumes of data analysis, and intensive security demands of contemporary power grids. Quantum computation, based on the principles of superposition, entanglement, and quantum parallelism, has the transformative potential of surmounting these drawbacks. In this paper, how quantum computing can be applied to smart grid technologies in four significant areas including: (i) quantumenhanced optimization of dynamic energy distribution and unit commitment tasks, (ii) quantum machine learning to reliably forecast demand loads and renewable energy, (iii) quantum cryptography to secure communication and data verification, and (iv) quantum sensing to achieve high-fidelity fault detection and grid state monitoring is discussed. The findings point to the advantages of the use of quantum algorithms in solving complex computationally intractable optimization problems involved in grid management through near real-time solutions using quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). The issues of noise, decoherence, hardware scalability, and hybrid quantum-classical approaches toward near-term deployment are also examined. This paper demonstrates that quantum computing has the potential to substantially improve the reliability, security, and efficiency of next-generation smart grids.