Exploring the trade-off between computational power and energy efficiency: An analysis of the evolution of quantum computing and its relation to classical computing
Elena Desdentado, Coral Calero, Ma Ángeles Moraga, Manuel Serrano, Félix O. García
Quantum computing is considered a revolutionary technology due to its ability to solve computational problems that are beyond the capabilities of classical computers. However, quantum computing requires great amounts of energy to run. Therefore, a factor in deciding whether to use quantum computing should be not only the complexity of the problem to be solved, but also the energy required to solve it. This paper presents an empirical study developed with the aim of comparing classical and quantum computing in terms of energy efficiency to determine whether the increased power of quantum computers is offset by their higher energy consumption. To achieve this, a variety of problems with different levels of complexity were tested on both types of computers. Specifically, we used the IBM Quantum computers with a maximum of 5 qubits and an Intel i7, as a classical computer. In addition to this we have also analysed the evolution of the quantum computers, performing measurements on three time periods. Our empirical study showed that there is a variability of results obtained in the three time periods and that quantum computing is not recommended for low-complexity problems, given its high energy consumption, particularly when compared to traditional computing.
Quantum computing is considered a revolutionary technology due to its ability to solve computational problems that are beyond the capabilities of classical computers. However, quantum computing requires great amounts of energy to run. Therefore, a factor in deciding whether to use quantum computing should be not only the complexity of the problem to be solved, but also the energy required to solve it. This paper presents an empirical study developed with the aim of comparing classical and quantum computing in terms of energy efficiency to determine whether the increased power of quantum computers is offset by their higher energy consumption. To achieve this, a variety of problems with different levels of complexity were tested on both types of computers. Specifically, we used the IBM Quantum computers with a maximum of 5 qubits and an Intel i7, as a classical computer. In addition to this we have also analysed the evolution of the quantum computers, performing measurements on three time periods. Our empirical study showed that there is a variability of results obtained in the three time periods and that quantum computing is not recommended for low-complexity problems, given its high energy consumption, particularly when compared to traditional computing.
Today, humanity cannot advance without the help of computation. There are still many problems that need resolving that would never be solved without the help of a powerful computer. It is also impossible to build a large enough memory to allow us to solve these complex problems. However, even if we manage to achieve the impossible, with the unprecedented rise of data and the ever-evolving complexity of algorithms, computing the answers in the traditional way would require billions of MWs of power consumption. Not to mention the energy required to cool down the powerful computers from the significant amount of heat they produce. Humanity is quickly reaching the limits of computations that can be currently achieved, raising concerns about the sustainability and environmental impact these power-hungry machines require. Enter quantum computing – the revolutionary technology holding the promise of not only enhancing computational speeds and capabilities but also potentially alleviating the energy burden imposed by traditional computing methods.
This article comprehensively examines the conceptual and practical aspects of two fundamental directions at the peak of the hierarchy of modern computing technologies — classical supercomputers and quantum computing systems. The relevance of the research is explained by the rapid growth of data volumes and the fact that traditional silicon technologies have reached their physical limits. The first part of the article analyzes the history of supercomputer development, their multi-core parallel architecture, and operating principles based on logical bits. Simultaneously, the technological limitations of such systems, including high energy consumption and cooling requirements, are highlighted. The second part explores the laws of quantum mechanics as the theoretical foundation of quantum computers, specifically the impact of superposition and entanglement phenomena on computing efficiency. The fundamental differences between classical bits and quantum bits (qubits) are scientifically justified, alongside the revolutionary potential of quantum algorithms (such as Shor’s and Grover’s algorithms) in traditional cryptographic and search systems. In the final section, a comparative analysis of these two powerful systems is conducted regarding processing speed, energy consumption, fault tolerance, and practical application areas. The research results indicate that quantum computers will not entirely replace classical supercomputers in the near future; instead, they will facilitate the formation of hybrid computing models. The article is intended for industry specialists, students, and those interested in the evolution of high technologies.
Quantum computing has emerged as a promising paradigm for addressing computational challenges in physics that remain intractable for classical methods. This study presents a quantitative benchmarking analysis of prominent quantum algorithms applied to representative physics problem classes, with the objective of evaluating their computational performance relative to state-of-the-art classical algorithms. Using standardized performance metrics—including execution time, algorithmic complexity, resource utilization, accuracy, scalability, and quantum advantage ratios—the study systematically compares quantum and classical approaches across multiple experimental configurations. The results demonstrate that quantum algorithms exhibit measurable performance gains for specific problem types, particularly in terms of runtime efficiency and scaling behavior, while also highlighting practical constraints related to qubit requirements and circuit depth. Visual and tabulated analyses further reveal that quantum advantage is highly problem-dependent and influenced by algorithmic structure and system size. Overall, the findings provide empirical evidence that quantum algorithms can offer substantial computational benefits for selected physics applications, while underscoring the importance of quantitative benchmarking in distinguishing theoretical promise from practical performance. This work contributes to ongoing efforts to objectively assess quantum advantage and informs future research directions in quantum algorithm development and physics-based applications.
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.
Energy consumption in solving computational problems has been gaining growing attention as one of the key performance measures for computers. Quantum computation offers advantages over classical computation in terms of various computational resources; however, proving its energy-consumption advantage has been challenging due to the lack of a theoretical foundation linking the physical concept of energy with the computer-scientific notion of complexity for quantum computation. To bridge this gap, we introduce a general framework for studying the energy consumption of quantum and classical computation, based on a computational model conventionally used for studying query complexity in computational complexity theory. Within this framework, we derive an upper bound for the achievable energy consumption of quantum computation, accounting for imperfections in implementation appearing in practice. As part of this analysis, we construct a protocol for Landauer erasure with finite precision in a finite number of steps, which constitutes a contribution of independent interest. Additionally, we develop techniques for proving a nonzero lower bound of energy consumption of classical computation, based on the energy-conservation law and Landauer’s principle. Using these general bounds, we rigorously prove that quantum computation achieves an exponential energy-consumption advantage over classical computation for solving a paradigmatic computational problem—Simon’s problem. Furthermore, we propose explicit criteria for experimentally demonstrating this energy-consumption advantage of quantum computation, analogous to the experimental demonstrations of quantum computational supremacy. These results establish a foundational framework and techniques to explore the energy consumption of computation, opening an alternative way to study the advantages of quantum computation.
Quantum computing has the potential to outperform classical computing in specific problem domains, but current systems rely heavily on classical resources for tasks such as quantum circuit compilation. As quantum hardware scales, the energy consumption of these supporting classical processes becomes increasingly important. This thesis investigates the energy consumption of quantum circuit compilation by analyzing existing literature and benchmarking results for different quantum software frameworks. The study focuses on comparing the performance of widely used quantum compilers. Energy consumption is estimated based on processor power models and execution time. Benchmark data from multiplier and quantum Fourier transform circuits are used to evaluate how compiler performance scales with increasing qubit counts and different hardware connectivity constraints. The results show that no single compiler is optimal for all tasks, but significant differences exist in runtime and energy efficiency. Compilers with better scalability, such as Qiskit in many cases, tend to consume less energy for larger circuits, while others exhibit rapidly increasing costs. Additionally, restricted hardware connectivity increases compilation time and energy consumption across most frameworks. The thesis concludes that energy consumption is an important, but unexplored aspect of quantum circuit compilation. As quantum systems grow in size, the classical overhead of compilation may become a limiting factor. Future work should focus on direct measurement of energy consumption and the development of energy-aware compilation techniques.