Quantum computers are emerging as a promising new technology due to their ability to solve complex problems that exceed the capabilities of classical systems in terms of time. Among various implementations, superconducting qubits have become the leading technology due to their scalability and compatibility with quantum error correction mechanisms. Although time has traditionally been the primary focus, energetic efficiency is becoming an increasingly important consideration, especially with the possibility of a quantum energetic advantage. In this article, the energy consumption of the Semiclassical Quantum Fourier Transform was analyzed on a superconducting quantum computing platform based on cat qubits. Quantum error correction mechanisms were studied and considered in the energy estimations. The results show how the energy consumption scales with the number of qubits and how the most relevant parameters required for qubit stabilization, gate implementation, and error correction codes contribute to the overall energy usage. An optimization method was developed to tune these parameters with the goal of minimizing energy consumption while maintaining qubit fidelities above a given threshold. Additionally, a comparative study with state-of-the-art classical computers indicates a potential quantum energetic advantage for systems with more than 26 qubits, assuming cryogenic systems operating at Carnot efficiency, with this energetic advantage arising before any computational advantage. This behavior persists even when realistic cryogenic systems and control electronics are taken into account.
Quantum computers are emerging as a promising new technology due to their ability to solve complex problems that exceed the capabilities of classical systems in terms of time. Among various implementations, superconducting qubits have become the leading technology due to their scalability and compatibility with quantum error correction mechanisms. Although time has traditionally been the primary focus, energetic efficiency is becoming an increasingly important consideration, especially with the possibility of a quantum energetic advantage. In this article, the energy consumption of the Semiclassical Quantum Fourier Transform was analyzed on a superconducting quantum computing platform based on cat qubits. Quantum error correction mechanisms were studied and considered in the energy estimations. The results show how the energy consumption scales with the number of qubits and how the most relevant parameters required for qubit stabilization, gate implementation, and error correction codes contribute to the overall energy usage. An optimization method was developed to tune these parameters with the goal of minimizing energy consumption while maintaining qubit fidelities above a given threshold. Additionally, a comparative study with state-of-the-art classical computers indicates a potential quantum energetic advantage for systems with more than 26 qubits, assuming cryogenic systems operating at Carnot efficiency, with this energetic advantage arising before any computational advantage. This behavior persists even when realistic cryogenic systems and control electronics are taken into account.
Semiconductor spin qubits offer a promising route to compact and scalable quantum computers, combining long coherence times in isotopically purified silicon with compatibility with semiconductor manufacturing. Beyond computational speedup, their energy efficiency is an important consideration for future large-scale systems. We evaluate the power and energy requirements of a Loss-DiVincenzo spin-qubit quantum computer by analyzing the Quantum Fourier Transform (QFT) and benchmarking it against the classical Fast Fourier Transform (FFT). We develop a detailed energy budget, identifying cryogenic cooling as the dominant baseline contribution before quantum error correction (QEC). The comparison between the QFT and the classical FFT reveals that, for any quantum workload operating under a polynomially bounded sampling regime, the crossover in energy consumption occurs at smaller problem sizes than the crossover in execution time. Thus, quantum computing can become energetically competitive before achieving a speedup in execution time. We then incorporate surface-code QEC and analyze the energy costs of QEC cycles, gate synthesis, and decoding. At scale, instrumentation and decoding become dominant contributions, while cryogenic cooling imposes a constraint on scalability. We show that optimizing the control-signal demultiplexing ratio can reduce the overall energy cost by approximately two orders of magnitude. For a system with about one million physical qubits, we estimate a power consumption of approximately 100 kW, at least one order of magnitude below that of today's highest-performance classical computing systems.
While extensive research over the past decades has been dedicated to developing scalable quantum computers, the question of their energetic performance has only gained attention more recently, but its importance is now recognized. In fact, quantum computers can only be a viable alternative if their energy cost scales favorably, and some research has shown that there is even a potential quantum energy advantage. In parallel, Rydberg atoms have recently emerged as one of the most promising platforms to implement a large-scale quantum computer. This work aims at contributing first steps to understand the energy efficiency of this platform, by investigating the energy consumption of the different elements of a Rydberg atom quantum computer. First, an experimental implementation of the Quantum Phase Estimation algorithm is analyzed, and an estimation of the energetic cost of executing it is calculated. Then, we derive an estimate of how the energy cost of performing the Quantum Fourier Transform scales with the number of qubits in the Rydberg platform. This analysis facilitates a comparison of the energy consumption of different elements within a Rydberg atom quantum computer, from the preparation of the atoms to the execution of the algorithm, and the measurement of the final state, enabling the evaluation of the energy expenditure of the Rydberg platform and the identification of potential improvements. Finally, we use the Quantum Fourier Transform as an energetic benchmark, comparing the scaling we obtained to that of the execution of the Discrete Fourier Transform in two state-of-the-art classical supercomputers. This comparison indicates that, in an ideal error-free scenario, a quantum energy advantage is achieved in the Rydberg platform for the Fourier Transform, in a regime where classical algorithms are still faster.
Understanding the energetic efficiency of quantum computers is essential for assessing their scalability and for determining whether quantum technologies can outperform classical computation beyond runtime alone. In this work, we analyze the energy required to solve the Boson Sampling problem, a paradigmatic task for quantum advantage, using a realistic photonic quantum computing architecture. Using the Metric-Noise-Resource methodology, we establish a quantitative connection between experimental control parameters, dominant noise processes, and energetic resources through a performance metric tailored to Boson Sampling. We estimate the energy cost per sample and identify operating regimes that optimize energetic efficiency. By comparing the energy consumption of quantum and state-of-the-art classical implementations, we demonstrate the existence of a quantum energetic advantage -- defined as a lower energy cost per sample compared to the best-known classical implementation -- that emerges before the onset of computational advantage, even in regimes where classical algorithms remain faster. Finally, we propose an experimentally feasible Boson Sampling architecture, including a complete noise and loss budget, that enables a near-term observation of quantum energetic advantage.
The question of the energetic efficiency of quantum computers has gained increasing attention recently. A precise understanding of the resources required to operate a quantum computer with a targeted computational performance and how the energy requirements can impact the scalability is still missing. In this work, one implementation of the quantum Fourier transform algorithm in a trapped-ion setup was studied. The main focus was to obtain a theoretical characterization of the energetic costs of quantum computation, based on actual experimental measurements performed on a similar trapped-ion setup.The energetic cost of the computation was estimated by analyzing the components of the setup and all the steps involved, from the cooling and preparation of the ions to the execution of the algorithm and readout of the result. In the Noisy Intermediate-Scale Quantum regime, a potential scaling of the energetic costs was argued and used to find a possible threshold for an energetic quantum advantage against state-of-the-art classical supercomputers. Remarkably, this threshold appears to be lower than the one for which computational time advantage is expected.
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 promises to revolutionize several scientific and technological domains through fundamentally new ways of processing information. Among its most compelling applications is digital quantum simulation, where quantum computers are used to replicate the behavior of other quantum systems. This could enable the study of problems that are otherwise intractable on classical computers, transforming fields such as quantum chemistry, condensed matter physics, and materials science. Despite this potential, realizations of practical quantum advantage for relevant problems are hindered by imperfections of current devices. This also affects quantum hardware based on superconducting circuits which is among the most advanced and scalable platforms. The envisaged long-term solution of fault-tolerant quantum computers that correct their own errors remains out of reach mainly due to the associated qubit number overhead. As a result, the field has developed strategies that combine quantum and classical resources, exploit hardware-native operations, and employ error mitigation techniques to extract meaningful results from noisy data. This doctoral thesis contributes to this broader effort by exploring methods for advancing quantum simulation across the full computational stack, including hardware-level innovations, refined techniques for noise modeling and error mitigation, and algorithmic improvements enabled by efficient measurement processing.