Abstract With the demonstrated ability to perform calculations in seconds that would take classical supercomputers thousands of years, quantum computers namely hold the promise of radically advancing sustainable IT. However, quantum computers face challenges due to the inherent noise in physical qubits, necessitating error correction for reliable operation in solving industrial-scale problems, which will require more computation time, energy, and electronic components than initial laboratory-scale experiments. Yet, while researchers have modeled and analyzed the environmental impacts of classical computers using Life cycle assessment the environmental performance of quantum computing remains unknown to date. This study contributes to filling this critical gap in two ways: (1) by establishing an environmental profile for quantum computers based on superconducting qubits; and (2) by comparing it to a functionally equivalent profile of a state-of-the-art supercomputer. With the comparison based on the problem size, the paper shows how the usage time can drive an environmental advantage for quantum computers under specific scaling conditions and quantum error correcting codes. The results emphasize that quantum error correction hardware has a substantial environmental impact due to the numerous electronic components needed to achieve 100 logical qubits. This paper can serve as a basis for designing more environmentally friendly quantum computers and for establishing their environmental profiles, as well as those of the human activities that will use them.
Abstract With the demonstrated ability to perform calculations in seconds that would take classical supercomputers thousands of years, quantum computers namely hold the promise of radically advancing sustainable IT. However, quantum computers face challenges due to the inherent noise in physical qubits, necessitating error correction for reliable operation in solving industrial-scale problems, which will require more computation time, energy, and electronic components than initial laboratory-scale experiments. Yet, while researchers have modeled and analyzed the environmental impacts of classical computers using Life cycle assessment the environmental performance of quantum computing remains unknown to date. This study contributes to filling this critical gap in two ways: (1) by establishing an environmental profile for quantum computers based on superconducting qubits; and (2) by comparing it to a functionally equivalent profile of a state-of-the-art supercomputer. With the comparison based on the problem size, the paper shows how the usage time can drive an environmental advantage for quantum computers under specific scaling conditions and quantum error correcting codes. The results emphasize that quantum error correction hardware has a substantial environmental impact due to the numerous electronic components needed to achieve 100 logical qubits. This paper can serve as a basis for designing more environmentally friendly quantum computers and for establishing their environmental profiles, as well as those of the human activities that will use them.
While researchers in both industry and academia are racing to build Quantum Computing (QC) platforms with viable performance and functionality, the environmental impacts of this endeavor, such as its carbon footprint, e-waste generation, mineral use, and water and energy consumption, remain largely unknown. A similar oversight occurred during the semiconductor revolution and continues to have disastrous consequences for the health of our planet. As we build the quantum computing stack from the ground up, it is crucial to comprehensively assess it through an environmental sustainability lens for its entire life-cycle: production, use, and disposal. In this paper, we highlight the need and challenges in establishing a QC sustainability benchmark that enables researchers to make informed architectural design decisions and celebrate the potential quantum environmental advantage. We propose a carbon-aware quantum computing (CQC) framework that provides the foundational methodology and open research questions for calculating the total life-cycle carbon footprint of a QC platform. Our call to action to the research community is the establishment of a new research direction known as, sustainable quantum computing that promotes both quantum computing for sustainability-oriented applications and the sustainability of quantum computing.
While researchers in both industry and academia are racing to build quantum computing (QC) platforms with viable performance and functionality, the environmental impacts of this endeavor, such as its carbon footprint, e-waste generation, mineral use, and water and energy consumption, remain largely unknown. A similar oversight occurred during the semiconductor revolution and continues to have disastrous consequences for the health of our planet. As we build the quantum computing stack from the ground up, it is crucial to comprehensively assess it through an environmental sustainability lens for its entire lifecycle: production, use, and disposal. In this article, we highlight the need and challenges in establishing a QC sustainability benchmark that enables researchers to make informed architectural design decisions and celebrate the potential “quantum climate advantage.” We propose a carbon-aware quantum computing (CQC) framework that provides the foundational methodology and open research questions in calculating the total life-cycle carbon footprint of a QC platform. Our call to action to the research community is the establishment of a new research direction known as sustainable quantum computing (SQC) that promotes quantum computing for both sustainability-oriented applications and the sustainability of quantum computing itself.
Quantum computers offer the promise of accelerated solutions for types of problems that are computationally unaffordable or intractable for classical computers, such as the simulation of quantum mechanics itself, quantum chemistry, and mathematical challenges like factoring large numbers. However, the relatively high error rate per quantum computing operation in the systems of today and of those expected to be developed in the future remains a key obstacle. To address this challenge, quantum error correction (QEC), which enables the creation of error-corrected (logical) qubits from a collection of physical qubits, is required. Depending on the choice of QEC code, hundreds of physical qubits may be required to implement a single logical qubit. As a result, the race to the creation of quantum computing systems capable of outperforming classical computing systems is largely a race to create quantum computers at the scale needed to support QEC. The criticality of scaling is evident in the quantum computing roadmaps produced by major research labs in this field, such as the one from IBM shown in Fig. 1. Starting from systems with only a few physical qubits a few years ago, the roadmap projects quantum computers with around 100,000 physical qubits and thousands of logical qubits by 2033 or so.
Quantum computation has emerged as a prominent research field with the potential to revolutionize information processing. Recent advancements have focused on extending qubit coherence times and enhancing error correction and fault-tolerant protocols, achieving coherence time to gate time ratios as high as 10⁸ and reducing error rates to as low as 0.143% ± 0.003% per cycle for 101 qubit system. The next critical challenge lies in scaling up qubit systems to support large-scale quantum computation that exceeds the performance of classical supercomputer. In this study, we summarize the working mechanisms, physical properties, gate operations, and limitations of three leading qubit platforms—superconducting qubits, trapped-ion qubits, and neutral atom qubits. Through a systematic comparison, we identify the key challenges that hinder qubit system scalability. Finally, we outline future research directions to address these challenges, including exploring the fundamental physics underpinning long coherence times, improving control tools such as laser systems, enhancing material quality and fabrication precision, and investigating hybrid quantum architectures. We hope this study offers a valuable perspective for researchers working toward the development of scalable and reliable quantum computing systems.
Quantum algorithms are often represented in terms of quantum circuits operating on ideal (logical) qubits. However, the practical implementation of these algorithms poses significant challenges. Many quantum algorithms require a substantial number of logical qubits, and the inherent susceptibility to errors of quantum computers require quantum error correction. The integration of error correction introduces overhead in terms of both space (physical qubits required) and runtime (how long the algorithm needs to be run for). This paper addresses the complexity of comparing classical and quantum algorithms, primarily stemming from the additional quantum error correction overhead. We propose a comprehensive framework that facilitates a direct and meaningful comparison between classical and quantum algorithms. By acknowledging and addressing the challenges introduced by quantum error correction, our framework aims to provide a clearer understanding of the comparative performance of classical and quantum computing approaches. This work contributes to understanding the practical viability and potential advantages of quantum algorithms in real-world applications. We apply our framework to quantum cryptanalysis, since it is well known that quantum algorithms can break factoring and discrete logarithm based cryptography and weaken symmetric cryptography and hash functions. In order to estimate the real-world impact of these attacks, apart from tracking the development of fault-tolerant quantum computers it is important to have an estimate of the resources needed to implement these quantum attacks. This analysis provides state-of-the art snap-shot estimates of the realistic costs of implementing quantum attacks on these important cryptographic algorithms, assuming quantum fault-tolerance is achieved using surface code methods, and spanning a range of potential error rates. These estimates serve as a guide for gauging the realistic impact of these algorithms and for benchmarking the impact of future advances in quantum algorithms, circuit synthesis and optimization, fault-tolerance methods and physical error rates.
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.