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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. Published by the American Physical Society 2025

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. Published by the American Physical Society 2025

Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low probability, high impact uncertainty realizations. To address this issue, we propose a new formulation for stochastic optimal power flow that explicitly distinguishes between "normal operation", in which automatic generation control (AGC) is sufficient to guarantee system security, and "adverse operation", in which the system operator is required to take additional actions, e.g., manual reserve deployment. The new formulation has been compared with the classical ones in a case study on the IEEE-118 and IEEE-300 bus systems. We observe that our consideration of extreme scenarios enables solutions that are more secure than typical chance-constrained formulations, yet less costly than solutions that guarantee robust feasibility with only AGC. 10 pages

Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low probability, high impact uncertainty realizations. To address this issue, we propose a new formulation for stochastic optimal power flow that explicitly distinguishes between "normal operation", in which automatic generation control (AGC) is sufficient to guarantee system security, and "adverse operation", in which the system operator is required to take additional actions, e.g., manual reserve deployment. The new formulation has been compared with the classical ones in a case study on the IEEE-118 and IEEE-300 bus systems. We observe that our consideration of extreme scenarios enables solutions that are more secure than typical chance-constrained formulations, yet less costly than solutions that guarantee robust feasibility with only AGC. 10 pages

AbstractAs the bones and muscles of our built environment, engineering structures support all kind of societal activities. However, they consume huge amounts of resources and significantly contribute to impact on our environment. Structural design codes play an important matter in this regard since they regulate the use of materials by use of prescribed decision rules. These relatively simple and generalized rules offer significant potential for improvement. Grounded on risk-based optimization approaches, this paper explores this potential in connection with the design of reinforced concrete floor systems. Assuming a large variety of realistic design situations, representative sets of such members are defined and designed according to the semi-probabilistic safety concept in the Eurocodes. The benefits of a risk-informed structural design compared to the use of these standardized decision rules are demonstrated in terms of material consumption and CO2 emissions.

AbstractAs the bones and muscles of our built environment, engineering structures support all kind of societal activities. However, they consume huge amounts of resources and significantly contribute to impact on our environment. Structural design codes play an important matter in this regard since they regulate the use of materials by use of prescribed decision rules. These relatively simple and generalized rules offer significant potential for improvement. Grounded on risk-based optimization approaches, this paper explores this potential in connection with the design of reinforced concrete floor systems. Assuming a large variety of realistic design situations, representative sets of such members are defined and designed according to the semi-probabilistic safety concept in the Eurocodes. The benefits of a risk-informed structural design compared to the use of these standardized decision rules are demonstrated in terms of material consumption and CO2 emissions.

In the last decade, the manufacturing capacity of silicon, the dominant PV technology, has increasingly been concentrated in China. This has led to PV cost reduction of approximately 80%, while, at the same time, posing risks to PV supply chain security. Recent advancements of novel perovskite tandem PV technologies as an alternative to traditional silicon-based PV provide opportunities for diversification of the PV manufacturing capacity and for increasing the GHG emission benefit of solar PV. Against this background, we estimate the current and future cost-competitiveness and GHG emissions of a set of already commercialized as well as emerging PV technologies for different production locations (China, USA, EU), both at residential and utility-scale. We find EU and USA-manufactured thin-film tandems to have 2 to 4% and 0.5 to 2% higher costs per kWh and 37 to 40%and 32 to 35% less GHG emissions per kWh at residential and utility-scale, respectively. Our projections indicate that they will also retain competitive costs (up to 2% higher)and a 20% GHG emissions advantage per kWh in 2050.