Summary Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. In this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least to more energy efficient than their classical counterparts; why feedback‐based quantum optimization is energy‐inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of 20. Finally, we use the NchooseK high‐level programming model to run optimization problems on both gate‐based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate‐based counterparts.

The growing convergence of high-performance, data analytics, and machine-learning applications is increasingly pushing computing systems toward heterogeneous processors and specialized hardware accelerators. Hardware heterogeneity, in turn, leads to finer-grained workflows. State-of-the-art server-less computing resource managers do not currently provide efficient scheduling of such fine-grained tasks on systems with heterogeneous CPUs and specialized hardware accelerators (e.g., GPUs and FPGAs). Working with fine-grained tasks presents an opportunity for more efficient energy use via new scheduling models. Our proposed scheduler enables technologies like Nvidia's Multi-Process Service (MPS) to pack multiple fine-grained tasks on GPUs efficiently. Its advantages include better co-location of jobs and better sharing of hardware resources such as GPUs that were not previously possible on container orchestration systems. We propose a Kubernetes-native energy-aware scheduler that integrates with our heterogeneous framework. Combining fine-grained resource scheduling on heterogeneous hardware and energy-aware scheduling results in up to 17.6% improvement in makespan, up to 20.16% reduction in energy consumption for CPU workloads, and up to 58.15% improvement in makespan, and up to 28.92% reduction in energy consumption for GPU workloads.

With the slowing of Moore's law and decline of Dennard scaling, computing systems increasingly rely on specialized hardware accelerators in addition to general-purpose compute units. Increased hardware heterogeneity necessitates disaggregating applications into workflows of fine-grained tasks that run on a diverse set of CPUs and accelerators. Current accelerator delivery models cannot support such applications efficiently, as (1) the overhead of managing accelerators erases performance benefits for fine-grained tasks; (2) exclusive accelerator use per task leads to underutilization; and (3) specialization increases complexity for developers.