Authors: Rolando Pablo Hong Enriquez (Hewlett Packard Labs), Rosa Badia (Barcelona Supercomputing Center), Barbara Chapman (Hewlett Packard Enterprise), Kirk Bresniker (Hewlett Packard Labs), Aditya Dhakal (Hewlett Packard Labs), Eitan Frachtenberg (Hewlett Packard Labs), Ninad Hogade (Hewlett Packard Labs), Gourav Rattihalli (Hewlett Packard Labs), Pedro Bruel (Hewlett Packard Labs), Alok Mishra (Hewlett Packard Labs), Dejan Milojicic (Hewlett Packard Labs)
Abstract: 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 due to 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. A way to achieve this is by using Parameterized Quantum Circuits (PQCs) in optimization or even machine learning tasks.
The energy consumption of quantum algorithms has been poorly studied. Here 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 2× to 4× more energy efficient than their classical counterparts. Why Feedback-Based Quantum Optimization is energy-inefficient and how a technique like Rosalin, could boost the energy-efficiency of other algorithms by a factor of ≥ 20×
Long Description: 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 due to 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. A way to achieve this is by using Parameterized Quantum Circuits (PQCs) in optimization or even machine learning tasks.
The energy consumption of quantum algorithms has been poorly studied. Here 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 2× to 4× more energy efficient than their classical counterparts. Why Feedback-Based Quantum Optimization is energy-inefficient and how a technique like Rosalin, could boost the energy-efficiency of other algorithms by a factor of ≥ 20×
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