Estimating The Energy Consumption of Quantum Computing from A Full System Aspect

TL;DR

This paper introduces a comprehensive energy model for quantum computing in HPC, analyzing NISQ and FTQC regimes to guide energy-efficient quantum advantage.

Contribution

It presents the first full-system energy model distinguishing costs for NISQ and FTQC, with practical instantiations and insights for energy optimization.

Findings

NISQ energy dominated by QEM sampling multiplier

FTQC cost influenced by physical-qubit overhead and code distance

Model offers actionable insights for energy-efficient quantum computing

Abstract

Quantum computing promises disruptive capabilities, yet its energy footprint has received far less attention than its asymptotic speedups. We present a first-order, full-system energy model for quantum computing in an high performance computing (HPC) context. The model separates costs common to NISQ and FTQC, such as system maintenance and classical processing, from regime-specific ones such as error mitigation for NISQ and error correction for FTQC. We instantiate the model on 96- and 100-qubit Heisenberg time-evolution simulations on IBM Eagle r3 and a representative VQE workload, and sketch the FTQC energy pipeline. We find that NISQ energy is dominated by the QEM sampling multiplier, while FTQC cost shifts to physical-qubit overhead set by the code distance and magic states. Our model provides actionable insights into the energy consumption of both NISQ and FTQC workloads, and paves the way toward energy-efficient quantum advantage.