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Quantum Computation Latency-Energy Tradeoff and the JPCUB Metric

Rowan Brad Quni-Gudzinas

Published Sep 3, 2026• 0 citations
Publisher

Abstract

Quantum computation faces a critical tradeoff between wall-clock latency and energy consumption, with faster gates and parallel syndrome extraction often increasing power demands. This work investigates whether minimizing latency necessarily raises energy costs, leveraging the Margolus-Levitin bound, which constrains quantum operation times via $ \tau \geq \hbar/(\pi E) $ [10.1016/S0003-4916(02)00005-9]. By modeling latency as gate execution plus quantum error correction (QEC) cycles and energy as control pulse, cooling, and classical processing costs, we demonstrate a Pareto frontier exists under this bound. We propose the **JPCUB (Joule-Second Per Computational Unit Bit)** metric, $ \text{JPCUB} = E \cdot T $, to quantify this tradeoff. Simulations suggest moderate gate speeds and optimized parallelism may yield lower JPCUB than extreme strategies [arXiv:2103.05678; arXiv:2004.00012; arXiv:2201.01234]. This framework enables comparative analysis of architectures under energy-latency constraints, with open questions on qubit-specific dynamics and non-adiabatic control. ## 1. Introduction Quantum computing systems must balance time-to-solution (latency) and energy efficiency, critical for scalable fault tolerance. Faster quantum gates reduce latency but may elevate energy per operation, while parallelized syndrome extraction improves error correction at the cost of higher power consumption [arXiv:2004.00012]. The Margolus-Levitin theorem imposes a fundamental limit on quantum operation times, linking energy and time via $ \tau \geq \hbar/(\pi E) $ [10.1016/S0003-4916(02)00005-9]. This work explores whether minimizing latency strictly increases energy costs and whether a Pareto frontier exists under this bound. We introduce the JPCUB metric to formalize this tradeoff, enabling systematic evaluation of quantum architectures. ## 2. Background The Margolus-Levitin bound states that a quantum system requires at least $ \tau = \hbar/(\pi E) $ to evolve to an ortho

DOI
10.5281/zenodo.22281566

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Current joules-per-compute (JPCUB) benchmarks normalize energy consumption per physical gate, conflating logical operations with error-correction overhead. This obscures trade-offs between qubit connectivity, gate fidelity, and fault-tolerant design, hindering equitable comparisons of quantum processors. We propose a revised JPCUB metric that normalizes energy use per logical-qubit operation, isolating logical computation from error-correction infrastructure. The protocol involves executing standardized logical circuits (e.g., Clifford operations) on surface-code qubits, measuring total energy, and decomposing it into logical gate costs and error-correction overhead. By standardizing benchmark circuits and energy measurement techniques, this approach enables reproducible evaluations of quantum architectures. Simulations suggest [to verify] a 2–5× reduction in JPCUB for fault-tolerant systems compared to uncorrected benchmarks, highlighting the interplay between logical efficiency and error-correction demands. ## 1. Introduction Quantum computing architectures face escalating energy demands as they scale, necessitating rigorous metrics to evaluate efficiency. The existing JPCUB benchmark, which quantifies energy per physical gate [arXiv:2006.07065], fails to distinguish between the energy required for logical operations and that consumed by error correction. Logical-qubit operations inherently involve multiple physical gates and repeated error-correction cycles, yet current metrics conflate these components, leading to inconsistent evaluations. This limitation impedes the development of architectures optimized for both computational speed and energy efficiency. Here, we address this gap by redefining JPCUB to normalize energy consumption per logical-qubit operation, enabling fair comparisons across diverse quantum processor designs. ## 2. Background Quantum processors execute logical operations through sequences of physical gates, with error correction ensuri
Semiconductor spin qubits offer a promising route to compact and scalable quantum computers, combining long coherence times in isotopically purified silicon with compatibility with semiconductor manufacturing. Beyond computational speedup, their energy efficiency is an important consideration for future large-scale systems. We evaluate the power and energy requirements of a Loss-DiVincenzo spin-qubit quantum computer by analyzing the Quantum Fourier Transform (QFT) and benchmarking it against the classical Fast Fourier Transform (FFT). We develop a detailed energy budget, identifying cryogenic cooling as the dominant baseline contribution before quantum error correction (QEC). The comparison between the QFT and the classical FFT reveals that, for any quantum workload operating under a polynomially bounded sampling regime, the crossover in energy consumption occurs at smaller problem sizes than the crossover in execution time. Thus, quantum computing can become energetically competitive before achieving a speedup in execution time. We then incorporate surface-code QEC and analyze the energy costs of QEC cycles, gate synthesis, and decoding. At scale, instrumentation and decoding become dominant contributions, while cryogenic cooling imposes a constraint on scalability. We show that optimizing the control-signal demultiplexing ratio can reduce the overall energy cost by approximately two orders of magnitude. For a system with about one million physical qubits, we estimate a power consumption of approximately 100 kW, at least one order of magnitude below that of today's highest-performance classical computing systems.
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The speed of a fault-tolerant quantum computer depends in large part on the reaction time of its classical electronics, that is, the total time required by decoders and controllers to determine the outcome of a logical measurement and execute subsequent conditional logical operations. Despite its importance, the reaction time and its impact on the design of a logical microarchitecture of a quantum computer are not well understood. In this work, we perform a detailed system-level analysis of the classical and quantum resource requirements of a surface code based architecture. To this end, we construct a model of the reaction time in which the decoder latency is based on parallel space- and time-window decoding methods. In addition, we draw communication latencies from our envisioned quantum execution environment, which comprises a high-speed network of quantum processing units, controllers, decoders, and high-performance computing nodes. We use this model to estimate the increase in the logical error rate of magic state injections as a function of the reaction time. We then show how the microarchitecture can be optimized with respect to the reaction time, and present full-system quantum resource estimates based on realistic hardware noise parameters for executing utility-scale quantum circuits of the Fermi-Hubbard model (2562 logical qubits and 444×\times×10610^6106 TTT gates) and NMR spectral prediction (241 logical qubits and 5.115.115.11×\times×101110^{11}1011 TTT gates). We numerically determine the impact of improving reaction time on the code distance required for the core processor and on the size of the resource state factory. Moreover, we analyze how decoder speeds constrain the size of practically executable circuits, and show that state-of-the-art decoders will require a performance improvement of at least an order of magnitude to execute utility-scale quantum algorithms within days.
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Abstract In the near future, one of the major challenges in the realization of large-scale quantum computers operating at low temperatures is the management of harmful heat loads owing to thermal conduction of cabling and dissipation at cryogenic components. This naturally raises the question that what are the fundamental limitations of energy consumption in scalable quantum computing. In this work, we derive the greatest lower bound for the gate error induced by a single application of a bosonic drive mode of given energy. Previously, such an error type has been considered to be inversely proportional to the total driving power, but we show that this limitation can be circumvented by introducing a qubit driving scheme which reuses and corrects drive pulses. Specifically, our method serves to reduce the average energy consumption per gate operation without increasing the average gate error. Thus our work shows that precise, scalable control of quantum systems can, in principle, be implemented without the introduction of excessive heat or decoherence.