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Adding physical qubits does not automatically make a quantum computer more useful. What matters is how much reliable computation a system completes in a given time, and how much total energy that work consumes. “Compute-per-watt” is a helpful way to frame this, but it is a framing, not a settled benchmark. Published sources on the topic do not yet establish a comparable compute-per-watt figure across quantum platforms, so any comparison has to state its own terms.
What “compute-per-watt” means, and what it does not yet mean
The idea treats a quantum system as a machine that turns electrical power into solved problems. The numerator is useful work. The denominator is the energy the system draws to do it. Neither side is standardized yet.
The closest thing to a formal definition is IEEE P3329, “Standard for Quantum Computing Energy Efficiency.” The IEEE Standards Association lists it as an active Project Authorization Request (PAR). Its scope statement reads: “This standard defines energy efficiency metrics for quantum computing (gate-based, quantum annealing, quantum simulation). It compares the performance of the computation to its energy consumption.” Because the project is still active, it defines the scope of metrics in progress. It is not a finished, published standard.
Why qubit count is a poor proxy for useful work
Physical qubits are the hardware devices. Useful computation usually runs on logical qubits, which are encoded across many physical qubits so that errors can be detected and corrected. Physical qubits are therefore used redundantly: one logical qubit can require many of them, and the ratio depends on the hardware error rate and the error-correction code in use. A machine with a large physical count but high error rates can deliver less usable computation than a smaller machine that keeps errors under control.
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Microsoft Quantum’s technical discussion, “The scalable logical qubits that will enable utility-scale quantum computing,” treats reliability, scale, capability and performance as coupled dimensions. It cautions against judging a platform on any one of them. It also identifies trade-offs among qubit count, fidelity, runtime, code overhead and decoder latency. This is a company-published technical framework rather than an industry standard, but it explains why a headline qubit number misleads.
A qubit count leaves out at least four things:
- Logical error rate: how often the encoded computation fails.
- Capability: whether the system can run repeated error-correction cycles and supported fault-tolerant logical operations.
- Runtime: how long an end-to-end calculation takes once decoding and feedback are included.
- Overhead: how many physical qubits and control resources each logical qubit consumes.
Where the energy goes: defining the system boundary
Energy per unit of computation depends entirely on what is counted. The processor chip is only part of the picture. The IEEE P3329 scope explicitly includes classical and quantum control chains. The 2026 preprint discussed below measures the energy consumed by the hardware. A credible boundary should state which of the following subsystems are included:
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- The quantum processor itself
- Cryogenic or other environmental systems, where the platform needs them
- Control electronics that generate and route signals
- Readout hardware
- Classical decoding and control that process error-correction data and feed results back into the computation
Wiring and networking overhead
In a TechRadar Pro opinion piece published September 18, 2026, Matt Rijlaarsdam argues that wiring and networking overhead can reduce compute-per-watt even as qubit counts rise. He states that more than 90% of a superconducting chip’s surface is taken up by wiring, and he gives an illustrative cost range for a million-qubit system. These are the author’s claims. They have not been independently verified in the published sources on this topic, so treat them as a hypothesis to test against platform data rather than established fact.
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A preprint’s definition of efficiency
A 2026 arXiv preprint by Miquel Carrasco-Codina and coauthors, “Energy efficiency of quantum computers” (dated May 14, 2026), defines the metric directly:
“We define the energy efficiency of a quantum computer as the ratio of the number of algorithms it can perform during a given time over the energy consumed by the hardware during this time.”
In this definition the numerator counts completed algorithms over a time window, not installed qubits. The denominator is hardware energy over the same window. It is a useful template, but it comes from a preprint and counts hardware energy rather than the full classical stack. Read alongside the IEEE scope, both definitions point the same way: the processor chip alone may not represent what an end user gets per watt.
How to compare two systems fairly
No single measurement protocol is prescribed for quantum systems. A fair comparison therefore needs the same workload and target reliability on both sides, plus an explicit statement of each axis below.
| Axis | What to report | Why it matters |
|---|---|---|
| Useful work | Algorithms or workload completed within a stated time window | Installed physical qubits do not show how much work finished |
| Reliability | Logical error rate and target end-to-end success probability | A large device with uncontrolled errors produces unreliable output |
| Capability | Repeated error-correction cycles and supported logical operations | Shows whether fault-tolerant computation is achievable on the platform |
| Speed | Logical cycle time and total runtime, including decoding and feedback | Decoder latency is one of the trade-offs that can change total runtime |
| Energy boundary | Which quantum and classical subsystems are counted in the energy figure | Determines whether two energy figures can be compared at all |
| Cost and overhead | Physical-to-logical qubit ratio, control requirements, and number of repetitions | Shows the resources needed to reach the same result |
If a vendor or article reports only a qubit count, or an energy figure without a stated boundary, the number cannot support a comparison of useful computation per watt.
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What the U.S. roadmap does and does not establish
On September 17, 2026, the U.S. Department of Energy Office of Science published “The Quantum Inflection Point: Charting a Science-First Roadmap for the Nation,” written by Darío Gil, the department’s Under Secretary for Science. The document sets a milestone-driven roadmap toward a scientifically relevant, error-corrected quantum computer by 2028. It advocates integrating quantum systems with high-performance computing in hybrid workflows. It also calls for technology neutrality across superconducting, neutral-atom, trapped-ion, photonic and spin-qubit approaches.
Gil’s statement reads: “Our goal is not simply to build the largest quantum computer; it is to solve problems that are otherwise completely intractable.”
Treat this as a government roadmap and target. It states what the agency intends to reach and by when. It does not report that the 2028 goal has been met. Its emphasis on solved problems rather than machine size is consistent with the compute-per-watt framing, but it does not adopt compute-per-watt as an official metric.
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The claim-by-claim sources cited above are: the TechRadar Pro opinion piece by Matt Rijlaarsdam (September 18, 2026); the IEEE Standards Association’s P3329 project page; the Carrasco-Codina et al. arXiv preprint (May 14, 2026); the DOE Office of Science roadmap (September 17, 2026); and Microsoft Quantum’s technical discussion.
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