Why quantum scales on compute-per-watt, not qubit count

Date:
Fri, 18 Sep 2026 14:04:33 +0000

Description:
AI never planned for its own success. Quantum still can, if the industry
moves now.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter The tech industry was not ready for what came after ChatGPT. Power grids, cooling systems and data centers
are being rebuilt right now because none of them were designed for the demand that arrived.

UN researchers expect AI to double the power and water that data centers use by 2030. Had the industry known that demand was coming, it would have
designed for it. Latest Videos From TechRadar Watch full video here:

Quantum computing is early enough to do exactly that. Matt Rijlaarsdam Social Links Navigation

CEO and co-founder of QuantWare. AI hyperscalers care about one thing: how much useful computation they get for each dollar invested. You may like Why the future of computing is hybrid Why business leaders need to take quantum seriously Quantum's greatest breakthrough may be trust

This total cost of ownership is dominated by depreciation and power usage. In the current infrastructure buildout, they have sufficient access to capital
to fund the capital expenditure but insufficient access to power.

This constraint is so acute that data centers in space are becoming an economic possibility if growth continues at this pace. But even if the AI industry matures and growth slows, total cost of ownership will dominate. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro
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This directly translates into capital requirements and compute-per-watt being the most important metrics. The quantum industry should care less about qubit counts and more about compute-per-watt However, quantum computers are still sold on qubit count, and qubit count alone says nothing about economic
returns on a system. What will matter at scale is how much compute-per-watt the end user gets and this is not driven by qubit counts alone.

Take superconducting qubit processors, for instance, which are one of the quantum computing platforms most suitable for computation. They have been stuck on around 100 qubits for almost a decade. This is because on todays chips, more than 90% of the surface is taken up by the wiring that controls the qubits and reads their answers, rather than by the qubits themselves.
What to read next Quote of the day by PsiQuantum CEO Jeremy OBrien: "If you think the goal is to get to the top of the Empire State Building, you build longer ladders" Why the next computing revolution will be hybrid, human and slightly unpredictable The quantum deadline is unclear. The need to prepare isnt.

The usual way around the problem is to network many small processors
together. However, networking is lossy and gives sparse connections. Thats
bad in classical chips, and exponentially bad in quantum chips. The work of holding the system together grows faster than the qubits it adds, and more of the power goes into running the machine than into computing with it.

This gives you higher qubit counts, but those are not equivalent to systems built with less networking: it costs you compute-per-watt. The quantum industry should care more about experience curves An experience curve is a simple idea: every time the total number made doubles, the cost of each one falls by a fixed amount. It is what drove down the price of solar panels and batteries, and quantum will not be an exception, but only if we build at the volumes that let the curve work.

Optimistic quantum roadmaps never talk about price reductions, when it is one of the most important problems our industry needs to solve. On todays price per qubit, a million-qubit machine would cost somewhere between 100 billion and a trillion dollars. The cost per qubit has to fall at least a hundredfold in the coming years for quantum computers to be economically viable, which is doable, but not solved by more laboratory proof of concepts.

The transistor is the clearest case in computing itself. A single transistor once cost around a dollar. Today a chip carries billions of them, and each
one costs a fraction of a cent. That fall came from decades of making more of them, driving down the cost year after year.

Costs fall the way they fell in classical computing: volume manufacturing of standardized parts combined with compounding (but not stepwise) technical progress. This requires an open architecture in which specialist companies each build one layer of the machine.

This Quantum Open Architecture processors from one company, cryogenics from another, control electronics from a third is what will drive specialization and in turn, drive the required economies of scale.

With the field transitioning from science to engineering, there are plenty of cool one-off demos in startups today; there are very few companies actually building the supply chain. It matters to do this now Quantum computers will change the world. They have the promise to change the way we do drug discovery, material development, and machine learning.

This also ties back to AI. The same workloads straining the grid today are
the kind of heavy computation a quantum machine could one day take on, at a fraction of the power, once the economics are there. Getting there as fast as possible will create a massive amount of value and make all of our lives better.

The technology is progressing. But we have not focused on the economic
metrics that matter, and if we dont do so soon, we risk building an industry that makes expensive demos rather than actually useful computers. Qubit
counts alone dont matter. Building supply chains takes years, if not decades.

Whether the industrial fabrication, standardized parts and processors with exponentially more compute-per-watt will be there to achieve the economies of scale will be determined by choices made in the next few years. Best business computers: leading desktop PCs organizations. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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