A fault-tolerant quantum computer demonstrates commercial quantum advantage before 2032.
Quantum systems currently struggle with error correction and noise. Resolution requires a commercially viable deployment outperforming classical supercomputers on a practical industry task.
Across 6 frontier models answering independently, the panel thinks this is unlikely: the median is 17%.
They do not agree: GPT puts it at 63% and DeepSeek at 9%, 54 points apart. A spread that wide usually means the question turns on something none of them can see yet.
Nothing is settled until 24 Oct 2036, when the outcome is checked against sources and every number above gets a permanent score.
What each one said
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GPTOpenAI
The decisive variable is whether error-corrected logical qubits scale fast enough to beat classical cost on a paid-use case.
- IBM, Google, Quantinuum and PsiQuantum must turn demonstrations into reliable, fault-tolerant systems before 24 October 2036.
- Logical-qubit roadmaps target this decade, but practical advantage also needs fast decoding, low overhead and a buyer-valued workload.
- The base rate for decade-ahead hardware roadmaps is mixed; a 2028–30 logical-qubit breakthrough would raise this sharply.
63% -
GeminiGoogle
Success hinges on scaling beyond 1,000 logical qubits with real-time decoding on economically valuable problems by 2031.
- Leading roadmaps from IBM, Quantinuum, and Google target early fault tolerance by 2029-2030, but hardware timelines routinely slip.
- Commercially outperforming frontier exascale classical methods requires vast circuit depth that error thresholds cannot yet sustain.
- A demonstrated breakthrough in low-overhead neutral atom or topological surface codes by 2028 would sharply raise these odds.
27% -
GrokxAI
Needs million-qubit-scale FTQC running a real industry workload that beats classical HPC by 2031.
- IBM/Google/PsiQuantum roadmaps target logical qubits ~2029 but not commercial-scale FT machines until later.
- No precedent for error-corrected systems outperforming supercomputers on chemistry/optimization; NISQ advantage claims keep failing.
- A 2028+ logical-qubit demo that immediately maps to a paid industry use-case would raise this sharply.
18% -
ClaudeAnthropic
Hinges on error-corrected logical qubits scaling fast enough to beat classical systems on a real industry task by 2032.
- Current FTQC roadmaps (IBM, Google, Quantinuum, PsiQuantum) target thousands of logical qubits only around 2029-2033, with commercial advantage lagging further.
- No quantum system has yet demonstrated unambiguous commercial advantage on a practical task versus classical supercomputers, so this requires a first-ever milestone.
- Error correction overhead, decoherence, and classical algorithm improvements historically push 'quantum advantage' claims later than announced, favoring a cautious…
15% -
DeepSeekDeepSeek
Whether error-corrected logical qubits scale from today's ~100 to thousands and get wrapped in sellable software by 2032.
- Google's Willow (Dec 2024) showed below-threshold surface-code scaling, but the field has ~100 physical qubits per logical qubit, so commercial tasks need millions of…
- IBM's roadmap targets 200 logical qubits by 2029 and 2,000 by 2033; Quantinuum and PsiQuantum aim for fault tolerance around 2029-2030, leaving little runway for…
- Classical HPC also improves yearly, raising the advantage bar; the 2019-2024 record shows repeated 'quantum advantage' claims being overtaken by better classical…
9% -
QwenAlibaba
Scaling from today's handful of logical qubits to thousands needed for commercial tasks in ~5 years has no credible precedent.
- IBM's own roadmap targets 'quantum advantage' around 2029-2033 with ~100K physical qubits; commercial deployment adds 1-2 years beyond that.
- Google's 2024 Willow chip showed improved error correction but still far from the logical qubit counts needed for practical chemistry or optimization tasks.
- A single hardware breakthrough (e.g., a viable topological qubit or dramatic error-rate reduction) could shift this, but no such result has been demonstrated at scale.
9%
6 of 6 models answered · 54 points between the highest and lowest. None was shown the market price.
Question sourced from a news sweep on 27 Oct 2026. Forecast by google/gemini-3.8-flash, anthropic/claude-sonnet-5, openai/gpt-5.6-terra, x-ai/grok-4.6, deepseek/deepseek-v4.1-flash, qwen/qwen3.8-max-0902 via OpenRouter.