How close are fault-tolerant quantum computers?
The core physics milestone has been passed but the engineering has not: error correction that improves as you add qubits was demonstrated on real hardware in December 2024 (Google's Willow), and small groups of logical qubits now outperform their physical parts (Microsoft/Quantinuum). Machines with hundreds of logical qubits — where usefulness begins — are vendor roadmap targets for around 2029-2030, not demonstrations (as of August 2026).
Below-threshold error correction: yes — December 2024 For the first time, making the error-correcting code bigger made the logical error rate exponentially smaller — the prerequisite the whole fault-tolerance programme rests on, demonstrated with real-time decoding. The honest caveats: this was a quantum memory (storing a state, not computing with it), a single logical qubit, and the logical lifetime beat the best physical qubit by 2.4x, not by the orders of magnitude algorithms need.
Logical qubits beating physical ones: yes — 2024 onwards Microsoft and Quantinuum encoded 12 logical qubits on a 56-qubit trapped-ion machine and showed entangled logical circuits with error rates 22x below the physical baseline, plus a small end-to-end chemistry demonstration using two of them. Caveats: shallow circuits, restricted gate sets, and heavy use of error detection with discarded runs — valuable evidence, far from universal fault-tolerant computation.
Universal logical gates: first ingredients shown 2025 Error-corrected memories and Clifford circuits are not enough for universal computation — that requires non-Clifford gates, supplied via 'magic states'. Quantinuum demonstrated producing these at logical error rates below physical levels in 2025, removing a major in-principle blocker. The open engineering problem is volume: large algorithms consume magic states by the million, and no device produces them at anything near that rate.
Useful-scale machines: promised for 2029-2030 These are serious engineering plans with published intermediate milestones — IBM's rests on qLDPC codes that cut physical-qubit overhead by up to ~90% versus surface codes, plus a real-time decoder design. But qLDPC decoding at scale, magic-state production rates, and multi-chip coupling are all unproven at the target sizes, and this industry's dates have slipped before. Treat 2029 as a target the vendors are staking credibility on, not a delivery date.
Cryptography-scale requirements: ~1M physical qubits on paper Resource estimates put the canonical 'useful' fault-tolerant workload — breaking RSA-2048 — at under a million physical qubits, down from 20 million in 2019 thanks to better arithmetic circuits and denser logical storage. That still leaves a gap of roughly three orders of magnitude in qubit count over today's best hardware, sustained at error rates and uptimes no system has shown. The estimate's steady fall is itself the number to watch: algorithmic improvements are doing as much work as hardware.
What does 'fault-tolerant' actually mean?
Today's physical qubits fail at rates around one error per thousand operations — some better, some worse; see how to read hardware specs. The algorithms with proven speedups need billions of operations to complete without an uncorrected error, so the raw hardware is short by six or seven orders of magnitude.
Fault tolerance closes that gap with redundancy: many physical qubits encode one logical qubit, errors are detected continuously via ancilla measurements, and a classical decoder fixes them in real time. The threshold theorem says this works — logical errors shrink exponentially as the code grows — but only once physical error rates are below a threshold. The price is overhead: roughly 100 to 1,000 physical qubits per logical qubit, depending on the code and the target error rate.
So 'how close?' is really three questions: is the hardware below threshold (yes, demonstrated), can logical qubits compute and not just persist (partially, at small scale), and can anyone build the sheer quantity required (not yet — that is what the roadmaps promise).
What has actually been demonstrated so far?
The trajectory since 2023, each step real and each step limited:
- 2024 (April-September): Microsoft and Quantinuum ran logical circuits with error rates up to hundreds of times below physical baselines, scaling to 12 entangled logical qubits — on shallow circuits with heavy postselection.
- 2024 (December): Google's Willow showed below-threshold error correction: a distance-7 surface code memory whose error rate halved with each code-distance step. One logical qubit, storing rather than computing.
- 2025: Quantinuum demonstrated high-fidelity logical magic states — the ingredient for universal logical computation — at small scale.
Note what is absent from the list: no deep logical circuit, no algorithm run fault-tolerantly end-to-end, no logical qubit count above the dozens. The demonstrations validate the recipe; nobody has yet cooked the meal.
Should I trust the 2029 dates?
Trust them as targets, not deliveries. IBM (Starling: 200 logical qubits, 100 million gates) and Quantinuum (Apollo: hundreds of logical qubits) have both staked public roadmaps on 2029-2030, with named intermediate systems along the way — which makes the claims falsifiable year by year, a healthy property. This industry has also missed dates before.
Rather than watching announcements, watch four metrics: the error-suppression factor per code-distance step (Willow's was ~2.14 — it needs to hold at larger distances), logical two-qubit gate error rates (memories are the easy part), magic-state production rates, and decoder throughput at scale. Steady movement on all four makes 2029-2030 plausible for first systems; a stall on any one pushes everything right.
What would change this page's answer: a demonstrated logical algorithm — dozens of logical qubits, thousands of logical gates, no postselection — would move useful fault tolerance from roadmap to imminent. Conversely, if error suppression saturates as codes grow, the timelines lengthen substantially. For what this unlocks if it works, see the quantum advantage assessment.
What can you actually use today?
The devices listed on the QPU index are almost all physical-qubit machines: roughly 100-1,000 noisy qubits with two-qubit gate errors around 0.1-1%, no error correction applied to your circuits. Error mitigation (clever post-processing) is standard on cloud services; error correction is not something you can switch on.
The narrow exception: Microsoft's Azure Quantum offers early access to small numbers of 'reliable' logical qubits on Quantinuum hardware — useful for experimenting with logical-qubit workflows, at scales of a dozen logical qubits rather than the hundreds that algorithms need.
Note that the lab on this site is a noiseless simulator: your circuits run error-free there, which is precisely the luxury real hardware does not have. Comparing a lab run against a hardware run of the same circuit is the fastest way to feel why fault tolerance matters.
Classifications follow the QPU137 editorial policy: every applied label carries a date, source, scale, and hardware, or it does not render. Found an error? Report it.