Quantinuum, IonQ, IBM, Google: who is ahead in quantum?
In our quantum computing market deck, you will find everything you need to understand the market
SUMMARY
Quantinuum, IonQ, IBM, Google: who is ahead in quantum? Google is ahead on demonstrated quantum advantage, Quantinuum is ahead on practical logical-qubit execution, IBM is ahead on ecosystem and industrial roadmap, and IonQ is ahead on public trapped-ion fidelity claims.
The race does not have one clean winner because each company leads on a different layer of the stack. Google has the strongest proof that a quantum machine can beat classical computation on a checkable task, while Quantinuum has the strongest evidence of turning physical qubits into logical computation.
On a simple 23-criteria yes-count, Quantinuum comes first with 16 positive checks. Google and IonQ tie at 11, while IBM follows with 10, but that score should be read as breadth of public evidence rather than a weighted technical winner.
The most important pattern is that qubit count is no longer the decisive metric. The better question is how many reliable logical operations a machine can run before noise destroys the computation.
Google’s advantage is unusually deep scientifically. Its Willow error-correction work, Quantum Echoes claim, real-time decoding, and RCS results create the strongest “proof” package among the four companies.
Quantinuum’s advantage is more execution-oriented. Its trapped-ion architecture, all-to-all connectivity, logical-qubit demonstrations, Microsoft collaboration, and Helios metrics make it look closest to practical fault-tolerant execution.
IBM looks less impressive if we only count demonstrated logical-computing results today. But it looks much stronger if we score manufacturing, roadmap specificity, access, software ecosystem, dated targets, and the credibility of building a quantum platform at industrial scale.
IonQ has one of the strongest headline physical metrics in the field: a reported two-qubit fidelity above 99.99%. The problem is that this does not yet translate, in the public evidence, into the same depth of logical-qubit and repeated-error-correction proof shown by Google or Quantinuum.
The industry is transparent enough to compare directionally, but not enough to score every company on the same clean sheet. Each company emphasizes the metric that makes it look strongest, which means the comparison has to separate physics, logical execution, customer access, and roadmap credibility.
The most overread numbers are probably IonQ’s physical-fidelity and #AQ claims, IBM’s 2029 roadmap targets, and Google’s spectacular RCS timing headline. They all matter, but none alone proves useful fault-tolerant quantum computing today.
The real leadership test is converging toward logical error rates, logical two-qubit gates, repeated QEC cycles, real-time decoding, and useful application-level circuits. On that standard, the current read is: Google has the strongest breakthrough evidence, Quantinuum has the strongest logical execution evidence, IBM has the strongest industrialization plan, and IonQ still needs more public logical-computing proof.
This market map, featured in our quantum computing market deck, highlights top companies and startups in the quantum computing market
As of today, who is ahead in quantum computing?
As of today, Google is ahead in demonstrated quantum advantage, Quantinuum is ahead in practical logical-qubit and error-correction execution, IBM is ahead in ecosystem and roadmap industrialization, and IonQ is ahead on public trapped-ion fidelity claims but less proven on logical computing.
If we decide to judge them on 23 criteria, here is the yes-count ranking we get: Quantinuum 16, Google 11, IonQ 11, IBM 10.
Here are the details:
| # | Question | Quantinuum | IonQ | IBM | |
|---|---|---|---|---|---|
| 1 | Has working quantum processors today? | yes | yes | yes | yes |
| 2 | Publishes public hardware benchmarks? | yes | yes | yes | yes |
| 3 | Maintains a public quantum software stack? | yes | yes | yes | yes |
| 4 | Offers customer cloud access today? | yes | yes | yes | unclear |
| 5 | Shows 50+ qubit-scale capability? | yes | yes | yes | yes |
| 6 | Has a broad quantum user ecosystem? | yes | yes | yes | unclear |
| 7 | Has full-stack quantum control capability? | yes | yes | yes | yes |
| 8 | Demonstrates all-to-all qubit connectivity? | yes | yes | no | no |
| 9 | Reports 99.9% two-qubit fidelity? | yes | yes | unclear | unclear |
| 10 | Reports 99.99% two-qubit fidelity? | no | yes | no | no |
| 11 | Demonstrated beyond-classical sampling? | yes | unclear | unclear | yes |
| 12 | Demonstrated verifiable quantum advantage? | no | no | no | yes |
| 13 | Demonstrated real-time error correction? | yes | unclear | unclear | unclear |
| 14 | Demonstrated logical qubits beating physical? | yes | unclear | unclear | yes |
| 15 | Demonstrated repeated error correction cycles? | yes | unclear | unclear | yes |
| 16 | Demonstrated multiple logical qubits? | yes | unclear | unclear | unclear |
| 17 | Demonstrated logical two-qubit gates? | yes | unclear | unclear | unclear |
| 18 | Demonstrated scalable real-time decoding? | yes | unclear | unclear | yes |
| 19 | Published end-to-end FTQC architecture? | unclear | yes | yes | unclear |
| 20 | Has explicit 2029 FTQC target? | unclear | no | yes | no |
| 21 | Delivered useful fault-tolerant applications? | no | no | no | no |
| 22 | Runs million-gate logical circuits today? | no | no | no | no |
| 23 | Has commercial fault-tolerant quantum today? | no | no | no | no |
If you want more recent data on this point, please see our latest quantum computing market report.
What metrics actually tell us who wins quantum computing?
The metric that matters is not “qubit count” but “how many reliable logical operations can the machine execute before the computation dies.”
When we dug through the recent announcements, the useful pattern is clear: raw hardware quality matters, but it only becomes strategically decisive when it converts into logical qubits, logical gates, repeated correction cycles, and circuits that cannot be reproduced classically.
The best way to read the race is therefore in layers.
First, we check whether the physical machine is good enough: two-qubit fidelity, measurement fidelity, connectivity, crosstalk, coherence, and circuit depth.
Then we check whether that quality survives error correction: logical error rate, break-even versus physical qubits, number of logical qubits, logical two-qubit gates, and real-time decoding.
Finally, we check whether the system produces something externally meaningful: verifiable quantum advantage, customer access, repeatable benchmarks, and useful application-level circuits.
| Metric | Why it matters |
|---|---|
| Logical error rate per cycle / per gate | This is the closest metric to “can the computer run a long useful computation?” Physical fidelity is upstream; logical error rate is the thing fault-tolerant computing ultimately needs. |
| Break-even logical performance | A logical qubit only matters if it beats the best unencoded physical version. Google showed below-threshold scaling on Willow; Quantinuum has shown encoded computations beating unencoded ones. |
| Number of useful logical qubits | One beautiful logical qubit is a physics milestone; dozens of logical qubits start to look like a computing architecture. This is why Quantinuum’s 48 error-corrected / 94 error-detected logical-qubit claims matter. |
| Logical two-qubit gates | Single logical qubits are not enough. A fault-tolerant machine needs entangling operations between logical qubits, otherwise it cannot run general algorithms. |
| Repeated QEC cycles with real-time decoding | Error correction is only scalable if decoding happens fast enough while the processor is running. Google reported real-time decoding on Willow; IBM and Quantinuum have made real-time decoding and control central to their roadmaps. |
| Two-qubit gate fidelity | This is still the single most important physical-level metric because two-qubit gates are usually the noisiest operations. IonQ’s >99.99% claim is therefore technically important, but it is not the same as proving logical execution. |
| Circuit volume: width × depth × fidelity | A useful processor must run wide and deep circuits without collapsing into noise. IBM’s emphasis on 5,000+ two-qubit-gate workloads and Quantinuum’s RCS/fidelity results are more informative than qubit count alone. |
| Independent verifiability of advantage claims | Quantum advantage is only persuasive if the task is checkable or classically benchmarked hard. Google’s Quantum Echoes is strong because it was framed as a verifiable quantum-advantage algorithm, not just random sampling. |
| Cloud/customer access | This tells us whether the machine is a lab artifact or an operating platform. IBM, Quantinuum, and IonQ are more open here than Google; Google’s Willow access is still selective, not broad public cloud access. |
| Roadmap credibility: dated, quantified, architecture-backed targets | Roadmaps are weak when they are just qubit counts. They become useful when they include logical qubits, gate counts, architecture choices, decoding, manufacturing, and dates. IBM is strongest here with 200 logical qubits and 100M gates by 2029. |
As this chart shows, and as featured in our quantum computing market deck, search interest in quantum computing has grown significantly
Are quantum companies transparent on the metrics that matter?
No, not fully. The industry is transparent enough to compare directionally, but not transparent enough to score everyone on the same technical sheet.
The biggest problem is that each company chooses the metric where it looks strongest: Google publishes deep scientific milestones, Quantinuum publishes unusually concrete logical/error-correction evidence, IBM publishes roadmap and ecosystem metrics, and IonQ publishes very strong physical-fidelity and AQ claims.
The clean read is: Quantinuum and Google are the most technically transparent; IBM is the most roadmap-transparent; IonQ is commercially loud and technically selective.
That does not mean IonQ’s metrics are fake. It means the public evidence is less complete on the exact layer that decides fault-tolerant leadership: multi-logical-qubit execution and logical circuit performance.
| Company | Transparency score | Why |
|---|---|---|
| Quantinuum | 88 / 100 | Best transparency on the metrics that matter most for fault tolerance: physical qubits, gate fidelities, all-to-all architecture, customer availability, logical-qubit demonstrations, beyond-break-even encoded computation, and published papers/preprints. The main caveat is that some high-rate logical-qubit work relies on error detection, postselection, and code choices that need careful interpretation. |
| 84 / 100 | Very strong scientific transparency: Nature-level Willow QEC data, below-threshold scaling, distance-7 surface code, RCS benchmark, Quantum Echoes, and real-time decoder details. The weak point is access and operating transparency: Willow is not broadly available to public customers, and Google is less explicit than IBM on a dated commercial FTQC system roadmap. | |
| IBM | 78 / 100 | Best roadmap transparency: named systems, 2029 Starling target, 200 logical qubits, 100M gates, Nighthawk/Loon architecture, 300mm fabrication, and $10B five-year investment. But IBM has less public evidence than Google or Quantinuum on demonstrated high-quality logical-qubit execution today. |
| IonQ | 65 / 100 | Strong on headline physical metrics: >99.99% two-qubit fidelity and #AQ 64 are public and specific. But IonQ is less transparent on comparable logical-computing evidence: fewer public demonstrations of repeated QEC cycles, multi-logical-qubit algorithms, logical gates, or real-time decoding than Quantinuum or Google. |
If you want more recent data on this point, please see our latest quantum computing market report.
What are the most impressive quantum metrics published recently?
The most impressive recent quantum metrics are not the biggest numbers but the ones that move from physics demo toward fault-tolerant computation.
Over the last 18 months, Google and Quantinuum published the strongest “proof” metrics, IBM published the strongest industrialization metrics, and IonQ published the cleanest physical-fidelity headline.
The ranking below is intentionally strict. A metric ranks higher when it is closer to useful fault-tolerant computation, externally interpretable, difficult to fake with a narrow benchmark, and connected to a published paper or concrete system.
| Rank | Company and metric | Why this ranking | When |
|---|---|---|---|
| 1 | Google: Quantum Echoes ran 13,000× faster than the best classical algorithm on a top supercomputer | This is the strongest algorithmic milestone because Google framed it as a verifiable quantum-advantage task, not just a random benchmark. It matters because the output can be checked against physics structure, which makes the advantage claim more meaningful. | Oct. 2025 |
| 2 | Quantinuum: 48 error-corrected logical qubits and 94 error-detected logical qubits on 98 physical qubits | This is the strongest logical-qubit scaling metric. The reason it ranks just below Google’s advantage claim is that the codes and postselection details require interpretation, but the raw conversion of hardware into many logical qubits is exceptional. | Mar. 2026 |
| 3 | Google: below-threshold surface-code QEC on Willow, distance-7 with 0.143% error per cycle | This is one of the cleanest QEC results in the field: increasing code distance reduced logical error, which is exactly the direction fault tolerance requires. It is less “commercial” than Quantinuum’s many-logical-qubit claim, but scientifically very hard to beat. | Dec. 2024 / Nature 2025 |
| 4 | Google: Willow RCS benchmark in under five minutes vs 10 septillion years classical estimate | The number is spectacular and useful as a system-level stress test. It ranks below Quantum Echoes because RCS is less directly useful and has a history of classical-simulation debates. | Dec. 2024 |
| 5 | Quantinuum Helios: 98-qubit trapped-ion processor with all-to-all connectivity and 7.9×10⁻⁴ average two-qubit infidelity | This is a very strong full-system hardware metric because it combines scale, fidelity, and architecture. It is more meaningful than a single best-pair fidelity because it is averaged across operational zones. | Nov. 2025 |
| 6 | IonQ: >99.99% two-qubit gate fidelity without ground-state cooling | This is the best pure physical-fidelity metric in the set. It ranks below the logical-QEC metrics because high physical fidelity is necessary but not sufficient to prove logical computation. | Oct. 2025 |
| 7 | IBM: Starling target of 200 logical qubits and 100M gates by 2029 | This is the most concrete industrial FTQC target among the four companies. It ranks high because it specifies logical qubits and gate count, not just physical qubits, but it is still a roadmap rather than a demonstrated machine. | Jun. 2025 |
| 8 | Quantinuum + Microsoft: 12 highly reliable logical qubits | This was an important bridge from “a few logical qubits” to a multi-logical-qubit register. It ranks below Helios because Helios later pushed logical scale much further. | Sep. 2024 |
| 9 | Quantinuum + Microsoft: 800× better logical error rate than physical error rate | This is a clean break-even-style metric: error correction actually improved the computation rather than adding overhead and noise. It is slightly older, but still foundational for judging Quantinuum’s lead in logical execution. | Apr. 2024 |
| 10 | Google: real-time decoding on Willow with 63 µs average latency at distance-5 | This matters because fault tolerance is not just a code; it is a live control loop. It ranks below the headline QEC scaling because it is an enabling metric, not the final logical-computing result. | Dec. 2024 |
| 11 | Google AlphaQubit 2: real-time decoding faster than 1 µs per cycle up to distance 11 on commercial accelerators | This is a strong scalability signal for the classical side of QEC. It ranks lower because it is decoder infrastructure, not a full hardware demonstration of a useful logical computer. | Dec. 2025 |
| 12 | IBM: $10B five-year quantum commitment | This is not a physics metric, but it matters because quantum leadership now requires fabrication, packaging, software, partnerships, and M&A. It ranks below technical demonstrations because money alone does not prove performance. | Jun. 2026 |
| 13 | IBM: Loon chip demonstrates hardware elements for fault-tolerant quantum computing | This is a credible architecture milestone. It ranks below IBM’s Starling target because the announcement is less directly comparable to a demonstrated logical-qubit benchmark. | Nov. 2025 |
| 14 | IBM: Nighthawk with 120 qubits and 218 tunable couplers | Nighthawk matters because IBM is improving connectivity and workload complexity, not just adding qubits. It ranks below logical-QEC evidence because it is still physical-layer progress. | Nov. 2025 / Jan. 2026 access |
| 15 | IBM: Nighthawk designed for 5,000 two-qubit-gate workloads | This is a better IBM metric than qubit count because it speaks to circuit depth. It is still less impressive than logical execution because deep noisy circuits are not the same as fault-tolerant circuits. | Nov. 2025 |
| 16 | Quantinuum Helios: 99.9975% single-qubit fidelity and 99.921% two-qubit fidelity reported for commercial system | This is impressive because it is commercial-system fidelity, not just a lab-isolated headline. It ranks below IonQ’s 99.99% two-qubit claim on peak fidelity, but above many roadmap metrics because it is tied to a launched system. | Nov. 2025 |
| 17 | IonQ: #AQ 64 on Tempo | This is a strong application-style benchmark and a useful commercial signal. It ranks lower because #AQ is partly IonQ’s own framing and is less universally interpretable than logical error rate or verifiable advantage. | Sep. 2025 |
| 18 | IonQ: 100-qubit Tempo system associated with #AQ 64 | The scale plus AQ claim is notable. It is less impressive than the 99.99% fidelity paper because raw system size and aggregate AQ do not show fault-tolerant execution. | Sep. 2025 |
| 19 | Quantinuum: Helios real-time control engine for dynamic programs | This matters because practical QEC needs dynamic circuits, measurement-conditioned operations, and low-latency control. It ranks lower because the announcement is architectural unless paired with specific logical-circuit metrics. | Nov. 2025 |
| 20 | Quantinuum + NVIDIA: real-time QEC decoding with NVQLink / CUDA-Q integration | This is important because the decoding bottleneck is moving from theory into systems engineering. It ranks below Quantinuum’s logical-qubit results because it is an infrastructure proof point rather than the end output. | Nov. 2025 / Mar. 2026 |
| 21 | IBM: QEC decoding 10× speedup over current leading approach | This is meaningful because IBM’s 2029 roadmap depends on decoding speed. It ranks below Google’s published real-time latency because “10× speedup” is less directly comparable without the same full latency context. | Nov. 2025 |
| 22 | IBM: move to 300mm wafer fabrication to double development speed and raise chip complexity 10× | This is one of IBM’s strongest industrialization metrics. It ranks lower than QEC metrics because manufacturing speed does not prove quantum performance, but it matters for scaling. | Nov. 2025 |
| 23 | Google: Willow has 105 qubits with best-in-class QEC and RCS benchmarks, according to Google | This is a strong combined hardware benchmark. It ranks below the individual QEC and advantage metrics because “best-in-class” is broader and less precise. | Dec. 2024 |
| 24 | Quantinuum: 56-qubit H2-1 RCS result with 100× improvement over prior Google 2019 benchmark | This was a strong trapped-ion system-level benchmark and showed classical-simulation pressure. It ranks lower because it is just outside the strict 18-month window but still inside the 24-month fallback. | Jun. 2024 |
| 25 | Quantinuum: 56 trapped-ion qubits with high-fidelity all-to-all operation | This matters because all-to-all connectivity reduces routing overhead, which can preserve fidelity in real algorithms. It ranks below newer Helios metrics because Helios is larger and more recent. | Jun. 2024 |
| 26 | IBM: Heron R2 / 156-qubit processor with up to 5,000 two-qubit-gate circuit execution cited in 2024 reporting | This is a useful noisy-circuit depth metric. It ranks below Nighthawk because Nighthawk is newer and explicitly designed for more complex workloads. | Nov. 2024 / 2025 docs |
| 27 | IBM: Nighthawk early access through ibm_miami, with median T1 of 350 µs | This is a solid access-plus-quality signal. It ranks lower because coherence time alone is not a complete performance metric. | Jan. 2026 |
| 28 | Google: Willow Early Access Program for selected external researchers | This matters because Google moved from closed internal demonstrations toward external scrutiny. It ranks low because it is selective access, not broad cloud availability. | Mar.–Apr. 2026 |
| 29 | IonQ: roadmap claims tied to millions of qubits / tens of thousands of logical qubits by 2030 | This is ambitious and commercially important. It ranks low because it is a target, not a demonstrated metric, and the public logical-computing proof points lag the claim. | Jun. 2025 |
| 30 | IBM: open quantum advantage tracker and partner ecosystem around advantage claims | This is useful governance infrastructure: IBM is trying to make advantage claims auditable. It ranks last because the tracker is not itself a quantum-performance result. | Nov. 2025 |
If you want more recent data on this point, please see our latest quantum computing market report.
This chart, included in our quantum computing market deck, illustrates yearly VC funding for quantum computing startups
Which company has published the most impressive metrics so far?
Google has published the most impressive “quantum advantage” metrics; Quantinuum has published the most impressive “fault-tolerant execution” metrics.
If we force one aggregate winner across the last 18 to 24 months, we would put Google first by scientific breakthrough, Quantinuum first by logical-computing execution, IBM first by industrial roadmap, and IonQ first by physical trapped-ion fidelity headline.
Our aggregate ranking would be:
| Rank | Company | Why |
|---|---|---|
| 1 | Google has the two highest-impact proof metrics: Willow’s below-threshold surface-code QEC and Quantum Echoes’ 13,000× verifiable quantum-advantage claim. That combination is rare: one metric says the hardware is moving toward fault tolerance, the other says the machine can already do a classically hard, checkable task. | |
| 2 | Quantinuum | Quantinuum is closest to turning hardware quality into logical computing. Its 12 logical qubits with Microsoft, Helios system fidelity, 48 error-corrected logical qubits, 94 error-detected logical qubits, and fault-tolerant algorithm work make it the strongest execution story. |
| 3 | IBM | IBM’s metrics are less about “look what we already proved” and more about “look how we will industrialize it.” The 2029 Starling target, 200 logical qubits, 100M gates, Nighthawk/Loon, 300mm fabrication, and $10B commitment make IBM the most credible platform-scale roadmap. |
| 4 | IonQ | IonQ has one of the most impressive single physical metrics in the market: >99.99% two-qubit fidelity. But if we score only demonstrated progress toward logical computing, IonQ has fewer public proof points than Google or Quantinuum. |
If you want more recent data on this point, please see our latest quantum computing market report.
Who publishes impressive-looking metrics that may not mean much?
The company we would be most careful with is IonQ, not because its headline metrics are fake, but because they are easy to overread.
A >99.99% two-qubit gate fidelity is genuinely impressive. The problem is that a best-in-class physical gate metric does not automatically prove repeated error correction, logical gates, useful logical circuits, or a scalable fault-tolerant control stack.
The second metric to treat carefully is #AQ. IonQ’s #AQ 64 is useful as an application-style benchmark, but it is not as universally decisive as logical error rate, logical gate fidelity, repeated QEC cycles, or verifiable quantum advantage.
It compresses several performance dimensions into one number, which makes it easy for non-specialists to read as “IonQ is ahead overall,” when the public logical-computing evidence does not support that stronger conclusion yet.
We would also be careful with IBM roadmap metrics. IBM’s roadmap is the best in the industry, but targets like “200 logical qubits and 100M gates by 2029” are not the same as demonstrated capability today.
IBM deserves credit for specificity, dated milestones, ecosystem access, and industrial planning; it should not be scored as already ahead in logical execution until it publishes comparable logical-qubit and logical-gate results.
Google has the opposite problem: its metrics are scientifically strong, but some headlines can sound more commercially mature than they are.
The Willow RCS “10 septillion years” number is an extraordinary benchmark, but it is not a useful application. Google’s own Willow access page also says the processor is “not yet available to the public,” which matters when comparing ecosystem maturity.
If you want more recent data on this point, please see our latest quantum computing market report.
This chart, included in our quantum computing market deck, looks at IonQ’s strategy in quantum computing
OUR METHODOLOGY
This analysis tests who is currently ahead in quantum computing among Quantinuum, IonQ, IBM, and Google, based on the evidence available today. We compare the companies across demonstrated technical results, operating-platform maturity, and dated roadmap credibility.
The 23-criteria table is a breadth check, not a weighted winner model. It shows where public evidence exists, while the final ranking gives more weight to proof points that move closer to fault-tolerant computing.
For recent metrics, we focused mainly on the last 18 months, while keeping a small number of 2024 results where they explain a company’s current position. We ranked mixed signals — benchmarks, logical-qubit results, fidelity claims, access programs, investment commitments, and roadmap targets — by how directly they show progress toward useful, reliable quantum computation.
We used company-specific metrics when they were concrete and sourceable, but weighted them more carefully when they were less standardized across the industry. “Unclear” means the public evidence was not specific enough to score confidently; it does not mean the company has not done the work.
We gave the most weight to metrics that move from physical hardware toward logical computation: logical error rates, break-even logical performance, repeated QEC cycles, real-time decoding, logical two-qubit gates, verifiable quantum advantage, and application-level circuits.
We treated roadmap claims differently from demonstrated results. IBM’s Starling target, IonQ’s future logical-qubit ambitions, and Google or Quantinuum architecture claims matter, but they are not scored the same way as published logical-qubit, QEC, fidelity, or advantage demonstrations.
We also separated customer-access evidence from technical leadership evidence. IBM, Quantinuum, and IonQ look stronger on platform availability, while Google looks stronger on scientific proof but weaker on broad public access to Willow.
Key sources used for this analysis include: Nature on Google’s below-threshold surface-code QEC, arXiv on related quantum error-correction work, Google’s Willow chip announcement, Google Research on making quantum error correction work, Google on Quantum Echoes and verifiable quantum advantage, Google Research on verifiable quantum advantage, Nature on Google’s later quantum-advantage work, and Google Quantum AI on Willow early access.
Additional key sources include: Quantinuum’s Helios system page, Quantinuum’s Helios launch note, arXiv on Quantinuum logical-qubit work, Quantinuum on skinny logical codes, arXiv on later Quantinuum logical-code work, Microsoft Azure Quantum on 12 logical qubits with Quantinuum, Microsoft on Quantinuum logical qubits with 800× better error rate, arXiv on Quantinuum/Microsoft logical-qubit results, Quantinuum’s 56-qubit trapped-ion announcement, and arXiv on Quantinuum’s 56-qubit benchmark work.
Other key sources include: IonQ on >99.99% two-qubit gate fidelity, arXiv on IonQ’s high-fidelity trapped-ion work, IonQ on #AQ 64, IBM on its large-scale fault-tolerant quantum computing roadmap, and IBM’s $10B quantum commitment announcement.
This chart, included in our quantum computing market deck, illustrates yearly funding for quantum computing startups
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- Quantinuum, IonQ, IBM, Google: who is ahead in quantum?
