Q-CTRL executes 100-qubit QFT on IBM Heron; proprietary compilation doubles prior demonstration scale

【Article Title】 Q-CTRL executes 100-qubit QFT on IBM Heron; proprietary compilation doubles prior demonstration scale 【Article Body】 AI TRANSLATION This article is an AI-generated translation of the original Japanese article. For the authoritative version, please refer to the original. Read the original Japanese article → (https://quantumindex.jp/qctrl-100-qubit-qft-ibm-heron/) Q-CTRL executed the quantum Fourier transform (QFT) on up to 100 qubits on a 156-qubit IBM Heron r3 processor. By combining its proprietary compilation techniques with error suppression, the company reports that the correct target frequency emerged as the sole most frequently observed outcome across all test circuits. Executive summary In the experiments, periodic signals were encoded into registers of 50, 80, and 100 qubits, and the QFT was used to extract the target integer frequency. For all test circuits that included 100 qubits, the correct bitstring was the unique most frequent measured outcome. For 50 qubits the correct result appeared 8.4 times more often than the next-most-frequent incorrect result, and the unitary process fidelity was 11.4%. For 80 qubits those figures were 7.5 times and 1.8%, respectively. Q-CTRL adopted a Convolutional QFT that uses a single ancilla qubit. By aggregating operations into small kernels that are swept across the register sequentially, the design reduces the entangling gates affecting each qubit. The company also combined dynamical decoupling, truncation of small rotations, precise timing adjustments of execution, and readout error mitigation. According to Q-CTRL, the number of CX gates required for an n-qubit QFT on IBM Heron is n²−n＋2, which is close to the n²−n of an ideal all-to-all connected layout. By suppressing routing overhead due to connectivity constraints, they say they doubled register width compared with previous on-device QFT demonstrations. Key points • Used a 156-qubit IBM Heron r3 and executed QFT on up to 100 qubits. • For all test circuits that included 100 qubits, the correct target frequency was the sole most frequent outcome. • Process fidelity was 11.4% for 50 qubits and 1.8% for 80 qubits. The article does not report the process fidelity for 100 qubits. • Combined Convolutional QFT with dynamical decoupling and other techniques to limit noise accumulation from entangling gates and to reduce routing overhead. • Reports a CX-gate count of n²−n＋2, achieving gate efficiency close to the ideal all-to-all connected case. Technical and business implications The QFT is a core subroutine used in phase estimation, factoring, and related algorithms; scaling its on-hardware execution faces challenges from noise accumulation and qubit connectivity constraints. Technically, the result shows that compiler optimizations tailored to hardware plus active error suppression can enable extraction of a target signal from 100-qubit-scale states even on pre-fault-tolerant processors. From a business perspective, the work demonstrates the potential to extract more performance from existing hardware via software. However, the article alone does not provide enough information to judge the process fidelity at 100 qubits, direct comparisons under identical conditions to alternative methods, or whether this leads to practical quantum advantage or near-term commercial applications. What to watch next Future decision factors include detailed experimental data not reported in the article, such as the process fidelity and number of measurements at 100 qubits. Important next steps are comparisons with existing methods under the same conditions, independent reproduction, and demonstrations that the approach works for other circuits and hardware. It will also be important to see demonstrations of full algorithms that use QFT (for example, phase estimation), and whether Q-CTRL’s technologies are integrated into customer projects. ✍️ Quantum Index Analysis The notable aspect of this result is less the nominal scale of 100 qubits and more that, even on a noisy device, the QFT’s correct frequency could be identified as the dominant peak in measurement outcomes. This is not a claim of reproducing QFT with high fidelity; rather, by combining compilation and error-suppression techniques, Q-CTRL maintained the practically necessary condition of being able to “read out the answer” from a 100-qubit-class state. Indeed, process fidelity falls to 11.4% at 50 qubits and to 1.8% at 80 qubits. Even so, the fact that the correct bitstring was the unique most frequent result shows that, even with low overall fidelity, the answer of interest can sometimes still be identified. Q-CTRL’s performance-management and error-suppression tools are already available as Qiskit Functions on the IBM Quantum Platform, and this demonstration also serves as a product showcase that their software can operate effectively on real circuits at the 100‑qubit scale. On the other hand, the article omits the process fidelity at 100 qubits, the absolute occurrence rate of the correct outcome, and the number of measurement shots. Next steps to assess practical value include direct comparisons with other compilation and error-suppression approaches under identical conditions, and demonstrations that full algorithms incorporating QFT can similarly yield extractable answers. Related articles • JIJ releases quantum programming language “Qamomile” v0.14.0 — supports resource estimation and conversion to multiple SDKs (https://en.quantumindex.jp/jij-qamomile-v0-14-0-quantum-programming-language/) • Ranking the capabilities and prospects of 29 quantum companies [Summer 2026] (https://en.quantumindex.jp/top-29-quantum-companies-ranking-summer-2026/) • IBM et al. demonstrate “quantum advantage” via verifiable quantum computation (https://en.quantumindex.jp/ibm-trusted-quantum-computation-advantage/) Source Read the original announcement (https://q-ctrl.com/blog/breaking-the-100-qubit-barrier-executing-the-quantum-fourier-transform-at-scale-on-ibm-hardware) If you found this article useful, please consider sharing it. ──────────────────── 【AI Evaluation Request】 Evaluate the following article critically, not based on the quality of its writing, but on its **original informational value as a specialist publication**. Do not give a high rating to summaries, paraphrases, explanations of technical terms, or generic observations such as "this will be worth watching" or "the timeline remains uncertain" if they could easily be produced by a general-purpose generative AI, even if they are well written. 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Q-CTRL executed the quantum Fourier transform (QFT) on up to 100 qubits on a 156-qubit IBM Heron r3 processor. By combining its proprietary compilation techniques with error suppression, the company reports that the correct target frequency emerged as the sole most frequently observed outcome across all test circuits.

Executive summary

In the experiments, periodic signals were encoded into registers of 50, 80, and 100 qubits, and the QFT was used to extract the target integer frequency. For all test circuits that included 100 qubits, the correct bitstring was the unique most frequent measured outcome. For 50 qubits the correct result appeared 8.4 times more often than the next-most-frequent incorrect result, and the unitary process fidelity was 11.4%. For 80 qubits those figures were 7.5 times and 1.8%, respectively.

Q-CTRL adopted a Convolutional QFT that uses a single ancilla qubit. By aggregating operations into small kernels that are swept across the register sequentially, the design reduces the entangling gates affecting each qubit. The company also combined dynamical decoupling, truncation of small rotations, precise timing adjustments of execution, and readout error mitigation.

According to Q-CTRL, the number of CX gates required for an n-qubit QFT on IBM Heron is n²−n＋2, which is close to the n²−n of an ideal all-to-all connected layout. By suppressing routing overhead due to connectivity constraints, they say they doubled register width compared with previous on-device QFT demonstrations.

Key points

• Used a 156-qubit IBM Heron r3 and executed QFT on up to 100 qubits.
• For all test circuits that included 100 qubits, the correct target frequency was the sole most frequent outcome.
• Process fidelity was 11.4% for 50 qubits and 1.8% for 80 qubits. The article does not report the process fidelity for 100 qubits.
• Combined Convolutional QFT with dynamical decoupling and other techniques to limit noise accumulation from entangling gates and to reduce routing overhead.
• Reports a CX-gate count of n²−n＋2, achieving gate efficiency close to the ideal all-to-all connected case.

Technical and business implications

The QFT is a core subroutine used in phase estimation, factoring, and related algorithms; scaling its on-hardware execution faces challenges from noise accumulation and qubit connectivity constraints. Technically, the result shows that compiler optimizations tailored to hardware plus active error suppression can enable extraction of a target signal from 100-qubit-scale states even on pre-fault-tolerant processors.

From a business perspective, the work demonstrates the potential to extract more performance from existing hardware via software. However, the article alone does not provide enough information to judge the process fidelity at 100 qubits, direct comparisons under identical conditions to alternative methods, or whether this leads to practical quantum advantage or near-term commercial applications.

What to watch next

Future decision factors include detailed experimental data not reported in the article, such as the process fidelity and number of measurements at 100 qubits. Important next steps are comparisons with existing methods under the same conditions, independent reproduction, and demonstrations that the approach works for other circuits and hardware. It will also be important to see demonstrations of full algorithms that use QFT (for example, phase estimation), and whether Q-CTRL’s technologies are integrated into customer projects.

✍️ Quantum Index Analysis

The notable aspect of this result is less the nominal scale of 100 qubits and more that, even on a noisy device, the QFT’s correct frequency could be identified as the dominant peak in measurement outcomes. This is not a claim of reproducing QFT with high fidelity; rather, by combining compilation and error-suppression techniques, Q-CTRL maintained the practically necessary condition of being able to “read out the answer” from a 100-qubit-class state.

Indeed, process fidelity falls to 11.4% at 50 qubits and to 1.8% at 80 qubits. Even so, the fact that the correct bitstring was the unique most frequent result shows that, even with low overall fidelity, the answer of interest can sometimes still be identified. Q-CTRL’s performance-management and error-suppression tools are already available as Qiskit Functions on the IBM Quantum Platform, and this demonstration also serves as a product showcase that their software can operate effectively on real circuits at the 100‑qubit scale.

On the other hand, the article omits the process fidelity at 100 qubits, the absolute occurrence rate of the correct outcome, and the number of measurement shots. Next steps to assess practical value include direct comparisons with other compilation and error-suppression approaches under identical conditions, and demonstrations that full algorithms incorporating QFT can similarly yield extractable answers.

Related articles

• JIJ releases quantum programming language “Qamomile” v0.14.0 — supports resource estimation and conversion to multiple SDKs
• Ranking the capabilities and prospects of 29 quantum companies [Summer 2026]
• IBM et al. demonstrate “quantum advantage” via verifiable quantum computation

Source

Read the original announcement