Cleveland Clinic, RIKEN and IBM shortlisted for Gordon Bell Prize for quantum–HPC chemistry on 12,635‑atom protein

【Article Title】 Cleveland Clinic, RIKEN and IBM shortlisted for Gordon Bell Prize for quantum–HPC chemistry on 12,635‑atom protein 【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/cleveland-clinic-riken-ibm-gordon-bell-finalists-2026/) The research team from Cleveland Clinic, RIKEN (the RIKEN Center for Advanced Intelligence Project), and IBM has been named a finalist for the 2026 ACM Gordon Bell Prize for chemistry research that couples quantum processors with supercomputers. The work targeted a 12,635‑atom protein system, and the newly posted paper reports both end‑to‑end automation across quantum and classical computingclassical computingClassical Computing / Classical Computation / Classical Computing / Classical ComputationA computation method that uses bits of 0 and 1; the form of computation performed by the computers commonly used today.QI NoteUsed as a point of comparison with quantum computing, but it can encompass CPUs, GPUs, supercomputers, and specialized algorithms, so care should be taken about the conditions of comparison. resources and improvements in protein–ligand binding energy calculations. ✍️ Quantum Index Analysis The following explains the technical and commercial implications behind the announcement and highlights evaluation points that are not obvious from headlines and numbers alone. Read our independent analysis ↓ Summary The research team combined the IBM Quantum Heron processor with the supercomputers Fugaku, ROQUO, and the University of Tokyo’s Miyabi‑G to simulate a biologically relevant molecular system that is among the largest reported: a protein of 12,635 atoms. In an arXiv paper summarizing additional work, they present a fully automated end‑to‑end workflow that runs on RIKEN’s JHPC‑Quantum GPU supercomputer ROQUO. The workflow reduces manual adjustments and data movement previously required when spanning multiple computing resources and organizations, shortening overall time to result. They also report that protein–ligand binding energy calculations better reflected expected binding behavior on benchmark systems compared with previous approaches. Key points • The Cleveland Clinic, RIKEN and IBM research team was selected as a finalist for the 2026 ACM Gordon Bell Prize • They linked an IBM Quantum Heron processor with multiple supercomputers to simulate a 12,635‑atom protein system • The new work automates end‑to‑end processing across quantum and classical compute resources • Automation reduced manual coordination and data movement between organizations • They report improved accuracy in protein–ligand binding energy calculations Technical and commercial significance The significance of this result lies not only in scaling the size of the molecular target in quantum‑assisted simulations, but also in simultaneously advancing cross‑platform operational automation and calculation accuracy. Such workflows could lower operational overhead for handling larger numbers of proteins, experimental conditions, and more complex molecular systems. However, the announcement does not establish a timeline for commercialization, a business model, or practical quantum advantagequantum advantage量子優位性 / Quantum Advantage / Quantum Computational AdvantageFor a particular problem, a quantum computer demonstrates a practical advantage over classical computation in terms of speed, accuracy, cost, etc.QI NoteNot necessarily synonymous with "quantum supremacy"; the term is often used to include practical usefulness. When evaluating claims, check the classical methods used for comparison and the evaluation metrics. over classical computing for production use. What to watch next Going forward, key questions include whether the authors will provide quantitative comparisons showing how much the automation reduced wall‑clock time and human effort. For the binding energy accuracy improvements, it will be important to see whether the gains hold across a broader set of target molecules, comparisons with classical methods, and reproduction by independent groups. Additionally, whether the approach scales to more complex molecular systems and whether integrating a QPUQPUQuantum Processor / Quantum Processor / Quantum Processing Unit / QPUThe central part of the hardware that houses qubits and performs quantum computational operations such as quantum gates and measurements.QI NoteThe performance of a QPU cannot be judged by the number of qubits alone. Gate fidelity, connectivity, speed, error rates, and other factors must be considered together. yields practical advantages will be important evaluation axes. ✍️ Quantum Index Analysis The key point of this achievement is not the use of a quantum processorquantum processorQuantum Processor / Quantum Processor / Quantum Processing Unit / QPUThe central part of the hardware that houses qubits and performs quantum computational operations such as quantum gates and measurements.QI NoteThe performance of a QPU cannot be judged by the number of qubits alone. Gate fidelity, connectivity, speed, error rates, and other factors must be considered together. alone, but that heterogeneous computing—QPU, CPU, GPU and supercomputers—was run as a single computational system on a real, large‑scale chemistry problem. The Gordon Bell Prize focuses on HPC, and the evaluation here appears to emphasize scale‑up and system integration in computational science rather than standalone QPU performance. In simplified terms, the computational flow is as follows. • Divide the large protein system into fragments • Perform preprocessing, classical calculations, and job orchestration on the HPC side (e.g., ROQUO) • Send parts requiring quantum processing to a QPU such as IBM Quantum Heron and execute quantum circuits • Return the results to the HPC side for CPU/GPU postprocessing and reconstruction • ROQUO coordinates and executes these QPU↔HPC round trips as a unified workflow ROQUO itself is not a quantum computer but a GPU supercomputer built around NVIDIA Blackwell GPUs and Grace CPUs. It is composed of 135 nodes and houses 135 NVIDIA GB200 NVL4 devices, 540 Blackwell GPUs, and 270 Grace CPUs, using NVIDIA Quantum‑X800 InfiniBand for inter‑node communication. The name “Quantum‑X800” is not an indication of quantum computing capability. ROQUO’s role in quantum computing is not that it contains quantum hardware, but that it is positioned as the core compute platform of the JHPC‑quantum project to link quantum computers and HPC. RIKEN has provisioned ROQUO to interoperate with IBM Quantum System Two “ibm_kobe”, Quantinuum’s “Reimei” in Wako, and Fugaku, enabling an integrated pipeline: HPC preprocessing → QPU quantum circuitquantum circuit量子回路 / Quantum CircuitA representation of the computation procedure executed on a quantum computer, listing in order operations such as qubit initialization, quantum gate operations, and measurements.QI NoteEven for the same algorithm, the number of qubits used, circuit depth, and number of gates vary depending on the implementation. When comparing real-device performance, also check the circuit scale and the conditions after compilation. execution → GPU/HPC postprocessing. The connection layer uses an “SQC Interface” that leverages CUDA‑Q among other tools, and QURI SDK Enterprise has been deployed. This enables compute nodes on ROQUO and Fugaku to send work to quantum computers and process the returned results on the HPC side. The significance of the automation in this work is that it moves from researchers manually coordinating across multiple hardware platforms and sites to executing these steps as a single computational flow. Using this architecture, the team performed calculations on a protein system of 12,635 atoms. Therefore, this figure does not mean the QPU alone evaluated a system of that size. The result should be viewed as heterogeneous computing that assigns roles to both quantum and classical resources to scale up to a large scientific problem. Also, the “up to 210× accuracy improvement” does not demonstrate quantum advantagequantum advantage量子優位性 / Quantum Advantage / Quantum Computational AdvantageFor a particular problem, a quantum computer demonstrates a practical advantage over classical computation in terms of speed, accuracy, cost, etc.QI NoteNot necessarily synonymous with "quantum supremacy"; the term is often used to include practical usefulness. When evaluating claims, check the classical methods used for comparison and the evaluation metrics. over classical computingclassical computingClassical Computing / Classical Computation / Classical Computing / Classical ComputationA computation method that uses bits of 0 and 1; the form of computation performed by the computers commonly used today.QI NoteUsed as a point of comparison with quantum computing, but it can encompass CPUs, GPUs, supercomputers, and specialized algorithms, so care should be taken about the conditions of comparison.. The comparison is against a previous quantum–classical hybrid method; the result shows improved accuracy from a refined hybrid approach. Although further improvements in binding energy were reported in September, the announcement alone does not make clear which algorithmic or computational changes contributed and by how much. The next question is whether using this complex quantum–HPC configuration itself provides measurable computational value. A robust comparison against a highly optimized GPU/HPC‑only approach—matching accuracy, wall‑clock time, and required compute resources—will be needed to show concrete benefits from incorporating a QPU. Whether the workflow can be generalized beyond protein and chemistry calculations to other large‑scale scientific problems will also be an important evaluation axis. NETWORKSee the IBM Quantum industry network → (/qi-network/) Related articles • IBM expands quantum optimization benchmark QOBLIB, aggregating results from over 2,000 instances (https://en.quantumindex.jp/ibm-qoblib-quantum-optimization-benchmark/) • Quemix and Idemitsu propose a low‑cost MD approach for ionic transport under electric fields (https://en.quantumindex.jp/quemix-ofdft-mlmd-ionic-transport/) • QunaSys’ QURI SDK Enterprise adopted for RIKEN’s quantum–HPC integration platform (https://en.quantumindex.jp/qunasys-quri-sdk-enterprise-jhpc-quantum/) Sources Read the original announcement (https://www.ibm.com/quantum/blog/gordon-bell-finalists-2026) 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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The research team from Cleveland Clinic, RIKEN (the RIKEN Center for Advanced Intelligence Project), and IBM has been named a finalist for the 2026 ACM Gordon Bell Prize for chemistry research that couples quantum processors with supercomputers. The work targeted a 12,635‑atom protein system, and the newly posted paper reports both end‑to‑end automation across quantum and classical computingclassical computingClassical Computing / Classical Computation / Classical Computing / Classical ComputationA computation method that uses bits of 0 and 1; the form of computation performed by the computers commonly used today.QI NoteUsed as a point of comparison with quantum computing, but it can encompass CPUs, GPUs, supercomputers, and specialized algorithms, so care should be taken about the conditions of comparison. resources and improvements in protein–ligand binding energy calculations.

Summary

The research team combined the IBM Quantum Heron processor with the supercomputers Fugaku, ROQUO, and the University of Tokyo’s Miyabi‑G to simulate a biologically relevant molecular system that is among the largest reported: a protein of 12,635 atoms. In an arXiv paper summarizing additional work, they present a fully automated end‑to‑end workflow that runs on RIKEN’s JHPC‑Quantum GPU supercomputer ROQUO. The workflow reduces manual adjustments and data movement previously required when spanning multiple computing resources and organizations, shortening overall time to result. They also report that protein–ligand binding energy calculations better reflected expected binding behavior on benchmark systems compared with previous approaches.

Key points

• The Cleveland Clinic, RIKEN and IBM research team was selected as a finalist for the 2026 ACM Gordon Bell Prize
• They linked an IBM Quantum Heron processor with multiple supercomputers to simulate a 12,635‑atom protein system
• The new work automates end‑to‑end processing across quantum and classical compute resources
• Automation reduced manual coordination and data movement between organizations
• They report improved accuracy in protein–ligand binding energy calculations

Technical and commercial significance

The significance of this result lies not only in scaling the size of the molecular target in quantum‑assisted simulations, but also in simultaneously advancing cross‑platform operational automation and calculation accuracy. Such workflows could lower operational overhead for handling larger numbers of proteins, experimental conditions, and more complex molecular systems. However, the announcement does not establish a timeline for commercialization, a business model, or practical quantum advantagequantum advantage量子優位性 / Quantum Advantage / Quantum Computational AdvantageFor a particular problem, a quantum computer demonstrates a practical advantage over classical computation in terms of speed, accuracy, cost, etc.QI NoteNot necessarily synonymous with "quantum supremacy"; the term is often used to include practical usefulness. When evaluating claims, check the classical methods used for comparison and the evaluation metrics. over classical computing for production use.

What to watch next

Going forward, key questions include whether the authors will provide quantitative comparisons showing how much the automation reduced wall‑clock time and human effort. For the binding energy accuracy improvements, it will be important to see whether the gains hold across a broader set of target molecules, comparisons with classical methods, and reproduction by independent groups. Additionally, whether the approach scales to more complex molecular systems and whether integrating a QPUQPUQuantum Processor / Quantum Processor / Quantum Processing Unit / QPUThe central part of the hardware that houses qubits and performs quantum computational operations such as quantum gates and measurements.QI NoteThe performance of a QPU cannot be judged by the number of qubits alone. Gate fidelity, connectivity, speed, error rates, and other factors must be considered together. yields practical advantages will be important evaluation axes.

✍️ Quantum Index Analysis

The key point of this achievement is not the use of a quantum processorquantum processorQuantum Processor / Quantum Processor / Quantum Processing Unit / QPUThe central part of the hardware that houses qubits and performs quantum computational operations such as quantum gates and measurements.QI NoteThe performance of a QPU cannot be judged by the number of qubits alone. Gate fidelity, connectivity, speed, error rates, and other factors must be considered together. alone, but that heterogeneous computing—QPU, CPU, GPU and supercomputers—was run as a single computational system on a real, large‑scale chemistry problem. The Gordon Bell Prize focuses on HPC, and the evaluation here appears to emphasize scale‑up and system integration in computational science rather than standalone QPU performance.

In simplified terms, the computational flow is as follows.

• Divide the large protein system into fragments
• Perform preprocessing, classical calculations, and job orchestration on the HPC side (e.g., ROQUO)
• Send parts requiring quantum processing to a QPU such as IBM Quantum Heron and execute quantum circuits
• Return the results to the HPC side for CPU/GPU postprocessing and reconstruction
• ROQUO coordinates and executes these QPU↔HPC round trips as a unified workflow

ROQUO itself is not a quantum computer but a GPU supercomputer built around NVIDIA Blackwell GPUs and Grace CPUs. It is composed of 135 nodes and houses 135 NVIDIA GB200 NVL4 devices, 540 Blackwell GPUs, and 270 Grace CPUs, using NVIDIA Quantum‑X800 InfiniBand for inter‑node communication. The name “Quantum‑X800” is not an indication of quantum computing capability.

ROQUO’s role in quantum computing is not that it contains quantum hardware, but that it is positioned as the core compute platform of the JHPC‑quantum project to link quantum computers and HPC. RIKEN has provisioned ROQUO to interoperate with IBM Quantum System Two “ibm_kobe”, Quantinuum’s “Reimei” in Wako, and Fugaku, enabling an integrated pipeline: HPC preprocessing → QPU quantum circuitquantum circuit量子回路 / Quantum CircuitA representation of the computation procedure executed on a quantum computer, listing in order operations such as qubit initialization, quantum gate operations, and measurements.QI NoteEven for the same algorithm, the number of qubits used, circuit depth, and number of gates vary depending on the implementation. When comparing real-device performance, also check the circuit scale and the conditions after compilation. execution → GPU/HPC postprocessing.

The connection layer uses an “SQC Interface” that leverages CUDA‑Q among other tools, and QURI SDK Enterprise has been deployed. This enables compute nodes on ROQUO and Fugaku to send work to quantum computers and process the returned results on the HPC side. The significance of the automation in this work is that it moves from researchers manually coordinating across multiple hardware platforms and sites to executing these steps as a single computational flow.

Using this architecture, the team performed calculations on a protein system of 12,635 atoms. Therefore, this figure does not mean the QPU alone evaluated a system of that size. The result should be viewed as heterogeneous computing that assigns roles to both quantum and classical resources to scale up to a large scientific problem.

Also, the “up to 210× accuracy improvement” does not demonstrate quantum advantagequantum advantage量子優位性 / Quantum Advantage / Quantum Computational AdvantageFor a particular problem, a quantum computer demonstrates a practical advantage over classical computation in terms of speed, accuracy, cost, etc.QI NoteNot necessarily synonymous with "quantum supremacy"; the term is often used to include practical usefulness. When evaluating claims, check the classical methods used for comparison and the evaluation metrics. over classical computingclassical computingClassical Computing / Classical Computation / Classical Computing / Classical ComputationA computation method that uses bits of 0 and 1; the form of computation performed by the computers commonly used today.QI NoteUsed as a point of comparison with quantum computing, but it can encompass CPUs, GPUs, supercomputers, and specialized algorithms, so care should be taken about the conditions of comparison.. The comparison is against a previous quantum–classical hybrid method; the result shows improved accuracy from a refined hybrid approach. Although further improvements in binding energy were reported in September, the announcement alone does not make clear which algorithmic or computational changes contributed and by how much.

The next question is whether using this complex quantum–HPC configuration itself provides measurable computational value. A robust comparison against a highly optimized GPU/HPC‑only approach—matching accuracy, wall‑clock time, and required compute resources—will be needed to show concrete benefits from incorporating a QPU. Whether the workflow can be generalized beyond protein and chemistry calculations to other large‑scale scientific problems will also be an important evaluation axis.

NETWORKSee the IBM Quantum industry network →

Related articles

• IBM expands quantum optimization benchmark QOBLIB, aggregating results from over 2,000 instances
• Quemix and Idemitsu propose a low‑cost MD approach for ionic transport under electric fields
• QunaSys’ QURI SDK Enterprise adopted for RIKEN’s quantum–HPC integration platform

Sources

Read the original announcement