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Mitsui & Co. and Mitsubishi Electric Benchmark Approximate and Logical QFT on Quantinuum Heliosquantum-computing

Mitsui & Co. and Mitsubishi Electric Benchmark Approximate and Logical QFT on Quantinuum Helios

Mitsui & Co. and Mitsubishi Electric Benchmark Approximate and Logical QFT on Quantinuum Helios Japanese industrial conglomerates Mitsui & Co. and Mitsubishi Electric have published joint experimental benchmarks evaluating the Quantum Fourier Transform (QFT) on Quantinuum’s 98-qubit Helios trapped-ion quantum computer. Detailed in a co-authored white paper (“Experimental Evaluation of the Quantum Fourier Transform on a Trapped-Ion Quantum Computer“), the team executed both physical-qubit approximate QFT and Steane-encoded logical QFT circuits. The experiment evaluates hardware scaling and algorithmic utility across two computational regimes: Physical Qubit Approximate QFT (Up to 98 Qubits): The researchers executed approximate QFT circuits up to the processor’s full 98 physical qubit capacity. By setting the small-angle phase rotation truncation parameter (degs=5), the system preserved a non-zero target-state probability (Ptarget​=0.143 at 98 qubits) while applying leakage-detection (LD) post-selection to mitigate environmental physical errors. Logical QFT with Steane Code (Up to 12 Logical Qubits): Using the 7-qubit Steane error-correcting code ([[7,1,3]]), the team instantiated up to 12 logical qubits across 84 physical qubits. Evaluating error-detection post-selection against active error correction, the team observed that error detection achieved higher logical target-state probabilities (PL​target = 0.934 for 4 logical qubits, 0.774 for 8 logical qubits) at the cost of reduced shot acceptance rates (31% at 8 logical qubits, 8% at 12 logical qubits). Logical T-Gate Implementation Trade-Offs: In a two-logical-qubit QFT test, the team compared non-fault-tolerant direct analog rotations with fault-tolerant code-switching state injection (using 30 physical qubits across quantum Reed-Muller and Steane blocks). Direct analog rotation yielded higher output fidelity under current physical noise levels, highlighting the operational overheads associated with full

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Quantinuum, NVIDIA, and Pfizer Validate Generative Quantum AI (GenQAI) Framework for Pharmaceutical R&Dquantum-computing

Quantinuum, NVIDIA, and Pfizer Validate Generative Quantum AI (GenQAI) Framework for Pharmaceutical R&D

Quantinuum, NVIDIA, and Pfizer Validate Generative Quantum AI (GenQAI) Framework for Pharmaceutical R&D Researchers from Quantinuum, NVIDIA, and Pfizer Inc. have validated a Generative Quantum AI (GenQAI) framework designed to automate and accelerate quantum circuit synthesis for pharmaceutical research and electronic structure modeling. In their paper, “Learning to Prepare Molecular Ground States with Transformer Models“, the hybrid architecture combines classical High-Performance Computing (HPC), generative transformer models, and quantum processing units (QPUs) to compute ground-state preparation circuits for complex active pharmaceutical ingredients (APIs). The multi-institutional team introduced ADAPT-GQE, a generative AI model trained on quantum chemistry datasets generated via GPU-accelerated classical supercomputing. The model predicts complete ground-state quantum circuits for imipramine—a tricyclic antidepressant used as an industry benchmark for forced degradation and shelf-life stability studies—executing the resulting circuits on Quantinuum’s 98-qubit Helios-1 trapped-ion hardware. [ GenQAI / ADAPT-GQE Quantum Circuit Synthesis Pipeline ] │ ┌────────────────────────────────────────┼────────────────────────────────────────┐ ▼ ▼ ▼ HPC Data Generation (NVIDIA CUDA-Q) Generative AI Circuit Synthesis QPU Execution & Validation • GPU-Accelerated ADAPT-VQE Circuits. • Fine-Tuned NVIDIA Nemotron Models. • Quantinuum Helios-1 Processor. • OpenMM & MACE-OFF MD Conformers. • Gemma 3 / Nemotron-Nano Transformer. • InQuanto Chemistry Platform. • 12 to 16 Active-Space Qubit Maps. • 3-4 Orders of Magnitude Speedup. • Validated Imipramine Ground State. The experiment resolves a fundamental computational bottleneck in near-term variational quantum algorithms (VQEs): Bypassing Iterative Gradient Calculations: Standard adaptive algorithms like ADAPT-VQE require evaluating thousands of operator gradients and re-optimizing parameter landscapes at every step, ren

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