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Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes

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⚡ Quantum Brief
Researchers achieved a breakthrough in quantum chemistry by simulating protein-ligand complexes with over 12,000 atoms, surpassing previous size limits by 40× using heterogeneous quantum-classical (HQC) methods. The team decomposed molecules into fragments, running 9,200 quantum circuits on IBM’s 156-qubit processors (Cleveland and Kobe) for over 100 hours, collecting 1.3 billion measurement outcomes—the largest HQC chemistry computation to date. Classical supercomputers (Fugaku and Miyabi-G) computed fragment wavefunctions with 72.5% parallel efficiency, leveraging scalable linear algebra kernels to bridge quantum and classical workflows. Accuracy improved up to 210× over prior methods, with HQC matching coupled-cluster (CCSD) precision in fragment energies, validating the approach for dispersion- and electrostatics-dominated biomolecular systems. This work establishes a scalable, systematically improvable pathway for ab initio biomolecular simulations, demonstrating real-world utility for quantum-enhanced drug discovery and materials science.
Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes

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“Ab initio wavefunction methods provide accurate molecular simulations but their computational scaling restricts applications to small systems. We develop a workflow combining quantum embedding to decompose a molecule into fragments with a heterogeneous quantum-classical (HQC) method to simulate fragments. We sample fragment electronic configurations on two 156-qubit quantum processors (ibm _ cleveland, ibm _ kobe), using up to 94 qubits, running 9,200 circuits for over 100 hours, collecting 1.3⋅ 10 9 measurement outcomes - the most resource-intensive HQC computation for quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on two supercomputers (Fugaku, Miyabi-G), achieving 72.5 % parallel efficiency with scalable distributed linear algebra kernels. We simulate two protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608 and 12,635 atoms), demonstrate >40× increase in system size and up to 210× improvement in accuracy over the previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies, and establish a scalable pathway for systematically improvable biomolecular simulations.” submitted by /u/Earachelefteye [link] [comments]

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drug-discovery
quantum-chemistry
quantum-investment
quantum-geopolitics
quantum-hardware

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Source: Reddit r/QuantumComputing (RSS)