Quantum computing can play a significant role in green hydrogen production modeling by efficiently calculating quantum electronic energies in molecular systems, which is crucial for understanding reaction mechanisms and optimizing catalysts[1]. Quantum machine learning (QML) can simulate electron behavior with high precision, leading to more accurate predictions of electronic structures, bandgaps, and catalytic activities for photocatalytic water splitting[2].
Quantum simulations may optimize catalysts by accelerating calculations like Hartree-Fock and DFT, speeding up the discovery of materials with ideal light absorption and charge transport properties[2]. This can reduce green hydrogen costs, as demonstrated by a new quantum-backed technology that produces high-purity hydrogen without additional purification[6]. Realistic timelines suggest that a single calculation for catalytic systems with 52-65 orbitals could take a few weeks on mid-term quantum hardware, requiring about 4000 logical qubits[1].
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