Quantum Simulation & Scientific Applications

Richard Feynman's original 1982 insight that sparked quantum computing: 'Nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical.' Simulating quantum systems on classical computers requires exponential resources. A molecule with 50 electrons requires 2⁵⁰ complex numbers to describe its state exactly — beyond any supercomputer. A quantum computer naturally represents quantum states. Simulating a 50-electron molecule needs only 50 qubits — linear, not exponential. This is the killer app that no amount of classical computing power can match. IMPACT AREAS: Drug discovery (simulating protein folding and drug-protein binding), materials science (new battery electrolytes, solar cells, superconductors), fertiliser production (modelling nitrogen fixation could reduce the 2% of global energy used by the Haber-Bosch process).

VQE (VARIATIONAL QUANTUM EIGENSOLVER): The leading near-term quantum chemistry algorithm. Uses a hybrid quantum-classical loop to find molecular ground states. Already running on current hardware for small molecules. REAL RESULTS TODAY: • Simulated hydrogen molecule (H₂) ground state accurately • Simulated lithium hydride (LiH) — relevant to battery chemistry • Simulated small organic molecules WHAT'S NEEDED FOR IMPACT: Simulating the FeMo cofactor in nitrogenase (the enzyme that naturally fixes nitrogen) needs ~300 logical qubits — roughly 300,000 physical qubits with error correction. Still years away. NEAREST PRACTICAL WIN: Quantum chemistry for drug screening at 50-100 logical qubits, achievable in the early 2030s.