Tong Jiang

Hybrid quantum-classical Monte Carlo had been limited by the large classical cost of repeatedly processing quantum measurement data, with hardware demonstrations reaching only 16 qubits. Compressing those measurements into a differentiable machine-learning surrogate that supplies the wavefunction overlaps and derivatives Monte Carlo needs accelerates the end-to-end calculation by orders of magnitude on Google's Willow processor, bringing hydrogen chains and iron-sulfur clusters to chemical accuracy on up to 40 qubits, more than doubling the scale of earlier hardware demonstrations.

Hybrid quantum-classical Monte Carlo had been limited by the large classical cost of repeatedly processing quantum measurement data, with hardware demonstrations reaching only 16 qubits. Compressing those measurements into a differentiable machine-learning surrogate that supplies the wavefunction overlaps and derivatives Monte Carlo needs accelerates the end-to-end calculation by orders of magnitude on Google's Willow processor, bringing hydrogen chains and iron-sulfur clusters to chemical accuracy on up to 40 qubits, more than doubling the scale of earlier hardware demonstrations.