Mirror subspace diagonalization: A real-time quantum Krylov algorithm with near-optimal sampling cost
Mirror subspace diagonalization (MSD) is a new quantum Krylov algorithm designed to estimate ground-state energy with near-optimal sampling cost. It uses a finite-difference formula involving symmetrically shifted timesteps to efficiently estimate the Hamiltonian matrix. This method significantly reduces the required number of measurements compared to conventional methods, achieving reductions of 10 to 10,000 times for molecular simulations.