Quantinuum’s Backpropagation Algorithm Rivals Sparse Pauli Simulation Speed

Quantinuum researchers have developed a new backpropagation algorithm that significantly reduces the memory demands of optimizing quantum circuits. The method achieves a reduction in memory cost by a factor of n compared with conventional reverse-mode automatic differentiation, where n denotes the number of parameters in the circuit. This advance maintains gradient accuracy comparable to the corresponding observable expectation values while matching the computational complexity of sparse Pauli simulation. Demonstrating the algorithm’s scalability, the team successfully optimized circuits for transverse-field Ising models in one, two, and three dimensions, as well as the three-dimensional Heisenberg model, and compressed two-dimensional time-evolution circuits; this level of complexity exceeds…