Studying the Integration Between Error Correction and Quantum Machine Learning at Google Quantum Al

As we continue advancing to the transition from classical to quantum computing, we face a significant challenge: quantum error. Quantum errors come from quantum gate imperfections and decoherence. Many quantum machine learning algorithms require multiple executions to attain a proper estimation due to the variability introduced by error. The Variational Quantum Eigensolver optimizes ansatz parameters to approximate the ground state of a Hamiltonian but error misclassifies qubit readouts, distorting Pauli operator estimates. This can cause the optimizer to follow an incorrect descent path, leading to unstable convergence. This year, I've focused on learning quantum error correction and quantum machine learning and am now connecting them by testing the effects of different error correction codes on quantum machine learning, specifically the Variational Quantum Eigensolver, using a Quokka quantum simulator.