Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz
Antonio Anna Mele, Glen Bigan Mbeng, Giuseppe E. Santoro, Mario Collura, Pietro Torta
Abstract
Parametrized quantum circuits inspired by adiabatic quantum computation often suffer from vanishing gradients, hindering the trainability of hybrid quantum-classical algorithms. Here, the authors put forward a strategy to overcome this by reusing parameters and iterating from small to large systems.
Topics & Concepts
AnsatzComputationAdiabatic processHamiltonian (control theory)QuantumQuantum computerTransferabilityStatistical physicsComputer scienceMathematicsApplied mathematicsTheoretical physicsPhysicsMathematical optimizationAlgorithmMathematical physicsQuantum mechanicsMachine learningLogitQuantum Computing Algorithms and ArchitectureQuantum many-body systemsQuantum Information and Cryptography