Meta-Variational Quantum Eigensolver: Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation
Alba Cervera-Lierta, Jakob S. Kottmann, Alán Aspuru-Guzik
Abstract
An algorithm that uses quantum machine learning methods to estimate the ground state energy of any parametrization of the Hamiltonian within a certain trusted region is developed, reducing the total computational cost of these variational quantum algorithms.
Topics & Concepts
Parameterized complexityQuantumHamiltonian (control theory)Ground statePhysicsAdiabatic quantum computationParametrization (atmospheric modeling)Quantum algorithmQuantum mechanicsQuantum systemEnergy (signal processing)Quantum computerQuantum simulatorQuantum operationState (computer science)Statistical physicsQuantum stateQuantum machine learningOpen quantum systemMathematicsVariational methodQuantum annealingComputer scienceQuantum dynamicsQuantum phase estimation algorithmQuantum processLearning with errorsComputational complexity theoryPotential energyHamiltonian systemQuantum error correctionQuantum Computing Algorithms and ArchitectureQuantum many-body systemsMachine Learning in Materials Science