Variatsioonikvantumalgravitsemistele

Variatsioonikvantumalgravitsemistele, commonly referred to as variational quantum eigensolvers (VQEs), is a hybrid quantum-classical algorithm designed to find the lowest eigenvalue, typically the ground state energy, of a given Hamiltonian. It is considered a leading candidate for near-term quantum computers due to its potential to be implemented with noisy intermediate-scale quantum (NISQ) devices.

The core idea behind VQEs is to leverage both quantum and classical computational resources. A quantum computer is used to prepare a parameterized quantum state, often called a "ansatz." This ansatz is designed to approximate the true ground state of the Hamiltonian. The expectation value of the Hamiltonian with respect to this ansatz is then measured on the quantum computer.

A classical optimizer then takes this measured expectation value and adjusts the parameters of the ansatz to minimize this value. This process is iterative: the quantum computer prepares a new ansatz state with updated parameters, the expectation value is measured again, and the classical optimizer further refines the parameters. This cycle continues until a convergence criterion is met, theoretically yielding the ground state energy.

The effectiveness of a VQE algorithm heavily relies on the choice of the ansatz and the classical optimization strategy. Various ansatzes, such as the hardware-efficient ansatz or the Unitary Coupled Cluster (UCC) ansatz, have been proposed and studied. Similarly, different classical optimization algorithms, like the SPSA (Simultaneous Perturbation Stochastic Approximation) or COBYLA (Constrained Optimization BY Linear Approximation), can be employed. VQEs have applications in quantum chemistry for molecular energy calculations, materials science, and optimization problems.