What is ansatz in quantum computing?
In the context of variational circuits, an ansatz usually describes a subroutine consisting of a sequence of gates applied to specific wires. Many variational circuit ansaetze [1] have been proposed by the quantum computing community.
Why VQE?
VQE can help us to estimate* the energy of the ground state of a given quantum mechanical system**. To be mathematically exact: upper/lower bounds are defined for sets, not single values.
What is the VQE?
The VQE is an algorithm running partly on a classical computer and partly on a quantum computer. It lets you find the combination of values that solve a given optimization problem — approximately. Solving optimization problems is paramount in machine learning. Machine learning algorithms depend on a set of parameters.
What are the inputs to The VQE algorithm?
The inputs to the VQE algorithm are a molecular Hamiltonian and a parametrized circuit preparing the quantum state of the molecule. Within VQE, the cost function is defined as the expectation value of the Hamiltonian computed in the trial state.
How does the quantum subroutine of VQE work?
Practically, the quantum subroutine of VQE amounts to preparing a state based off of a set of parameters vec ( θ) and performing a series of measurements in the appropriate basis. The paramaterized state (or ansatz) preparation can be tricky in these algorithms and can dramatically affect performance.
How is VQE an application of the Ritz variational principle?
It is an application of the Ritz variational principle, where a quantum computer is trained to prepare the ground state of a given molecule. The inputs to the VQE algorithm are a molecular Hamiltonian and a parametrized circuit preparing the quantum state of the molecule.
When to use variational quantum Eigensolver ( VQE )?
The Variational-Quantum-Eigensolver (VQE) [1, 2] is a quantum/classical hybrid algorithm that can be used to find eigenvalues of a (often large) matrix (H).