Description: VQE, or Variational Quantum Eigensolver, is an algorithm designed to solve optimization problems in quantum systems. Its main goal is to find the eigenvalues and eigenvectors of a Hamiltonian, which describes the energy of a quantum system. This approach is particularly relevant in quantum computing, where quantum systems can be complex and difficult to simulate with classical computers. VQE combines classical optimization techniques with quantum circuits, allowing calculations to be performed on available quantum hardware. Through an iterative process, the algorithm adjusts the parameters of a quantum circuit to minimize the expected energy of the system, which in turn allows for approximating the ground state of the quantum system. This methodology is especially useful in simulating various quantum systems, where understanding their quantum properties is sought. The versatility of VQE makes it a key tool in the research and development of quantum computing, opening new possibilities in fields such as quantum chemistry and materials science.
History: VQE was first proposed in 2014 by a group of researchers led by John Preskill and has since developed as one of the most promising techniques in quantum computing. Its introduction marked a significant advancement in the ability to simulate complex quantum systems using quantum computers, especially in the context of quantum chemistry.
Uses: VQE is primarily used in simulating quantum systems, such as molecules and materials, to calculate their energetic and structural properties. It is also applied in optimizing problems in quantum chemistry and in developing new materials with specific properties.
Examples: A practical example of VQE usage is the simulation of the hydrogen molecule, where the goal is to calculate its ground state energy. Another case is research into new superconducting materials, where VQE helps understand the quantum interactions affecting their properties.
