Iterative energy-scaled variational quantum eigensolver

A computer-implemented method, comprising:

• initializing, by a system operatively coupled to a processor, variational quantum parameters;
• and
• iteratively performing, by the system, in a sequential order, on respective block-diagonalized Hamiltonians of a group of block-diagonalized Hamiltonians, associated with a quantum Hamiltonian, formed using energy scale based symmetries:
• determining a variational wavefunction for a current block-diagonalized Hamiltonian in the sequential order based on the variational quantum parameters, and
• executing, via a quantum processor, a variational quantum eigensolver algorithm to optimize the variational quantum parameters for the variational wavefunction for the current block-diagonalized Hamiltonian;
• defining, by the system a final variational wavefunction for the quantum Hamiltonian using the variational quantum parameters optimized during a final iteration of the sequential order.

A computer program product for defining a final variational wavefunction for a quantum Hamiltonian, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• initialize variational quantum parameters;
• and
• iteratively perform, in a sequential order, on respective block-diagonalized Hamiltonians of a group of block-diagonalized Hamiltonians, associated with the quantum Hamiltonian, formed using energy scale based symmetries:
• determine a variational wavefunction for a current block-diagonalized Hamiltonian in the sequential order based on the variational quantum parameters, and
• execute, via a quantum processor, a variational quantum eigensolver algorithm to optimize the variational quantum parameters for the variational wavefunction for the current block-diagonalized Hamiltonian;
• define the final variational wavefunction for the quantum Hamiltonian using the variational quantum parameters optimized during a final iteration of the sequential order.