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Range dependent Hamiltonian Algorithm for numerical QUBO formulation
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With the advent and development of quantum computers, various quantum algorithms that can solve linear equations and eigenvalues faster than classical computers have been developed. The Harrow-Hassidim-Lloyd algorithm is an algorithm that can solve linear equations in a gate model quantum computer. Still, it is constrained by the use of quantum RAM and the size limit of the matrix according to the total number of qubits in the quantum computer. Recently, Jun and Lee developed a QUBO model for solving linear systems and eigenvalue problems in the quantum computer. However, even though their model uses 2048 qubits, the number of qubits for variables that can be used for the problem is only 64. To solve this problem, we introduce an algorithm that can be used by dividing the size of the entire domain according to the number of qubits. We also form a QUBO model related to each subregion.
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QUBO Refinement: Achieving Superior Precision through Iterative Quantum Formulation with Limited Qubits
An iterative bit-slicing QUBO refinement method claims 16-decimal precision for linear systems but demonstrates only 1e-13 error and lacks a proven convergence guarantee.
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