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QUBO formulations for numerical quantum computing
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With the advent of quantum computers, many quantum computing algorithms are being developed. Solving linear systems is one of the most fundamental problems in almost all science and engineering. The Harrow-Hassidim-Lloyd algorithm, a monumental quantum algorithm for solving linear systems on gate model quantum computers, was invented and several advanced variations have been developed. For a given n by n matrix A and a vector b, we will find unconstrained binary optimization (QUBO) models for a vector x that satisfies Ax=b. To formulate QUBO models for a linear system solving problem, we make use of a linear least-square problem with binary representation of the solution. We validate those QUBO models on the D-Wave system and discuss the results. For a simple system, we provide a Python code to calculate the matrix characterizing the relationship between the variables and to print the test code that can be used directly in the D-Wave system.
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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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