We propose an adaptive random quantum algorithm to obtain an optimized eigensolver. The changes in the involved matrices follow bio-inspired evolutionary mutations which are based on two figures of merit: learning speed and learning accuracy. This method provides high fidelities for the searched eigenvectors and faster convergence on the way to quantum advantage with current noisy intermediate-scaled quantum (NISQ) computers.
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