Abstract
Gate-model quantum computers can allow quantum computations in near-term implementations. The stabilization of an optimal quantum state of a quantum computer is a challenge, since it requires stable quantum evolutions via a precise calibration of the unitaries. Here, we propose a method for the stabilization of an optimal quantum state of a quantum computer through an arbitrary number of running sequences. The optimal state of the quantum computer is set to maximize an objective function of an arbitrary problem fed into the quantum computer. We also propose a procedure to classify the stabilized quantum states of the quantum computer into stability classes. The results are convenient for gate-model quantum computations and near-term quantum computers.
Highlights
Quantum computers can make possible quantum computations for efficient problem solving [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]
The novel contributions of our manuscript are as follows: 1. We propose a method for the stabilization of an optimal quantum state of a quantum computer through an arbitrary number of running sequences
We defined a method for the learning of stable quantum evolutions in gate-model quantum computer architectures
Summary
Quantum computers can make possible quantum computations for efficient problem solving [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. The problem is to fix the quantum state of the quantum computer in the optimal state for an arbitrary number of running sequences that is determined by the actual environment or by the current problem. We define a solution that utilizes unsupervised learning algorithms to determine the stable quantum states of the quantum computer and to classify the stable quantum states into stability classes. The proposed results are useful for experimental gate-based quantum computations and near-term quantum computer architectures. We propose a method for the stabilization of an optimal quantum state of a quantum computer through an arbitrary number of running sequences.
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