Variational quantum algorithms to estimate rank, quantum entropies, fidelity and Fisher information via purity minimization

• URL: http://arxiv.org/abs/2103.15956v2
• Date: Thu, 16 Sep 2021 14:07:42 GMT
• Title: Variational quantum algorithms to estimate rank, quantum entropies, fidelity and Fisher information via purity minimization
• Authors: Kok Chuan Tan, Tyler Volkoff
• Abstract summary: Variational quantum algorithms (VQAs) that estimate values of widely used physical quantities are developed. variations of these VQAs are also adapted to perform other useful functions such as quantum state learning and approximate inverse fractionals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational quantum algorithms (VQAs) that estimate values of widely used physical quantities such as the rank, quantum entropies, the Bures fidelity and the quantum Fisher information of mixed quantum states are developed. In addition, variations of these VQAs are also adapted to perform other useful functions such as quantum state learning and approximate fractional inverses. The common theme shared by the proposed algorithms is that their cost functions are all based on minimizing the quantum purity of a quantum state. Strategies to mitigate or avoid the problem of exponentially vanishing cost function gradients are also discussed.

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