Discriminating Quantum States with Quantum Machine Learning

• URL: http://arxiv.org/abs/2112.00313v1
• Date: Wed, 1 Dec 2021 07:09:14 GMT
• Title: Discriminating Quantum States with Quantum Machine Learning
• Authors: David Quiroga, Prasanna Date, Raphael C. Pooser
• Abstract summary: We propose, implement and analyze a quantum k-means (qk-means) algorithm with a low time complexity. Discriminating quantum states allows the identification of quantum states from low-level in-phase and quadrature signal (IQ) data. In order to reduce dependency on a classical computer, we use the qk-means to perform state discrimination on the IBMQ Bogota device.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum machine learning (QML) algorithms have obtained great relevance in the machine learning (ML) field due to the promise of quantum speedups when performing basic linear algebra subroutines (BLAS), a fundamental element in most ML algorithms. By making use of BLAS operations, we propose, implement and analyze a quantum k-means (qk-means) algorithm with a low time complexity of $\mathcal{O}(NKlog(D)I/C)$ to apply it to the fundamental problem of discriminating quantum states at readout. Discriminating quantum states allows the identification of quantum states $|0\rangle$ and $|1\rangle$ from low-level in-phase and quadrature signal (IQ) data, and can be done using custom ML models. In order to reduce dependency on a classical computer, we use the qk-means to perform state discrimination on the IBMQ Bogota device and managed to find assignment fidelities of up to 98.7% that were only marginally lower than that of the k-means algorithm. Inspection of assignment fidelity scores resulting from applying both algorithms to a combination of quantum states showed concordance to our correlation analysis using Pearson Correlation coefficients, where evidence shows cross-talk in the (1, 2) and (2, 3) neighboring qubit couples for the analyzed device.

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