Quantum Compressive Sensing Meets Quantum Noise: A Practical Exploration

• URL: http://arxiv.org/abs/2501.12335v1
• Date: Tue, 21 Jan 2025 18:10:03 GMT
• Title: Quantum Compressive Sensing Meets Quantum Noise: A Practical Exploration
• Authors: Naveed Naimipour, Collin Frink, Harry Shaw, Haleh Safavi, Mojtaba Soltanalian,
• Abstract summary: We present a practical implementation of Quantum Compressive Sensing (QCS) on Amazon Braket. QCS is a quantum data-driven approach to compressive sensing where the state of the tensor network is represented by a quantum state over a set of entangled qubits. We discuss potential long-term directions aimed at unlocking the full potential of quantum compressive sensing for applications such as signal recovery and image processing.
• Score: 8.260432715157027
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• Abstract: Compressive sensing is a signal processing technique that enables the reconstruction of sparse signals from a limited number of measurements, leveraging the signal's inherent sparsity to facilitate efficient recovery. Recent works on the Quantum Compressive Sensing (QCS) architecture, a quantum data-driven approach to compressive sensing where the state of the tensor network is represented by a quantum state over a set of entangled qubits, have shown promise in advancing quantum data-driven methods for compressive sensing. However, the QCS framework has remained largely untested on quantum computing resources or in the presence of quantum noise. In this work, we present a practical implementation of QCS on Amazon Braket, utilizing the Quantum Imaginary Time Evolution (QITE) projection technique to assess the framework's capabilities under quantum noise. We outline the necessary modifications to the QCS framework for deployment on Amazon Braket, followed by results under four types of quantum noise. Finally, we discuss potential long-term directions aimed at unlocking the full potential of quantum compressive sensing for applications such as signal recovery and image processing.

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