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Machine learning techniques have so far proved to be very promising for the analysis of data in several fields, with many potential applications. However, researchers have found that applying these methods to quantum physics problems is far more challenging due to the exponential complexity of many-body systems.

Quantum many-body systems are essentially microscopic structures made up of several interacting particles. While studies have focused on the collective behavior of these systems, using machine learning in these investigations has proven to be very difficult.

With this in mind, a team of researchers at Harvard University recently developed a quantum circuit-based algorithm inspired by (CNNs), a popular machine learning technique that has achieved remarkable results in a variety of fields. In their paper, published in Nature Physics, the researchers outlined this new architecture and evaluated its accuracy in recognizing quantum states associated with a 1-D, symmetry-protected topological phase.

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Category: Science