A qutrit represents a three-level quantum system, so that one qutrit can encode more information than a qubit, which corresponds to a two-level quantum system. This work investigates the potential of qutrit circuits in machine learning classification applications. We propose and evaluate different data-encoding schemes for qutrits, and find that the classification accuracy varies significantly depending on the used encoding. We therefore propose a training method for encoding optimization that allows to consistently achieve high classification accuracy, and show that it can also improve the performance within a data re-uploading approach. Our theoretical analysis and numerical simulations indicate that the qutrit classifier can achieve high classification accuracy using fewer components than a comparable qubit system. We showcase the qutrit classification using the encoding optimization method on a superconducting transmon qutrit, demonstrating the practicality of the proposed method on noisy hardware. Our work demonstrates high-precision ternary classification using fewer circuit elements, establishing qutrit quantum circuits as a viable and efficient tool for quantum machine learning applications.

中文翻译：

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在超导 transmon qutrit 上演示的量子机器学习编码优化


一个qutrit代表一个三能级量子系统，因此一个qutrit可以比一个量子位编码更多的信息，相当于一个二能级量子系统。这项工作研究了 qutrit 电路在机器学习分类应用中的潜力。我们提出并评估了 qutrit 的不同数据编码方案，发现分类精度根据所使用的编码而有很大差异。因此，我们提出了一种用于编码优化的训练方法，该方法可以持续实现高分类精度，并表明它还可以提高数据重新上传方法的性能。我们的理论分析和数值模拟表明，与同类量子位系统相比，qutrit 分类器可以使用更少的组件实现高分类精度。我们展示了在超导 transmon qutrit 上使用编码优化方法进行 qutrit 分类，证明了所提出的方法在噪声硬件上的实用性。我们的工作展示了使用更少电路元件的高精度三元分类，将 qutrit 量子电路建立为量子机器学习应用的可行且高效的工具。