arXiv:2606.20504v1 Announce Type: cross Abstract: We present a systematic study of von Neumann entropy estimation in multi-qutrit quantum systems using two complementary approaches: variational quantum algorithms (VQAs) and classical convolutional neural networks (CNNs), evaluated using an ideal (noise-free) quantum simulator. For systems up to three qutrits, we construct and evaluate 11 hardware-efficient SU(3)-inspired ansatzes. A parameter sweep shows that estimation accuracy is primarily determined by the number of trainable parameters, provided sufficient entanglement is present. Based on
The continuous advancements in quantum computing research necessitate enhanced methods for characterizing complex quantum states, which is crucial for building reliable quantum systems.
Accurate entropy estimation is fundamental for understanding entanglement and assessing the quality of multi-qutrit quantum systems, directly impacting the development of robust quantum computers.
This research introduces and evaluates specific methods (VQAs and CNNs) for efficiently estimating von Neumann entropy in multi-qutrit systems, potentially accelerating quantum algorithm development and error mitigation.
- · Quantum computing researchers
- · Quantum hardware developers
- · AI/ML in scientific computing
- · Traditional quantum state characterization methods
- · Hardware platforms with limited entanglement control
Improved understanding and control over high-dimensional quantum systems, specifically those using qutrits.
Faster development and deployment of more complex and stable quantum algorithms leveraging qutrit advantages.
Potential for quantum computers to tackle more intricate computational problems, surpassing classical limits, including advanced AI applications.
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