Tomography of Quantum States From Structured Measurements via Quantum-Aware Transformer

Area of Science:

• Quantum Information Science
• Quantum Computing
• Machine Learning

Background:

• Quantum state tomography (QST) reconstructs quantum states using measurements.
• Previous QST methods often overlook the inherent structure of quantum measurements.
• Bridging natural language processing and quantum measurement structures offers new avenues for QST.

Purpose of the Study:

• To develop a novel model for quantum state tomography (QST) that leverages the intrinsic structure of quantum measurements.
• To improve the fidelity and robustness of quantum state reconstruction from experimental data.
• To explore the analogy between structured sentences in natural language and structured quantum measurements.

Main Methods:

• Designed a quantum-aware transformer (QAT) model inspired by natural language processing transformers.
• Integrated quantum operators within the QAT architecture to process quantum data.
• Utilized Bures distance in the loss function to optimize quantum state fidelity.

Main Results:

• The QAT model demonstrated superior performance in reconstructing quantum states compared to existing methods.
• Achieved high-fidelity quantum state reconstruction.
• Showcased significant robustness against experimental noise in simulations and experiments on IBM quantum computers.

Conclusions:

• The quantum-aware transformer (QAT) presents a powerful new approach for quantum state tomography.
• Leveraging structured quantum measurements enhances the accuracy and reliability of QST.
• The QAT model offers a promising direction for advancing quantum information processing and quantum computing applications.