The Study of Custom Methods and Document Estimates for Quantum State Estimation

Abstract

Quantum state estimation is essential for quantum communication and computing. This study applies maximum likelihood estimation, Bayesian inference, and document-based pattern matching. The hybrid framework enhances accuracy, reduces redundancy, and accelerates classification. Two- and three-qubit noisy systems were analyzed for validation. Results showed higher fidelity and lower estimation errors with Bayesian methods. Spin-gap comparisons confirmed statistical reliability and physical relevance of the approach. The framework supports NISQ devices and hybrid quantum-classical platforms. Future work will explore hardware testing, larger qubit arrays, and machine learning integration.