<![CDATA[Quantum state tomography is essential for characterizing quantum systems, yet conventional methods suffer from exponential scaling in measurement requirements, limiting their applicability in high-dimensional systems. Efficient reconstruction of quantum states with minimal measurements has become a critical challenge in advancing quantum information technologies. This study aims to develop and evaluate an adaptive quantum state tomography framework capable of reconstructing high-dimensional quantum states with reduced measurement resources while maintaining high accuracy. A theoretical–computational approach was employed, integrating Bayesian adaptive measurement strategies with convex optimization–based reconstruction algorithms. Simulations were conducted across varying system dimensions, state types, and noise conditions to assess performance. The results indicate that the proposed adaptive method significantly reduces the number of required measurements by up to 75% while achieving reconstruction fidelity comparable to full tomography. The approach demonstrates strong robustness under moderate noise and exhibits faster convergence compared to compressed sensing techniques. These findings suggest that adaptive quantum state tomography provides an efficient and scalable solution for quantum state reconstruction. This study concludes that integrating adaptive measurement selection with optimized reconstruction algorithms can overcome fundamental scalability challenges and support the development of practical quantum technologies.]]>
