Design of an approximate quantum encoding model based on quantum circuits

Efficient preparation of amplitude-encoded quantum states representing high-dimensional classical data is essential for quantum information processing. However, the precise realization of such states faces a complexity barrier, since the required gate count grows exponentially with the system size, which makes implementation intractable on resource-constrained noisy intermediate-scale quantum (NISQ) devices. This paper proposes an approximate quantum encoding model (AQEM) based on parameterized quantum circuits. It is formulated as an efficient open-loop state preparation framework that reduces circuit depth and gate complexity to a polynomial scale while preserving high encoding fidelity. The framework adopts an offline-synthesis and online-deployment control paradigm: a simulation circuit is trained on a general-purpose simulator to map a precise amplitude-encoded state into a standard quantum state, and the inverse of this optimized circuit is then deployed as the implementation circuit in a NISQ device environment to reconstruct the approximate amplitude-encoded state. An automated architecture optimization strategy is integrated to enhance universality and adaptability. Numerical experiments on benchmark datasets validate the effectiveness, scalability, and expressiveness of the proposed framework, highlighting its feasibility for resource-efficient quantum data encoding in control and learning systems.