Numerous quantum algorithms assume that classical data has already been converted into quantum states, a process known as Quantum State Preparation (QSP). However, achieving precise QSP requires a circuit depth that scales exponentially with the number of qubits, posing a significant challenge to realizing quantum advantage. Recent research explores Parameterized Quantum Circuits (PQCs) as an approximate alternative, offering improved scalability with reduced circuit depth. However, the iterative, state-by-state optimization required by this approach creates substantial runtime overhead, which severely limits its practicality.To improve the efficiency of approximate QSP, we introduce a novel two-stage framework that can potentially generate QSP circuits for arbitrary quantum states. In theoffline training stage, our model learns a direct mapping from target states to circuit parameters, thereby bypassing the need foronline, state-by-state optimizationduring theinference stage. Extensive evaluations show that our approach significantly reduces runtime overhead by up to 132×, making a steady step towards efficient neural approximate QSP.

Quantum circuit simulation (QCS) is essential in the noisy intermediate scale quantum (NISQ) era when real quantum computers are scarce. However, fully tracking the states of a quantum system in QCS is highly challenging due to the exponential memory growth that significantly limits the computational reach of classical systems for QCS. Though it is straightforward to leverage secondary storage to extend the scale of QCS, excessive data movement between memory and storage dominates the simulation time, making this solution unrealistic. To tackle this challenge, we identify an intrinsic property of QCS and implement an open-source framework to effectively reduce data movement by >116x. We evaluate the framework on various benchmarks and demonstrate 4x memory reduction with only <20% overhead. On a memory constrained system, we show that it extends the scale of QCS to 32 qubits (64 GB memory requirement) while existing simulators are bounded to 28 qubits (4 GB memory requirement). Our implementation can be accessed via https://github.com/Zhaoyilunnn/qdao.