SuperEncoder: Towards Efficient Neural Approximate Quantum State Preparation

doi:10.1109/TC.2025.3644034

SuperEncoder: Towards Efficient Neural Approximate Quantum State Preparation

2025-12-15

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DOI：10.1109/TC.2025.3644034

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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 the offline training stage, our model learns a direct mapping from target states to circuit parameters, thereby bypassing the need for online, state-by-state optimization during the inference stage. Extensive evaluations show that our approach significantly reduces runtime overhead by up to 132$\times$, making a steady step towards efficient neural approximate QSP.

Keywords:

Quantum state preparation

performance optimization

SuperEncoder: Towards Efficient Neural Approximate Quantum State Preparation

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