SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation
This work addresses a bottleneck in quantum computing for researchers and practitioners by providing a more efficient method for state preparation, though it is incremental as it builds on existing neural and quantum techniques.
The paper tackles the problem of lengthy runtime in approximate Quantum State Preparation (QSP) by using a pre-trained neural network to directly generate QSP circuits, eliminating online iterations and achieving a 10x speedup in runtime compared to iterative methods.
Numerous quantum algorithms operate under the assumption that classical data has already been converted into quantum states, a process termed Quantum State Preparation (QSP). However, achieving precise QSP requires a circuit depth that scales exponentially with the number of qubits, making it a substantial obstacle in harnessing quantum advantage. Recent research suggests using a Parameterized Quantum Circuit (PQC) to approximate a target state, offering a more scalable solution with reduced circuit depth compared to precise QSP. Despite this, the need for iterative updates of circuit parameters results in a lengthy runtime, limiting its practical application. In this work, we demonstrate that it is possible to leverage a pre-trained neural network to directly generate the QSP circuit for arbitrary quantum state, thereby eliminating the significant overhead of online iterations. Our study makes a steady step towards a universal neural designer for approximate QSP.