SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation

TL;DR

This paper introduces SuperEncoder, a neural network-based approach to generate quantum state preparation circuits directly, reducing the need for iterative updates and making approximate quantum state preparation more scalable and practical.

Contribution

It proposes a pre-trained neural network model that can directly generate quantum state preparation circuits, advancing towards a universal neural quantum state preparer.

Findings

Pre-trained neural network effectively generates QSP circuits for arbitrary states.

Reduces runtime by eliminating iterative parameter updates.

Demonstrates potential for scalable quantum state preparation.

Abstract

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.