Advancing Quantum State Preparation Using Decision Diagram with Local Invertible Maps

• URL: http://arxiv.org/abs/2507.17170v2
• Date: Thu, 31 Jul 2025 07:42:03 GMT
• Title: Advancing Quantum State Preparation Using Decision Diagram with Local Invertible Maps
• Authors: Xin Hong, Aochu Dai, Chenjian Li, Sanjiang Li, Shenggang Ying, Mingsheng Ying,
• Abstract summary: We propose a family of efficient Quantum State Preparation (QSP) algorithms tailored to different numbers of available ancilla qubits.<n>Our approach exploits the power of Local Invertible Map Decision Diagrams (LimTDDs) to reduce quantum circuit complexity.
• Score: 5.328178128965817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum state preparation (QSP) is a fundamental task in quantum computing and quantum information processing. It is critical to the execution of many quantum algorithms, including those in quantum machine learning. In this paper, we propose a family of efficient QSP algorithms tailored to different numbers of available ancilla qubits - ranging from no ancilla qubits, to a single ancilla qubit, to a sufficiently large number of ancilla qubits. Our approach exploits the power of Local Invertible Map Tensor Decision Diagrams (LimTDDs) - a highly compact representation of quantum states that combines tensor networks and decision diagrams to reduce quantum circuit complexity. Extensive experiments demonstrate that our methods significantly outperform existing approaches and exhibit better scalability for large-scale quantum states, both in terms of runtime and gate complexity. Furthermore, our method shows exponential improvement in best-case scenarios.

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