Efficient Encoding of Matrix Product States into Quantum Circuits of One- and Two-Qubit Gates
A/Prof. Shi-Ju Ran
Department of Physics, Capital Normal University

Matrix product state (MPS) belongs to the most important mathematical models in, for example, condensed matter physics and quantum information sciences. However, to realize an N-qubit MPS with large N and large entanglement on a quantum platform is extremely challenging, since it requires high-level qubits or multi-body gates of two-level qubits to carry the entanglement. In this work, an efficient method that accurately encodes a given MPS into a quantum circuit with only one- and two-qubit gates is proposed. The idea is to construct the unitary matrix product operators that optimally disentangle the MPS to a product state. These matrix product operators form the quantum circuit that evolves a product state to the targeted MPS with a high fidelity. Our benchmark on the ground-state MPS's of the strongly-correlated spin models show that the constructed quantum circuits can encode the MPS's with much fewer qubits than the sizes of the MPS's themselves. This method paves a feasible and efficient path to realizing quantum many-body states and other MPS-based models as quantum circuits on the near-term quantum platforms.


About the Speaker

冉仕举, 2010年本科毕业于北京师范大学物理学系;2015年博士毕业于中国科学院大学物理学院;2015至2018年于西班牙光子科学研究所从事博士后研究,并于2017年获Fundacio-Catalunya独立博士后研究员fellowship;2018年入职首都师范大学物理系。研究方向是强关联系统的数值计算与量子模拟、量子机器学习模型与算法,主要使用的理论工具是张量网络。张量网络是一种起源于量子信息科学的强大的数值工具,其不但可用于高效处理量子多体系统,最近还被用于发展量子多体态空间的机器学习模型,实现基于量子理论的人工智能。

2019-11-04 9:30 AM
Room: A403 Meeting Room
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