Optimal qubit mapping search for encoding classical data into matrix product state representation with minimal loss

被引：0

作者：

Jeon, Hyeongjun ^{[1
]}

Lee, Kyungmin ^{[1
]}

Lee, Dongkyu ^{[2
]}

Kim, Bongsang ^{[2
]}

Kim, Taehyun ^{[1
,3
,4
,5
,6
]}

机构：

[1] Seoul Natl Univ, Dept Comp Sci & Engn, Seoul 08826, South Korea

[2] Quantum AI Dept, AI Lab, CTO, LG Elect, Seoul 06772, South Korea

[3] Seoul Natl Univ, Automat & Syst Res Inst, Seoul 08826, South Korea

[4] Seoul Natl Univ, Interuniv Semicond Res Ctr, Seoul 08826, South Korea

[5] Seoul Natl Univ, Inst Comp Technol, Seoul 08826, South Korea

[6] Seoul Natl Univ, Inst Appl Phys, Seoul 08826, South Korea

来源：

PHYSICS LETTERS A | 2024年 / 516卷

基金：

新加坡国家研究基金会;

关键词：

Quantum computing; Quantum encoding; Matrix product state; Machine learning; QUANTUM COMPUTATION; TENSOR NETWORKS;

D O I：

10.1016/j.physleta.2024.129642

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the efficiency and accuracy of MPS representations specifically designed for encoding classical data. Based on the observations that MPS truncation error depends on the pattern of the classical data, we devised an algorithm that finds optimal qubit mapping for given classical data, thereby improving the efficiency and fidelity of the MPS representation. Furthermore, we evaluate the impact of the optimized MPS in the context of quantum classifiers, demonstrating their enhanced performance compared to the conventional mapping. This improvement confirms the efficacy of the proposed techniques for encoding classical data into quantum states. MPS representation combined with optimal qubit mapping can pave a new way for more efficient and accurate quantum data representation and processing.

引用

页数：11