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A Quantum Neural Network with Built-In Self-Attention Mechanism

Shangshang Shi, Zhimin Wang, Jiaxin Li, Yanan Li, Ruimin Shang, Haiyong Zheng, Guoqiang Zhong, Yongjian Gu, Xin Huang

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.2139/ssrn.5297284

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Quantum Self-Attention Neural Network (QSANN) has demonstrated remarkable potential. However, it is limited to measuring only partial information from qubits, thereby restricting its information extraction capability. Moreover, the measurement process requires multiple measurements for each qubit, leading to inefficiency in utilizing the feature space. To address these challenges, this paper introduces a novel approach called Positive Operator-Valued Measure based Quantum Self-Attention Neural Network (POVM-QSANN). It leverages POVM operators as observables, enabling the extraction of comprehensive QKV (Query-Key-Value) feature vectors from each qubit. This innovative mechanism significantly enhances information extraction capability and efficiently utilizes the feature space. The experimental results underscore the considerable advancements of POVM-QSANN on MC and RP datasets. Particularly noteworthy is its remarkable accuracy increase of 9.68% compared to QSANN on the RP dataset.
We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer), which can be used as an approximation (or Ansatz) for solving the many-electron Schrödinger equation, the fundamental equation for quantum chemistry and material science. This equation can be solved from first principles, requiring no external training data. In recent years, deep neural networks like the FermiNet and PauliNet have been used to significantly improve the accuracy of these first-principle calculations, but they lack an attention-like mechanism for gating interactions between electrons. Here we show that the Psiformer can be used as a drop-in replacement for these other neural networks, often dramatically improving the accuracy of the calculations. On larger molecules especially, the ground state energy can be improved by dozens of kcal/mol, a qualitative leap over previous methods. This demonstrates that self-attention networks can learn complex quantum mechanical correlations between electrons, and are a promising route to reaching unprecedented accuracy in chemical calculations on larger systems.