Simultaneous determination of multiple low-energy eigenstates of many-body systems on a superconducting quantum processor

• URL: http://arxiv.org/abs/2507.14880v1
• Date: Sun, 20 Jul 2025 09:43:19 GMT
• Title: Simultaneous determination of multiple low-energy eigenstates of many-body systems on a superconducting quantum processor
• Authors: Huili Zhang, Yibin Guo, Guanglei Xu, Yulong Feng, Jingning Zhang, Hai-feng Yu, S. P. Zhao,
• Abstract summary: We employ the ancilla-entangled variational quantum eigensolver to simultaneously compute multiple low-lying eigenenergies and eigenstates of the H2 molecule and three- and five-spin transverse field Ising models (TFIMs) on a superconducting quantum processor.<n>Our experiments demonstrate that the algorithm is capable of simultaneously determining multiple eigenenergies and eigenstates of many-body systems with high efficiency and accuracy and with less computational resources.
• Score: 4.481806096924725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The determination of the ground and low-lying excited states is critical in many studies of quantum chemistry and condensed-matter physics. Recent theoretical work proposes a variational quantum eigensolver using ancillary qubits to generate entanglement in the variational circuits, which avoids complex ansatz circuits and successive measurements in the previous algorithms. In this work, we employ the ancilla-entangled variational quantum eigensolver to simultaneously compute multiple low-lying eigenenergies and eigenstates of the H2 molecule and three- and five-spin transverse field Ising models (TFIMs) on a superconducting quantum processor. We obtain the potential energy curves of H2 and show an indication of antiferromagnetic to paramagnetic phase transition in the TFIMs from the average absolute magnetization. Our experiments demonstrate that the algorithm is capable of simultaneously determining multiple eigenenergies and eigenstates of many-body systems with high efficiency and accuracy and with less computational resources.

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