Quick Navigation
Topics
Superconducting Qubits
Variational Hybrid Quantum Algorithms
Simultaneous determination of multiple low-lying energy levels on a superconducting quantum processor
arXiv
Authors: Huili Zhang, Yibin Guo, Guanglei Xu, Yulong Feng, Jingning Zhang, Hai-feng Yu, S. P. Zhao
Year
2026
Paper ID
3329
Status
Preprint
Abstract Read
~2 min
Abstract Words
145
Citations
N/A
Abstract
Determining the ground and low-lying excited states is critical in numerous scenarios. Recent work has proposed the ancilla-entangled variational quantum eigensolver (AEVQE) that utilizes entanglement between ancilla and physical qubits to simultaneously tagert multiple low-lying energy levels. In this work, we report the experimental implementation of the AEVQE on a superconducting quantum cloud platform, demonstrating the full procedure of solving the low-lying energy levels of the H2 molecule and the transverse-field Ising models (TFIMs). We obtain the potential energy curves of H2 and show an indication of the ferromagnetic to paramagnetic phase transition in the TFIMs from the average absolute magnetization. Moreover, we investigate multiple factors that affect the algorithmic performance and provide a comparison with ancilla-free VQE algorithms. Our work demonstrates the experimental feasibility of the AEVQE algorithm and offers a guidance for the VQE approach in solving realistic problems on publicly-accessible quantum platforms.
Why This Paper Matters
- This paper contributes to the Superconducting Qubits research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- Determining the ground and low-lying excited states is critical in numerous scenarios.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.