Universal Effectiveness of High-Depth Circuits in Variational Eigenproblems
Not on the map. Why?
Abstract (arXiv)
We explore the effectiveness of variational quantum circuits in simulating the ground states of quantum many-body Hamiltonians. We show that generic high-depth circuits, performing a sequence of layer unitaries of the same form, can accurately approximate the desired states. We demonstrate their universal success by using two Hamiltonian systems with very different properties: the transverse field Ising model and the Sachdev-Ye-Kitaev model. The energy landscape of the high-depth circuits has a proper structure for the gradient-based optimization, i.e. the presence of local extrema -- near any random initial points -- reaching the ground level energy. We further test the circuit's capability of replicating random quantum states by minimizing the Euclidean distance.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 0; 2023: 3; 2024: 3; 2025: 1.
10 papers.
- 2021Theory of overparametrization in quantum neural networkscited by 100
- 2023Understanding quantum machine learning also requires rethinking generalizationcited by 48
- 2024On the relation between trainability and dequantization of variational quantum learning modelscited by 24
- 2023Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metriccited by 10
- 2025Double descent in quantum kernel methodscited by 9
- 2021Variational Quantum Classifiers Through the Lens of the Hessiancited by 4
- 2023Quantum approximate optimization via learning-based adaptive optimizationcited by 4
- 2024Resource-efficient equivariant quantum convolutional neural networkscited by 4
- 2021Entanglement Diagnostics for Efficient Quantum Computationcited by 2
- 2024Variational Optimization for Quantum Problems using Deep Generative Networkscited by 0
- 2025Double descent in quantum kernel methods17 Jan 2025
- 2024Resource-efficient equivariant quantum convolutional neural networks2 Oct 2024
- 2024On the relation between trainability and dequantization of variational quantum learning models11 Jun 2024
- 2024Variational Optimization for Quantum Problems using Deep Generative Networks28 Apr 2024
- 2023Understanding quantum machine learning also requires rethinking generalization23 Jun 2023
- 2023Quantum approximate optimization via learning-based adaptive optimization27 Mar 2023
- 2023Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metric23 Mar 2023
- 2021Theory of overparametrization in quantum neural networks23 Sep 2021
- 2021Variational Quantum Classifiers Through the Lens of the Hessian21 May 2021
- 2021Entanglement Diagnostics for Efficient Quantum Computation24 Feb 2021