Efficient and Robust Parameter Optimization of the Unitary Coupled-Cluster Ansatz
arXiv:2401.04910 · doi:10.1021/acs.jctc.4c00155
Abstract
The variational quantum eigensolver (VQE) framework has been instrumental in advancing near-term quantum algorithms. However, parameter optimization remains a significant bottleneck for VQE, requiring a large number of measurements for successful algorithm execution. In this paper, we propose sequential optimization with approximate parabola (SOAP) as an efficient and robust optimizer specifically designed for parameter optimization of the unitary coupled-cluster ansatz on quantum computers. SOAP leverages sequential optimization and approximates the energy landscape as quadratic functions, minimizing the number of energy evaluations required to optimize each parameter. To capture parameter correlations, SOAP incorporates the average direction from the previous iteration into the optimization direction set. Numerical benchmark studies on molecular systems demonstrate that SOAP achieves significantly faster convergence and greater robustness to noise compared to traditional optimization methods. Furthermore, numerical simulations up to 20 qubits reveal that SOAP scales well with the number of parameters in the ansatz. The exceptional performance of SOAP is further validated through experiments on a superconducting quantum computer using a 2-qubit model system.
References in corpus (20)
- The Variational Quantum Eigensolver: a review of methods and best practices
- Exact Parameterization of Fermionic Wave Functions via Unitary Coupled Cluster Theory
- Doubling the size of quantum simulators by entanglement forging
- TensorCircuit: a Quantum Software Framework for the NISQ Era
- Performance comparison of optimization methods on variational quantum algorithms
- Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers
- A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers
- Towards a Larger Molecular Simulation on the Quantum Computer: Up to 28 Qubits Systems Accelerated by Point Group Symmetry
- Purification-based quantum error mitigation of pair-correlated electron simulations
- Overlapped grouping measurement: A unified framework for measuring quantum states
- Benchmarking of Different Optimizers in the Variational Quantum Algorithms for Applications in Quantum Chemistry
- Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits
- Training variational quantum algorithms with random gate activation
- AGP-based unitary coupled cluster theory for quantum computers
- TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era
- Avoiding local minima in Variational Quantum Algorithms with Neural Networks
- Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder
- Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansatze for Hybrid Variational Quantum Computing
- Towards chemical accuracy with shallow quantum circuits: A Clifford-based Hamiltonian engineering approach
- Accurate and gate-efficient quantum ansätze for electronic states without adaptive optimisation