An Efficient Gradient Sensitive Alternate Framework for VQE with Variable Ansatz

Abstract

Variational quantum eigensolver (VQE), aiming at determining the ground state energy of a quantum system described by a Hamiltonian on noisy intermediate scale quantum (NISQ) devices, is among the most significant applications of variational quantum algorithms (VQAs). However, the accuracy and trainability of the current VQE algorithm are significantly influenced due to the barren plateau (BP), the non-negligible gate error and limited coherence time in NISQ devices. To tackle these issues, a gradient-sensitive alternate framework with variable ansatz is proposed in this paper to enhance the performance of the VQE. We first propose a theoretical framework for VA-VQE via alternately solving a multi-objective optimization problem and the original VQE, where the multi-objective optimization problem is defined with respect to cost function values and gradient magnitudes. Then, we propose a novel implementation method based on the double $\epsilon$-greedy strategy with the candidate tree and modified multi-objective genetic algorithm. As a result, the local optima are avoided both in ansatz and parameter perspectives, and the stability of output ansatz is enhanced. The experimental results indicate that our framework shows considerably improvement of the error of the found solution by up to 87.9% compared with the hardware-efficient ansatz. Furthermore, compared with the full-randomized VA-VQE implementation, our framework is able to obtain the improvement of the error and the stability by up to 36.0% and 58.7%, respectively, with similar quantum costs.

arXiv comment30 pages, 17 figures

References in corpus (32)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• A variational eigenvalue solver on a quantum processor
• Quantum algorithm for solving linear systems of equations
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Supervised learning with quantum enhanced feature spaces
• Barren plateaus in quantum neural network training landscapes
• Quantum Circuit Learning
• Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
• An adaptive variational algorithm for exact molecular simulations on a quantum computer
• The power of quantum neural networks
• Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
• Hamiltonian Simulation by Qubitization
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Efficient Learning for Deep Quantum Neural Networks
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Theory of variational quantum simulation
• Quantum computers can search arbitrarily large databases by a single query
• qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor
• An initialization strategy for addressing barren plateaus in parametrized quantum circuits
• Layerwise learning for quantum neural networks
• Qulacs: a fast and versatile quantum circuit simulator for research purpose
• Effect of barren plateaus on gradient-free optimization
• From transistor to trapped-ion computers for quantum chemistry
• Quantum circuit architecture search for variational quantum algorithms
• Large gradients via correlation in random parameterized quantum circuits
• Entanglement Devised Barren Plateau Mitigation
• Higher Order Derivatives of Quantum Neural Networks with Barren Plateaus
• Expressibility of the alternating layered ansatz for quantum computation
• The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation
• Variational Quantum Singular Value Decomposition
• Mutual information-assisted Adaptive Variational Quantum Eigensolver

Cited by in corpus (1)

• Hamiltonian-reconstruction distance as a success metric for the Variational Quantum Eigensolver