Variational quantum algorithm with information sharing

Abstract

We introduce an optimisation method for variational quantum algorithms and experimentally demonstrate a 100-fold improvement in efficiency compared to naive implementations. The effectiveness of our approach is shown by obtaining multi-dimensional energy surfaces for small molecules and a spin model. Our method solves related variational problems in parallel by exploiting the global nature of Bayesian optimisation and sharing information between different optimisers. Parallelisation makes our method ideally suited to next generation of variational problems with many physical degrees of freedom. This addresses a key challenge in scaling-up quantum algorithms towards demonstrating quantum advantage for problems of real-world interest.

arXiv comment10 pages, 6 figures, use of IBM Quantum devices

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• Surrogate-based optimization for variational quantum algorithms
• Networked Quantum Services
• Natural parameterized quantum circuit
• Faster variational quantum algorithms with quantum kernel-based surrogate models
• Bayesian Learning of Parameterised Quantum Circuits
• Digital-analog quantum learning on Rydberg atom arrays
• Convergence and Quantum Advantage of Trotterized MERA for Strongly-Correlated Systems
• Sketching phase diagrams using low-depth variational quantum algorithms
• Quantum Mini-Apps: A Framework for Developing and Benchmarking Quantum-HPC Applications
• Variational Quantum Gate Optimization at the Pulse Level
• Qubit Readout Error Mitigation with Bit-flip Averaging
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