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Benchmarking Variational Quantum Eigensolvers for Quantum Chemistry
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Quantum chemistry is one of the most promising applications of quantum computers in the near future. For noisy intermediate-scale quantum devices, the quantum-classical hybrid framework based on the variational quantum eigensolver (VQE) has become the method of choice. In the literature, there are many different variants of VQE, but it is not known which one is optimal for a given molecule. For this purpose, we perform a thorough benchmarking on more than ten different kinds of VQE ansatzes (in systems up to 30 qubits), based on their performance on the energy accuracy, runtime until convergence, and number of parameters. Our results show that the ADAPT ansatz can be used to obtain more accurate energy for small systems (below 14 qubits), but it costs much more computational resources. For larger molecules, UCCSD0 has better performance. However, all the tested ansatzes can hardly reach chemical accuracy at stretched bond lengths. Our results were obtained using MindSpore Quantum, where the codes and the benchmarking toolkit are publicly available at Gitee.
Forward citations
Cited by 2 Pith papers
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BenchQC: A Benchmarking Toolkit for Quantum Computation
VQE within a quantum-DFT embedding reproduces reference ground-state energies for Al-, Al2, and Al3- to within fractions of a percent on simulators and with IBM noise models.
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Simulating methylamine using symmetry adapted qubit-excitation-based variational quantum eigensolver
A combination of symmetry filtering, qubit-excitation circuits, and qubit tapering is estimated to cut the two-qubit gate count for a VQE methylamine simulation by nearly two orders of magnitude, but the headline gate...
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