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Update 01/06/2023:
Read about our sneak-peak FAST-VQE hardware validation results here.
We are proud and cautiously optimistic about our new quantum algorithm FAST-VQE, which mitigates the measurement overhead of ADAPT-VQE for quantum chemistry.
One of the main areas, where quantum computing is thought to have a major industrial impact is quantum chemistry. However, with the current Noisy Intermediate-Scale Quantum (NISQ) hardware, noise and errors may lead to wrong solutions. Therefore, much work in the scientific community is going into developing suitable algorithms for these devices.
ADAPT-VQE has been the leading candidate for a NISQ-relevant quantum chemistry algorithm, useful for chemistry simulations with quantum computers. However, one major bottleneck with the ADAPT-VQE is the significant measurement resources required for estimating the importance of operators in the wave function.
Our team has shown a way to avoid the measurement bottleneck of this approach. The method is called Fermionic Adaptive Sampling Theory VQE, or just FAST-VQE, and while the work is still in peer review, we have preliminary results which show convergence with dramatically fewer shots than ADAPT-VQE!
We are now able to share the preprint, which is available on arXiv via the following link:
[2303.07417] Fermionic Adaptive Sampling Theory for Variational Quantum Eigensolvers (arxiv.org)
