Authors:

Justin Kim

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About Paper:

The Variational Quantum Eigensolver (VQE) is a quantum algorithm popular for potential applications in quantum chemistry-type calculations. More specifically, VQEs calculate an upper bound for molecular systems' ground state energies. This allows one to compute the optimal molecular structures (bond lengths and angles with the lowest energy)—a procedure called geometry optimization. Quantum algorithms such as VQE receive much interest as quantum computers have been predicted to be more efficient than their classical counterparts for quantum calculations. More specifically, the number of required qubits and quantum operations would scale less with system size. Amongst quantum algorithms, VQE shows promise through its resilience to quantum noise. However, VQEs still require further adjunct optimization procedures for applications in geometry optimization. Surrogate optimization is a noise-resistant procedure developed for conventional quantum chemistry algorithms such as Quantum Monte Carlo (QMC). QMC suffers from random noise, much like the VQE. Surrogate optimization mitigates the influence of noise in this optimization by considering an approximate Hessian for the studied system's ground state energy. This approximate Hessian is computed via a computationally cheaper surrogate theory. Then, the QMC energy can be minimized more efficiently by appropriately applying the approximate Hessian information. This method has been applied to a simulated VQE for a water molecule. Moving forward, a noise model will be applied to the simulated VQE such that it imitates a real quantum computer.This will test the practical viability of surrogate optimization for real VQEs.

Source:

Columbia / History / 2028

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Co-authors:

Justin Kim