An adaptive variational algorithm for exact molecular simulations on a quantum computer

By Harper R. Grimsley, Sophia E. Economou, Edwin Barnes, Nicholas J. Mayhall. Nature Communications, 2018

1,222 citations recorded by the source corpus.

Quantum simulation of chemical systems is one of the most promising near-term applications of quantum computers. The variational quantum eigensolver, a leading algorithm for molecular simulations on quantum hardware, has a serious limitation in that it typically relies on a pre-selected wavefunction ansatz that results in approximate wavefunctions and energies. Here we present an arbitrarily accurate variational algorithm that, instead of fixing an ansatz upfront, grows it systematically one operator at a time in a way dictated by the molecule being simulated. This generates an ansatz with a small number of parameters, leading to shallow-depth circuits. We present numerical simulations, including for a prototypical strongly correlated molecule, which show that our algorithm performs much better than a unitary coupled cluster approach, in terms of both circuit depth and chemical accuracy. Our results highlight the potential of our adaptive algorithm for exact simulations with present-day and near-term quantum hardware. Quantum algorithms for simulating chemical systems are limited because of the a priori assumption about the form of the target wavefunction. Here the authors present a new variational hybrid quantum-classical algorithm which allows the system being simulated to determine its own optimal state.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ecaa7f81d99a2acb99d1e7076a9b6ac33498bd2a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Barren plateaus in variational quantum computing (2024, neutral): “…small angle initializations (where one still randomly initializes the parameters but in a more structured way, such as in a special region) 65,66,68,188–196 , pre-training via classical 172,197–199 or quantum 200,201 methods, parameter transfer 138,139,202–208 and iterative learning strategies 23 .”
Quantum variational algorithms are swamped with traps (2022, positive): “In layer-wise VQE experiment of the main text, optimization is performed using an adaptive VQE algorithm similar to the one in [28].”
Quantum Generative Adversarial Networks for learning and loading random distributions (2019, background): “[47] H.”
qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor (2019, methodology): “This algorithm, termed Adaptive Derivative Assembled Pseudo-Trotter (ADAPT) VQE [30], employs a predetermined pool of operators from which the ansatz is dynamically constructed.”