qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansätze on a quantum processor

Presenting Author: Ho Lun Tang, Virginia Tech
Contributing Author(s): Harper R. Grimsley, Nicholas J. Mayhall, Edwin Barnes, Sophia E. Economou

Quantum simulation, one of the most promising applications of a quantum computer, is currently pursued on noisy intermediate-scale quantum (NISQ) devices. The variational quantum eigensolver is extensively used for finding the ground state energy of molecular Hamiltonians. The feasibility and performance of this algorithm depend critically on the depths of the state preparation circuits that create the ansatz on the quantum processor and on the number of variational parameters in this ansatz. Recently, an algorithm termed ADAPT-VQE was introduced to build system-adapted ansätze with substantially fewer variational parameters compared to other approaches. ADAPT-VQE provides a way to create an ansatz iteratively based on a predetermined operator pool, from which the algorithm selects the most important to apply at each step. However, deep state preparation circuits remain a challenge. Here, we present a hardware-efficient variant of this algorithm called qubit-ADAPT. By numerical simulations on various molecules, we show that qubit-ADAPT can reduce the circuit depth by one order of magnitude while maintaining the same accuracy as the original ADAPT-VQE. Addressing the high measurement cost, which is proportional to the size of the operator pool, we show how to construct a sufficient pool with size linear in the number of qubits. This result highlights the promise of adaptive simulation algorithms on near-term quantum devices.

(Session : from 5:00pm - 7:00pm)


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