Quantum Brain
← Back to papers

Quantum algorithms for grid-based variational time evolution

Pauline J. Ollitrault, Sven Jandura, Alexander Miessen, I. Burghardt, R. Martinazzo, F. Tacchino, I. Tavernelli·March 4, 2022·DOI: 10.22331/q-2023-10-12-1139
PhysicsComputer Science

AI Breakdown

Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.

Abstract

The simulation of quantum dynamics calls for quantum algorithms working in first quantized grid encodings. Here, we propose a variational quantum algorithm for performing quantum dynamics in first quantization. In addition to the usual reduction in circuit depth conferred by variational approaches, this algorithm also enjoys several advantages compared to previously proposed ones. For instance, variational approaches suffer from the need for a large number of measurements. However, the grid encoding of first quantized Hamiltonians only requires measuring in position and momentum bases, irrespective of the system size. Their combination with variational approaches is therefore particularly attractive. Moreover, heuristic variational forms can be employed to overcome the limitation of the hard decomposition of Trotterized first quantized Hamiltonians into quantum gates. We apply this quantum algorithm to the dynamics of several systems in one and two dimensions. Our simulations exhibit the previously observed numerical instabilities of variational time propagation approaches. We show how they can be significantly attenuated through subspace diagonalization at a cost of an additional O(MN2) 2-qubit gates where M is the number of dimensions and NM is the total number of grid points.

Related Research

Quantum Intelligence

Ask about quantum research, companies, or market developments.