Preentangling Quantum Algorithms -- the Density Matrix Renormalization Group-assisted Quantum Canonical Transformation
Mohsin Iqbal, David Mu\~noz Ramo, Henrik Dreyer

TL;DR
This paper introduces a novel quantum algorithm approach that combines classical density matrix renormalization group methods with quantum canonical transformations, reducing parameters needed for electronic structure calculations near multi-reference points.
Contribution
It presents a parameter-free preentangler method integrated with quantum algorithms and a new Matrix Product State preparation algorithm, enhancing efficiency in quantum chemistry simulations.
Findings
Requires fewer parameters than generalized unitary coupled cluster circuits near multi-reference points.
Demonstrates effectiveness on systems like H2O, N2, BeH2, and P4.
Proposes a new MPS preparation algorithm based on the Linear Combination of Unitaries.
Abstract
We propose the use of parameter-free preentanglers as initial states for quantum algorithms. We apply this idea to the electronic structure problem, combining a quantized version of the Canonical Transformation by Yanai and Chan [J. Chem. Phys. 124, 194106 (2006)] with the Complete Active Space Density Matrix Renormalization Group. This new ansatz allows to shift the computational burden between the quantum and the classical processor. In the vicinity of multi-reference points in the potential energy surfaces of HO, N, BeH and the P4 system, we find this strategy to require significantly less parameters than corresponding generalized unitary coupled cluster circuits. We propose a new algorithm to prepare Matrix Product States based on the Linear Combination of Unitaries and compare it to the Sequential Unitary Algorithm proposed by Ran in [Phys. Rev. A 101, 032310 (2020)].
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Machine Learning in Materials Science
