Simulating dirty bosons on a quantum computer
Lindsay Bassman Oftelie, Roel Van Beeumen, Daan Camps, Wibe A. de, Jong, Maxime Dupont

TL;DR
This paper explores how quantum computers can simulate dirty bosons, revealing the effects of disorder, interactions, and hardware noise on quantum phase transitions in one and two dimensions.
Contribution
It introduces methods for simulating dirty bosons on quantum computers, including adiabatic state preparation and noise analysis, applicable to current and future quantum hardware.
Findings
Quantum circuits can be compressed for 1D simulations on current hardware.
Large-scale classical simulations are needed for 2D due to circuit complexity.
Noise affects localized and delocalized phases differently, with scaling laws governing these effects.
Abstract
The physics of dirty bosons highlights the intriguing interplay of disorder and interactions in quantum systems, playing a central role in describing, for instance, ultracold gases in a random potential, doped quantum magnets, and amorphous superconductors. Here, we demonstrate how quantum computers can be used to elucidate the physics of dirty bosons in one and two dimensions. Specifically, we explore the disorder-induced delocalized-to-localized transition using adiabatic state preparation. In one dimension, the quantum circuits can be compressed to small enough depths for execution on currently available quantum computers. In two dimensions, the compression scheme is no longer applicable, thereby requiring the use of large-scale classical state vector simulations to emulate quantum computer performance. In addition, simulating interacting bosons via emulation of a noisy quantum…
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 many-body systems · Cold Atom Physics and Bose-Einstein Condensates · Neural Networks and Reservoir Computing
