Real-time dynamics of lattice gauge theories with a few-qubit quantum computer

By E. Martinez, Christine A. Muschik, Philipp Schindler, Daniel Nigg, A. Erhard, Markus Heyl, Philipp Hauke, Marcello Dalmonte, Thomas Monz, Peter Zoller and 1 more. Nature, 2016

831 citations recorded by the source corpus.

Gauge theories are fundamental to our understanding of interactions between the elementary constituents of matter as mediated by gauge bosons. However, computing the real-time dynamics in gauge theories is a notorious challenge for classical computational methods. This has recently stimulated theoretical effort, using Feynman’s idea of a quantum simulator, to devise schemes for simulating such theories on engineered quantum-mechanical devices, with the difficulty that gauge invariance and the associated local conservation laws (Gauss laws) need to be implemented. Here we report the experimental demonstration of a digital quantum simulation of a lattice gauge theory, by realizing (1 + 1)-dimensional quantum electrodynamics (the Schwinger model) on a few-qubit trapped-ion quantum computer. We are interested in the real-time evolution of the Schwinger mechanism, describing the instability of the bare vacuum due to quantum fluctuations, which manifests itself in the spontaneous creation of electron–positron pairs. To make efficient use of our quantum resources, we map the original problem to a spin model by eliminating the gauge fields in favour of exotic long-range interactions, which can be directly and efficiently implemented on an ion trap architecture. We explore the Schwinger mechanism of particle–antiparticle generation by monitoring the mass production and the vacuum persistence amplitude. Moreover, we track the real-time evolution of entanglement in the system, which illustrates how particle creation and entanglement generation are directly related. Our work represents a first step towards quantum simulation of high-energy theories using atomic physics experiments—the long-term intention is to extend this approach to real-time quantum simulations of non-Abelian lattice gauge theories.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/8c8bb5eb18d1a5b9160427c8874337204eb3dd15

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Citations in the corpus

Quantum simulation of fundamental particles and forces (2023, methodology): “The Schwinger model continues to be a "sandbox" for ideas and experiments [168, 176, 231, 232] for quantum simulation as it shares a number of features analogous to those of QCD. Quantum simulations of non-Abelian lattice gauge theories have also been performed on small lattices, such as SU(2) and…”
Practical quantum advantage in quantum simulation (2022, background): “The great strength of digital quantum simulators is the possibility to realize any desired Hamiltonian of the system 61,62 , providing opportunities to study wide-ranging models with no need to engineer this directly in the laboratory.”
Likelihood-Free Inference by Ratio Estimation (2016, background): “Such simulator-based (generative) models are used in a wide range of scientific disciplines to simulate different aspects of nature on the computer, ranging from sub-atomic particles (Martinez et al., 2016) to human societies (Turchin et al.”
A universal qudit quantum processor with trapped ions (2021, background): “Using auxiliary levels as a cache memory for quantum information has enabled the implementation of highly complex quantum algorithms [8] and dissipative processes [1], as well as improved decomposition of multi-qubit gates [6].”
Floquet approach to ℤ2 lattice gauge theories with ultracold atoms in optical lattices (2019, methodology): “First studies of the Schwinger model have been performed with quantum-classical algorithms [31] and a digital quantum computer composed of four trapped ions [32].”
Quantum information processing with superconducting circuits: a review (2016, neutral): “There is intense work on scaling up microfabricated ion traps [536, 537], with appli cations to benchmarking [538], computing [539–541] and simulation [542, 543].”