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[Paper Review] Waveguide QED toolboxes for synthetic quantum matter with neutral atoms

Y. Dong, Y. S. Lee|arXiv (Cornell University)|Dec 6, 2017
Neural Networks and Reservoir Computing4 citations
TL;DR

This paper presents a programmable toolbox for engineering complex quantum spin networks using neutral atoms near 1D photonic crystal waveguides, where long-range interactions are mediated by collective atomic motion and phonons. The approach enables universal simulation of 2-local and long-range SU(n)-symmetric spin models via dynamically generated gauge fields, offering a scalable platform for synthetic quantum matter.

ABSTRACT

An exciting frontier in quantum information science is the realization of complex many-body systems whose interactions are designed quanta by quanta. Hybrid nanophotonic system with cold atoms has emerged as the paradigmatic platform for engineering long-range spin models from the bottom up with unprecedented complexities. Here, we develop a toolbox for realizing a fully programmable complex spin-network with neutral atoms in the vicinity of 1D photonic crystal waveguides. The enabling platform synthesizes strongly interacting quantum materials mediated by phonons from the underlying collective external motion of the atoms. We generalize our approach of universal 2-local Hamiltonians to long-range lattice models for interacting SU(n)-magnons mediated by local dynamical gauge fields.

Motivation & Objective

  • To develop a fully programmable platform for simulating complex many-body quantum systems with engineered long-range interactions.
  • To overcome limitations of short-range interactions in ultracold atom systems by leveraging photonic waveguides and collective atomic motion.
  • To realize universal simulation of 2-local and long-range spin Hamiltonians using neutral atoms in a photonic environment.
  • To extend the framework to SU(n)-symmetric magnon models via local dynamical gauge fields.
  • To provide a scalable architecture for synthetic quantum matter with tunable, quanta-by-quanta control of interactions.

Proposed method

  • Utilizes 1D photonic crystal waveguides to mediate long-range interactions between neutral atoms via photon exchange.
  • Engineers interactions through collective external motion of atoms, coupling to phonons in the waveguide mode.
  • Constructs universal 2-local Hamiltonians using controlled atom-waveguide coupling and tunable laser drives.
  • Introduces local dynamical gauge fields to mediate long-range interactions in SU(n)-symmetric magnon models.
  • Employs a theoretical framework based on effective spin Hamiltonians derived from strong coupling between atoms and waveguide modes.
  • Generalizes the approach to support complex lattice models with tunable interaction ranges and symmetries.

Experimental results

Research questions

  • RQ1How can long-range spin interactions be engineered in a fully programmable way using neutral atoms and photonic waveguides?
  • RQ2What role do collective atomic motion and phonons play in mediating effective spin-spin interactions?
  • RQ3Can the platform simulate universal 2-local Hamiltonians with high tunability and scalability?
  • RQ4How can SU(n)-symmetric magnon models be realized through dynamically generated gauge fields in this architecture?
  • RQ5What is the feasibility of extending this toolbox to complex lattice geometries with long-range, symmetry-protected interactions?

Key findings

  • The platform enables universal simulation of 2-local spin Hamiltonians through engineered atom-waveguide coupling and collective motion.
  • Long-range interactions are mediated via phonons from collective atomic motion, enabling tunable interaction ranges.
  • The framework supports the realization of SU(n)-symmetric magnon models through local dynamical gauge fields.
  • The system allows for quanta-by-quanta design of interactions, offering unprecedented control over many-body Hamiltonians.
  • The approach provides a scalable architecture for synthetic quantum matter with complex spin networks.

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This review was created by AI and reviewed by human editors.