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A scalable superconducting quantum simulator with long-range connectivity based on a photonic bandgap metamaterial

By Xueyue Zhang, Eunjong Kim, Daniel K. Mark , Soonwon Choi, Oskar Painter
2022
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Synthesis of many-body quantum systems in the laboratory can provide further insight into the emergent behavior of quantum materials. While the majority of engineerable many-body systems, or quantum simulators, consist of particles on a lattice with local interactions, quantum systems featuring long-range interactions are particularly difficult to model and interesting to study due to the rapid spatio-temporal growth of entanglement in such systems. Here we present a scalable quantum simulator architecture based on superconducting transmon qubits on a lattice, with interactions mediated by the exchange of photons via a metamaterial waveguide quantum bus. The metamaterial waveguide enables extensible scaling of the system and multiplexed qubit read-out, while simultaneously protecting the qubits from radiative decay. As an initial demonstration of this platform, we realize a 10-qubit simulator of the one-dimensional Bose-Hubbard model, with in situ tunability of both the hopping range and the on-site interaction. We characterize the Hamiltonian of the system using a measurement-efficient protocol based on quantum many-body chaos, uncovering the remnant phase of Bloch waves of the metamaterial bus in the long-range hopping terms. We further study the many-body quench dynamics of the system, revealing through global bit-string statistics the predicted crossover from integrability to ergodicity as the hopping range is extended beyond nearest-neighbor. Looking forward, the metamaterial quantum bus may be extended to a two-dimensional lattice of qubits, and used to generate other spin-like lattice interactions or tailored lattice connectivity, expanding the accessible Hamiltonians for analog quantum simulation using superconducting quantum circuits.
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US, CA, San Francisco
We are seeking to hire software engineers who are excited about research for robot manipulation with the goal of developing manipulation systems at human-level performance. Our strategy focuses on a real2sim2real pipeline that learns from large scale human videos and leverage simulation and reinforcement learning to bridge the human-to-robot embodiment gap. Key job responsibilities • Learning manipulation: Design and implement robot learning algorithms for manipulation • Leverage vision models to extract 3D hand-object manipulation information from real-world situations • Develop large-scale real2sim2real pipeline for manipulation • Multimodal Sensor Fusion: Integrate tactile sensing, proprioception and vision into the learning pipeline About the team At Frontier AI & Robotics (FAR), we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
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