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Creators/Authors contains: "Monroe, Christopher"

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Nature Physics)
    Free, publicly-accessible full text available June 2, 2023
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Nature)
    Free, publicly-accessible full text available October 14, 2022
  3. ; ; ; ; ; ; ( , Physical Review Letters)
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  4. ; ; ; ; ; ( , Physical Review Research)
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  5. ; ; ; ; ; ; ; ( , Nature Communications)
    Abstract

    The quantum walk formalism is a widely used and highly successful framework for modeling quantum systems, such as simulations of the Dirac equation, different dynamics in both the low and high energy regime, and for developing a wide range of quantum algorithms. Here we present the circuit-based implementation of a discrete-time quantum walk in position space on a five-qubit trapped-ion quantum processor. We encode the space of walker positions in particular multi-qubit states and program the system to operate with different quantum walk parameters, experimentally realizing a Dirac cellular automaton with tunable mass parameter. The quantum walk circuits andmore »position state mapping scale favorably to a larger model and physical systems, allowing the implementation of any algorithm based on discrete-time quantum walks algorithm and the dynamics associated with the discretized version of the Dirac equation.

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  6. ; ; ; ; ; ; ; ; ; ; et al ( , Proceedings of the National Academy of Sciences)
    Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of themore »variational parameters, approximating the ground-state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward toward the application of the QAOA to more general problem instances.« less
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  7. ; ; ; ; ; ; ; ; ; ; et al ( , PRX Quantum)
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  8. ; ( , Quantum Science and Technology)
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  9. ; ; ( , Science)
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  10. ; ; ; ; ; ; ( , Physical Review A)
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