Publications

Showing 27 results for Author: Claire E. Marvinney

  • Aug, 2026

    Journal

    Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum co…

  • Mar, 2026

    Conference Paper

    We implement a continuous-variable quantum compiler that can learn a linear phase operation with a fourfold increase in precision and a factor of 80 reduction in time to solution when quantum resources are used.

  • Oct, 2025

    Journal

    Dark matter candidates with masses around the Planck scale are theoretically well-motivated, and it has been suggested that it might be possible to search for dark matter solely via gravitational interactions in this mass range. In this work, we explore the pathway towards searching for dark matter candidates with masses around the Planck scale using mechanical sensors whi…

  • Aug, 2025

    Journal

    Optomechanical sensors are capable of transducing external perturbations to resolvable optical signals. A particular regime of interest is that of high-bandwidth force detection, where an impulse is delivered to the system over a short period of time. Exceedingly sensitive impulse detection has been proposed to observe very weak signals like those due to long-range interac…

  • Jun, 2025

    Journal

    In recent years, distributed quantum sensing has gained interest for a range of applications requiring networks of sensors, from global-scale clock synchronization to high energy physics. In particular, a network of entangled sensors can improve not only the sensitivity beyond the shot noise limit, but also enable a Heisenberg scaling with the number of sensors. Here, usin…

  • Jul, 2024

    Journal

    Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology…

  • Dec, 2023

    Journal

    The combination of nontrivial band topology and symmetry-breaking phases gives rise to novel quantum states and phenomena such as topological superconductivity, quantum anomalous Hall effect, and axion electrodynamics. Evidence of intertwined charge density wave (CDW) and superconducting order parameters has recently been observed in a novel kagome material AV3Sb5 (A=K, Rb…

  • Dec, 2023

    Journal

    Magneto-optical sensors including spin noise spectroscopies and magneto-optical Kerr effect microscopies are now ubiquitous tools for materials characterization that can provide new understanding of spin dynamics, hyperfine interactions, spin-orbit interactions, and charge-carrier g-factors. Both interferometric and intensity-difference measurements can provide photon-shot…

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