Chronological feed of everything captured from Claire L. Edmunds.
This paper details a novel electron shelving detection routine for ${}^{171}$Yb$^{+}$ hyperfine qubits, addressing the challenge of low fluorescence yield and improving detection fidelity. The method significantly reduces single-ion detection errors, offering a pathway for scalable and ultra-high fidelity quantum information processing compatible with existing hardware limitations.
Researchers have successfully engineered spin-1 Haldane phase chains using trapped-ion qutrits on a qudit quantum processor. This work introduces a scalable and deterministic method for preparing the Affleck-Kennedy-Lieb-Tasaki (AKLT) state. This advancement enables the efficient exploration of quantum systems beyond traditional spin-1/2 models, overcoming classical simulation challenges inherent to intrinsic quantum phases.
This paper introduces a tensorial kernel support vector machine (TK-SVM) as an unsupervised machine learning approach for analyzing quantum data from trapped-ion experiments. The TK-SVM successfully differentiates between trivial and symmetry-protected topological (SPT) phases, even in noisy experimental datasets. This method offers a directly interpretable alternative to conventional machine learning techniques that often require prior training.
Researchers used a trapped-ion qudit quantum processor to observe dynamical localization in a disorder-free S=1 Floquet model. They identified an emergent 3T subharmonic response, indicating non-ergodic dynamics beyond qubit systems. Numerical simulations linked multipartite entanglement, via Quantum Fisher Information, to the transition between ergodic and localized regimes, opening avenues for studying ergodicity-breaking in higher-dimensional quantum systems.