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Andrew Cox

Publications and source records attributed to Andrew Cox.

3 recordsLinked to original sources

Large-scale quantum simulations of dissipative spin-1/2 Heisenberg chains

A quantum many-body system coupled to an environment relaxes to a nonequilibrium steady state that can sustain order with no equilibrium counterpart. Computing such steady states is harder than closed-system dynamics as the density matrix problem squares the Hilbert-space dimension, and no free energy selects the steady state. The dissipative spin-1/2 Heisenberg chain is a benchmark example for nonequilibrium steady state physics; various methods have each calculated its phase diagram but do not agree, and a controlled determination at large system size has remained out of reach. Here we simulate the Lindblad dynamics of chains of up to 50 sites on the superconducting processor ibm_kingston -- 100 simultaneously active qubits at up to 1700 entangling-gate depths -- realizing the dissipation via Stinespring dilation. The system's dissipative evolution is a self-correcting mechanism that effectively erases errors, so hardware noise enters only as a weak competing dissipator. We measure static structure factors and resolve ferromagnetic, antiferromagnetic, spin-density-wave, and paramagnetic steady states, mapping the phase diagram with 117 quantum hardware data points across the $J_x$--$J_y$ plane. We uncover a rich non-equilibrium phase diagram of ordered phases with only remnants of the mean-field order, and where sharp transitions give way to the crossovers expected in one dimension. We also find the existence of an incipient (Trotter-induced) spin density wave phase, highlighting the potential of controlled Trotterization as a tool to engineer various magnetic phases in dissipative spin systems. Our quantum simulations largely settle the lingering uncertainty regarding the correct phase diagram of this benchmark system. Moreover, they show that quantum computers are now a feasible tool for addressing scientific questions involving dissipative quantum systems.

quant-ph

A dancing bear, a colleague, or a sharpened toolbox? The cautious adoption of generative AI technologies in digital humanities research

The advent of generative artificial intelligence (GenAI) technologies has been changing the research landscape and potentially has significant implications for Digital Humanities (DH), a field inherently intertwined with technologies. This article investigates how DH scholars adopt and critically evaluate GenAI technologies for research. Drawing on 76 responses collected from an international survey study and 15 semi-structured interviews with DH scholars, we explored the rationale for adopting GenAI tools in research, identified the specific practices of using GenAI tools, and analyzed scholars' collective perceptions regarding the benefits, risks, and challenges. The results reveal that DH research communities hold divided opinions and differing imaginations towards the role of GenAI in DH scholarship. While scholars acknowledge the benefits of GenAI in enhancing research efficiency and enabling reskilling, many remain concerned about its potential to disrupt their intellectual identities. Situated within the history of DH and viewed through the lens of Actor-Network Theory, our findings suggest that the adoption of GenAI is gradually changing the field, though this transformation remains contested, shaped by ongoing negotiations among multiple human and non-human actors. Our study is one of the first empirical analyses on this topic and has the potential to serve as a building block for future inquiries into the impact of GenAI on DH scholarship.

cs.AI

Estimating the quality of academic books from their descriptions with ChatGPT

Although indicators based on scholarly citations are widely used to support the evaluation of academic journals, alternatives are needed for scholarly book acquisitions. This article assesses the value of research quality scores from ChatGPT 4o-mini for 9,830 social sciences, arts, and humanities books from 2019 indexed in Scopus, based on their titles and descriptions but not their full texts. Although most books scored the same (3* on a 1* to 4* scale), the citation rates correlate positively but weakly with ChatGPT 4o-mini research quality scores in both the social sciences and the arts and humanities. Part of the reason for the differences was the inclusion of textbooks, short books, and edited collections, all of which tended to be less cited and lower scoring. Some topics also tend to attract many/few citations and/or high/low ChatGPT scores. Descriptions explicitly mentioning theory and/or some methods also associated with higher scores and more citations. Overall, the results provide some evidence that both ChatGPT scores and citation counts are weak indicators of the research quality of books. Whilst not strong enough to support individual book quality judgements, they may help academic librarians seeking to evaluate new book collections, series, or publishers for potential acquisition.

cs.DL