Graduate Student’s Productivity Tools for Literature Review Research and Writing in the Age of AI

In the fast-evolving world of academia, it is not hyperbole to say that generative AI and algorithm-based productivity tools like ChatGPT, Research Rabbit, and LitMap are quickly becoming transformative forces, reshaping the way graduate students (among many groups) approach the research and writing of thesis/dissertation literature reviews. But while the plethora of possibilities engendered by generative productivity tools is in many ways remarkable, the technology itself can often be overwhelming—not only for the graduate students, but also for us as librarians and information professionals supporting independent researchers from any discipline. Indeed, the ever-growing number of AI tools on the market suggests that the era of artificial intelligence is here. For this reason, it is critical that we develop the skills necessary to provide support and guidance to the increasing number of graduate students engaging with these advanced technologies. In this session, we will focus on providing librarians with the skills necessary to effectively communicate with graduate students about productivity tools enabling the creation of original research and writing. We will begin by presenting a structured framework (predicated heavily on established educational and LIS research) that can be used to categorize productivity tools. In a sense, this framework will provide librarians and other information professionals with a useful wayfinder that enables the diverse range of productivity tools available to be contextualized situationally, making them easier to understand. After discussing the framework, we will then explore a curated selection of AI/generative and other tools, showcasing their potential to facilitate various stages of independent and original graduate research. Finally, we will address the ethical and legal considerations entwined with the recommendation and implementation of these tools, thus fostering a culture of informed and ethical LIS research and practice. After attending this session, librarians will arguably have a better understanding of the tools that are out there, empowering them to match these tools with the specific needs of the graduate students that they serve

Mechanical Resonances of Mobile Impurities in a One-Dimensional Quantum Fluid

We study a one-dimensional interacting quantum liquid hosting a pair of mobile impurities causing backscattering. We determine the effective retarded interaction between the two impurities mediated by the liquid. We show that for strong backscattering this interaction gives rise to resonances and antiresonances in the finite-frequency mobility of the impurity pair. At the antiresonances, the two impurities remain at rest even when driven by a (small) external force. At the resonances, their synchronous motion follows the external drive in phase and reaches maximum amplitude. Using a perturbative renormalization group analysis in quantum tunneling across the impurities, we study the range of validity of our model. We predict that these mechanical antiresonances are observable in experiments on ultracold atom gases confined to one dimension

Variational Microcanonical Estimator

We propose a variational quantum algorithm for estimating microcanonical expectation values in models obeying the eigenstate thermalization hypothesis. Using a relaxed criterion for convergence of the variational optimization loop, the algorithm generates weakly entangled superpositions of eigenstates at a given target energy density. An ensemble of these variational states is then used to estimate microcanonical averages of local operators, with an error whose dominant contribution decreases initially as a power law in the size of the ensemble and is ultimately limited by a small bias. We apply the algorithm to the one-dimensional mixed-field Ising model, where it converges for ansatz circuits of depth roughly linear in system size. The most accurate thermal estimates are produced for intermediate energy densities. In our error analysis, we find connections with recent works investigating the underpinnings of the eigenstate thermalization hypothesis. In particular, the failure of energy-basis matrix elements of local operators to behave as \textit{independent} random variables is a potential source of error that the algorithm can overcome by averaging over an ensemble of variational states.Comment: 27 pages, 20 figures, latest version contains a revised error analysi

Design of an intelligent database for metal nuclear fuel experience at Argonne National Laboratory

The purpose of this research is to develop an information system for the IFR program, named the IFR Materials Information System (IMIS). This information system will be used for experimental analysis and eventually for licensing. Up until this point, there has been no real integrated information system that covered the entire metal nuclear fuel experience. The data were gathered over such a long period of time and by diverse research groups, that they had never been assembled in one information system where they could be analyzed or accessed in a convenient and easy way

Reconstructing Thermal Quantum Quench Dynamics from Pure States

Simulating the nonequilibrium dynamics of thermal states is a fundamental problem across scales from high energy to condensed matter physics. Quantum computers may provide a way to solve this problem efficiently. Preparing a thermal state on a quantum computer is challenging, but there exist methods to circumvent this by computing a weighted sum of time-dependent matrix elements in a convenient basis. While the number of basis states can be large, in this work we show that it can be reduced by simulating only the largest density matrix elements by weight, capturing the density matrix to a specified precision. Leveraging Hamiltonian symmetries enables further reductions. This approach paves the way to more accurate thermal-state dynamics simulations on near-term quantum hardware.Comment: 8+4 pages, 6+3 figure

Adaptive Variational Quantum Dynamics Simulations

We propose a general-purpose, self-adaptive approach to construct variational wavefunction ans\"atze for highly accurate quantum dynamics simulations based on McLachlan's variational principle. The key idea is to dynamically expand the variational ansatz along the time-evolution path such that the ``McLachlan distance'', which is a measure of the simulation accuracy, remains below a set threshold. We apply this adaptive variational quantum dynamics simulation (AVQDS) approach to the integrable Lieb-Schultz-Mattis spin chain and the nonintegrable mixed-field Ising model, where it captures both finite-rate and sudden post-quench dynamics with high fidelity. The AVQDS quantum circuits that prepare the time-evolved state are much shallower than those obtained from first-order Trotterization and contain up to two orders of magnitude fewer CNOT gate operations. We envision that a wide range of dynamical simulations of quantum many-body systems on near-term quantum computing devices will be made possible through the AVQDS framework.Comment: 12 pages, 7 figure

Adaptive variational quantum minimally entangled typical thermal states for finite temperature simulations

Scalable quantum algorithms for the simulation of quantum many-body systems in thermal equilibrium are important for predicting properties of quantum matter at finite temperatures. Here we describe and benchmark a quantum computing version of the minimally entangled typical thermal states (METTS) algorithm for which we adopt an adaptive variational approach to perform the required quantum imaginary time evolution. The algorithm, which we name AVQMETTS, dynamically generates compact and problem-specific quantum circuits, which are suitable for noisy intermediate-scale quantum (NISQ) hardware. We benchmark AVQMETTS on statevector simulators and perform thermal energy calculations of integrable and nonintegrable quantum spin models in one and two dimensions and demonstrate an approximately linear system-size scaling of the circuit complexity. We further map out the finite-temperature phase transition line of the two-dimensional transverse field Ising model. Finally, we study the impact of noise on AVQMETTS calculations using a phenomenological noise model.Comment: 13 pages, 6 figure

Problem-tailored Simulation of Energy Transport on Noisy Quantum Computers

The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum computers pose their own challenge, highlighting the need to be frugal with quantum resources. In this work we report simulations on quantum hardware of infinite-temperature energy transport in the mixed-field Ising chain, a paradigmatic many-body system that can exhibit a range of transport behaviors at intermediate times. We consider a chain with $L=12$ sites and find results broadly consistent with those from ideal circuit simulators over 90 Trotter steps, containing up to 990 entangling gates. To obtain these results, we use two key problem-tailored insights. First, we identify a convenient basis\unicode{x2013}the Pauli $Y$ basis\unicode{x2013}in which to sample the infinite-temperature trace and provide theoretical and numerical justifications for its efficiency relative to, e.g., the computational basis. Second, in addition to a variety of problem-agnostic error mitigation strategies, we employ a renormalization strategy that compensates for global nonconservation of energy due to device noise. We expect that these techniques will prove useful beyond the specific application considered here.Comment: 8 + 10 pages, 3 + 8 figure