The aim of the Quantum Games workshop is to enable participants to develop an intuition for the particularly “non-intuitive” principles of quantum mechanics, such as superposition and wavefunction collapse (i.e., quantum measurement). These phenomena have important applications across the physical sciences, and some of the most exciting aspects are related to recent progress in quantum computing! The goal of the workshop is to teach participants to reason intuitively about quantum systems, thus enabling them to use their unique perspectives to become future leaders in the field of quantum information science!

Our research aims to analyze the impact of quantum mechanics-themed games on the development of high school students' intuition and interest in topics related to quantum phenomena, such as quantum superposition and quantum measurement. In the United States, quantum concepts are introduced at a fragmented level in high schools, and efforts to implement a quantum curriculum at the secondary level have been limited. This presentation describes the development of a workshop, and the associated research study, scheduled for implementation in the late winter and spring of 2024. The workshop utilizes an interactive game-play method to introduce students to concepts in quantum computing. We anticipate that this workshopbased pedagogical approach will enhance students' ability to connect intuitive and non-intuitive concepts in QISE (Quantum Information Science and Engineering) from their experience of playing the game. We hypothesize that enabling high school students to interact with quantum mechanics concepts during their secondary education will foster sustained interest and potentially encourage them to pursue further studies in the field of QISE.

Chemical reactions are commonly described by the reactive flux transferring the population from reactants to products across a double-well free energy barrier. Dynamics often involves barrier recrossing and quantum effects like tunneling, zero-point energy motion, and interference, which traditional rate theories, such as transition-state theory, do not consider. In this study, we investigate the feasibility of simulating reaction dynamics using a parametrically driven bosonic superconducting Kerr-cat device. This approach provides control over parameters defining the double-well free energy profile, as well as external factors like temperature and the coupling strength between the reaction coordinate and the thermal bath of nonreactive degrees of freedom. We demonstrate the effectiveness of this protocol by showing that the dynamics of proton-transfer reactions in prototypical benchmark model systems, such as hydrogen-bonded dimers of malonaldehyde and DNA base pairs, could be accurately simulated on the currently accessible Kerr-cat devices.

Molecular interactions are key to regulating cellular processes by transferring information between distant sites, promoting protein complex formation or enzyme activation. Understanding how biomolecules orchestrate this information transfer is critical to gaining insight into biochemical processes, modulating them through bioengineering, or even controlling reactions through small-molecule activators. In the past decade, network theory has been applied to identify molecular features and allosteric hubs essential for establishing information transfer in biomolecules. Molecular dynamics simulations often are the source of time-correlated data that is used to construct protein networks, which can be rationalized in terms of different correlation metrics. The specific molecular features and correlation metrics to use for reliably describing the coupled motions underlying allostery are often overlooked concepts. In this work, we provide a Python software package for performing network analysis on the molecular dynamics of biological systems. MDigest has a modular structure that allows for rapid evaluation and straightforward comparison of residue couplings obtained from different molecular features and various correlation metrics. In addition to commonly used features such as generalized correlation based on atomic displacements or covariance of dihedral angle fluctuations, we include a new network approach that relies on the correlation of electrostatic couplings over time, resulting in a coordinate-independent and physically relevant representation of the allosteric network. Besides allowing for efficient comparison of different networks at custom distance thresholding, our implementation allows the user to compare and contrast the underlying community structures and determine optimal paths for the most informative graph. Our enhanced and extended protocol allows for rigorous and comprehensive analysis of MD trajectories from small to large macromolecular complexes.