Quantum Accelerated Causal Tomography: Circuit Considerations Towards Applications

In this research we study quantum computing algorithms for accelerating causal inference. Specifically, we investigate the formulation of causal hypothesis testing presented in [\textit{Nat Commun} 10, 1472 (2019)]. The theoretical description is constructed as a scalable quantum gate-based algorithm on qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Our experiments on a simulator platform validates the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence.Comment: 9 pages, 5 figure

WW-triviality of low dimensional manifolds

A space $X$ is $W$-trivial if for every real vector bundle $\alpha$ over $X$ the total Stiefel-Whitney class $w(\alpha)=1$. It follows from a result of Milnor that if $X$ is an orientable closed smooth manifold of dimension $1,2,4$ or $8$, then $X$ is not $W$-trivial. In this note we completely characterize $W$-trivial orientable closed smooth manifolds in dimensions $3,5$ and $6$. In dimension $7$, we describe necessary conditions for an orientable closed smooth $7$-manifold to be $W$-trivial.Comment: 10 page

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.Comment: Accepted for publication in the proceedings of 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, North America (ISGT NA

Visualizing Quantum Circuit Probability -- estimating computational action for quantum program synthesis

This research applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. A tutorial-style introduction to states and various notions of the complexity of states are presented. Thereafter, the probability of states in the circuit model of computation is defined. Classical and quantum gate sets are compared to select some characteristic sets. The reachability and expressibility in a space-time-bounded setting for these gate sets are enumerated and visualized. These results are studied in terms of computational resources, universality and quantum behavior. The article suggests how applications like geometric quantum machine learning, novel quantum algorithm synthesis and quantum artificial general intelligence can benefit by studying circuit probabilities.Comment: 17 page