48 research outputs found
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
-triviality of low dimensional manifolds
A space is -trivial if for every real vector bundle over
the total Stiefel-Whitney class . It follows from a result of
Milnor that if is an orientable closed smooth manifold of dimension
or , then is not -trivial. In this note we completely characterize
-trivial orientable closed smooth manifolds in dimensions and . In
dimension , we describe necessary conditions for an orientable closed smooth
-manifold to be -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