56 citations · 113 across the 8 of their papers we have counts for
10 papers
Distribution of Quantum Circuits Over General Quantum Networks
Ranjani G Sundaram, Himanshu Gupta, C. R. Ramakrishnan
Near-term quantum computers can hold only a small number of qubits. One way to facilitate large-scale quantum computations is through a distributed network of quantum computers. In…
Pre-Distribution of Entanglements in Quantum Networks
Mohammad Ghaderibaneh, Himanshu Gupta, C. R. Ramakrishnan +1
Quantum network communication is challenging, as the No-Cloning theorem in quantum regime makes many classical techniques inapplicable. For long-distance communication, the only vi…
Efficient Quantum Network Communication using Optimized Entanglement-Swapping Trees
Mohammad Ghaderibaneh, Caitao Zhan, Himanshu Gupta +1
Quantum network communication is challenging, as the No-cloning theorem in quantum regime makes many classical techniques inapplicable. For long-distance communication, the only vi…
Value of Information in Probabilistic Logic Programs
Sarthak Ghosh, C. R. Ramakrishnan
In medical decision making, we have to choose among several expensive diagnostic tests such that the certainty about a patient's health is maximized while remaining within the boun…
Constraint-Based Inference in Probabilistic Logic Programs
Arun Nampally, Timothy Zhang, C. R. Ramakrishnan
Probabilistic Logic Programs (PLPs) generalize traditional logic programs and allow the encoding of models combining logical structure and uncertainty. In PLP, inference is perform…
Inference in Probabilistic Logic Programs using Lifted Explanations
Arun Nampally, C. R. Ramakrishnan
In this paper, we consider the problem of lifted inference in the context of Prism-like probabilistic logic programming languages. Traditional inference in such languages involves…
XPL: An extended probabilistic logic for probabilistic transition systems
Andrey Gorlin, C. R. Ramakrishnan
Generalized Probabilistic Logic (GPL) is a temporal logic, based on the modal mu-calculus, for specifying properties of reactive probabilistic systems. We explore XPL, an extension…
Adaptive MCMC-Based Inference in Probabilistic Logic Programs
Arun Nampally, C. R. Ramakrishnan
Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine th…
Parameter Learning in PRISM Programs with Continuous Random Variables
Muhammad Asiful Islam, C. R. Ramakrishnan, I. V. Ramakrishnan
Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's PRISM, Poole's ICL, De Raedt et al's ProbLog and Vennekens et al's LPAD, combines statistical and logical kn…
Inference in Probabilistic Logic Programs with Continuous Random Variables
Muhammad Asiful Islam, C. R. Ramakrishnan, I. V. Ramakrishnan
Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's PRISM, Poole's ICL, Raedt et al's ProbLog and Vennekens et al's LPAD, is aimed at combining statistical and…