4 citations · 9 across the 5 of their papers we have counts for
5 papers
ChipMEM: Verification-Grounded Memory for EDA Agents
Abdulrahman AlRabah, Joshua Mabry, Dilek Hakkani-Tür +4
Large language model (LLM)-based agents use Electronic Design Automation (EDA) tools to generate and revise register-transfer-level (RTL) designs under synthesis and verification f…
DUET: Agentic Design Understanding via Experimentation and Testing
Gus Henry Smith, Sandesh Adhikary, Vineet Thumuluri +5
AI agents powered by large language models (LLMs) are being used to solve increasingly complex software engineering challenges, but struggle with hardware design tasks. Register Tr…
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Siddarth Srinivasan, Sandesh Adhikary, Jacob Miller +2
Modeling joint probability distributions over sequences has been studied from many perspectives. The physics community developed matrix product states, a tensor-train decomposition…
Expressiveness and Learning of Hidden Quantum Markov Models
Sandesh Adhikary, Siddarth Srinivasan, Geoff Gordon +1
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in the development of hidden quantum Markov m…
Learning Quantum Graphical Models using Constrained Gradient Descent on the Stiefel Manifold
Sandesh Adhikary, Siddarth Srinivasan, Byron Boots
Quantum graphical models (QGMs) extend the classical framework for reasoning about uncertainty by incorporating the quantum mechanical view of probability. Prior work on QGMs has f…