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QIP 2023 Poster Abstract: A fast renormalization-group tensor-network decoder for quantum low-density parity check codes.
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Turbulence modeling for compressible flows using discrepancy tensor-basis neural networks and extrapolation detection .
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Effective Z3 model for finite-density QCD with tensor networks
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Navier-Stokes: Tensorization of the Sod and Blow-off problems [Slides]
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Configurational Integrals Solved by Tensorization [Slides]
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Recent Improvements in CP Poisson Tensor Algorithms
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Expressive Surrogate Models via Functional Tensor Networks
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Low-Rank Tensor Decompositions with Nonlinear Constraints for Conserving Quantities of Interest in Numerical Simulation Data Modeling
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Hybrid Methods for Tensor Decompositions that Leverage Stochastic and Deterministic Optimization
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Generalized Canonical Polyadic Tensor Decompositions for Streaming Data
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Incremental update techniques for online analysis of streaming tensor data
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Modeling spatio-temporal processes in climate models via functional tensor networks
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Conserving Quantities of Interest in Low-Rank Tensor Decompositions of Numerical Simulation Data
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Low-Rank Models for Tensor Count Data Corrupted by False Zeros
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Tensor Decompositions using Stochastic and Deterministic Optimization
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SPARTAN (Scalable Probabilistic Application Reconfigurable Tensor Autonomous Network)
The technical founder of Ludwig Computing Inc has been competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Ludwig Computing Inc, supported by the U.S. Department of Energy's Advanced Manufacturing Office through the Cyclotron Road program, has investigated the advantages of probabilistic computing for real-world compute-intensive applications. This research adds to the understanding of alternative computing paradigms by exploring a unique hardware-software co-design that integrates quantum computing methods with nature-inspired problem-solving techniques. The project's focus on areas such as combinatorial optimization, graph analytics, and machine learning demonstrates the potential for significant advancements in computational efficiency and performance. By harnessing natural randomness to streamline large circuits into fewer devices, Ludwig's approach enables massive parallelism, potentially offering higher throughput, speed, and energy efficiency compared to conventional hardware solutions. This work benefits the public by paving the way for more efficient computing solutions that could address complex real-world problems while potentially reducing energy consumption in data-intensive industries.