8 papers
Efficient upsampling for tensor-network and quantum-state encoded functions
Siddhartha E. Guzman, Egor Tiunov, Leandro Aolita
Both tensor trains (TTs) and quantum states provide compressed representations of grid-structured data with potentially exponential compression power. We present a unified framewor…
Quantum-inspired space-time PDE solver and dynamic mode decomposition
Raghavendra Dheeraj Peddinti, Stefano Pisoni, Narsimha Rapaka +4
The curse of dimensionality is ubiquitous in both numerical and data-driven methods. This is particularly severe for space-time methods, which treat the combined space-time domain…
A quantum-inspired multi-level tensor-train monolithic space-time method for nonlinear PDEs
N. R. Rapaka, R. Peddinti, E. Tiunov +5
We propose a multilevel tensor-train (TT) framework for solving nonlinear partial differential equations (PDEs) in a global space-time formulation. While space-time TT solvers have…
Compression, simulation, and synthesis of turbulent flows with tensor trains
Stefano Pisoni, Raghavendra Dheeraj Peddinti, Egor Tiunov +2
Numerical simulations of turbulent fluids are paramount to real-life applications, from predicting and modeling flows to diagnostic purposes in engineering. However, they are also…
Exponential Speed-ups for Structured Goemans-Williamson relaxations via Quantum Gibbs States and Pauli Sparsity
Haomu Yuan, Daniel Stilck França, Ilia Luchnikov +3
Quadratic Unconstrained Binary Optimization (QUBO) problems are prevalent in various applications and are known to be NP-hard. The seminal work of Goemans and Williamson introduced…
Probing quantum complexity via universal saturation of stabilizer entropies
Tobias Haug, Leandro Aolita, M. S. Kim
Nonstabilizerness or `magic' is a key resource for quantum computing and a necessary condition for quantum advantage. Non-Clifford operations turn stabilizer states into resourcefu…