householder-reflections

QR decomposition, or QR factorization, is a fundamental linear algebra method that decomposes a matrix into a product of an orthogonal matrix and an upper triangular matrix. It is widely used for solving linear least squares problems, computing eigenvalues, Gram-Schmidt, Householder reflections, or Givens rotations.Solver

Saryu: a recurrent language model whose state is moved by input-dependent Householder reflections. Open architecture research from India — constant memory per token, an exact parallel kernel, and two technical reports: the architecture, and what the same transport does when it fails (it collapses onto exact group quotients).

🧮Implementations of numerical linear algebra algorithms including Cholesky, LU Decomposition, ESOR, PSD check, and QR using Householder reflections, as part of the Linear Numerical Algebra ΘΠ03 course.

Dynamic Oracle Synthesis and Amplitude Amplification for unstructured 3-bit quantum pattern search with Qiskit. Implements bit-conditional Pauli-X conjugation around CCZ, 2D invariant subspace rotation dynamics, optimal stopping at R = 2 Grover iterations (94.53% target fidelity), and empirical validation via AerSimulator across all 8 basis states.