QuICS Special Seminar: Yanqiao Wang
Description
Title:Â Optimal Quantum Eigenvalue Transformation
Speaker:Â Â Yanqiao Wang (Tsinghua University)
Date & Time:Â Â December 7, 2026, 2:00pm
Where to Attend:Â Â ATL 3100A and Virtual Via Zoom: https://umd.zoom.us/j/96122257395?pwd=n8g1abBEJLfan4dmeErJBZAQdbMrLV.1
We discover two complementary linear-combination-of-Hermitian-matrices (LCHM) formulations to achieve a general non-normal matrix eigenvalue transformation. Vanilla LCHM contains LCHS as a special case. Wely LCHM gives an exact formulation of matrix powers and polynomials without truncation and angular quadrature error. Â LCHM formulas lead to new quantum eigenvalue transformation (QET) algorithms with optimal circuit depth, ancilla qubits, and postselection repetitions. LCHM-based QETs unify various quantum linear algebraic problems, including driven ODEs, iterative methods, resolvents, log functions, shifted fractional powers, Sign and ReLU transforms, and Faber approximation on non-circular domains. Besides optimal asymptotic scaling, Weyl LCHM with numerical-radius rescaling of weighted-shift matrices can exponentially reduce the prefactor in query complexity over prior QET methods. Reference: https://arxiv.org/abs/2607.25812
*We strongly encourage attendees to use their full name (and if possible, their UMD credentials) to join the zoom session.*
Speaker:Â Â Yanqiao Wang (Tsinghua University)
Date & Time:Â Â December 7, 2026, 2:00pm
Where to Attend:Â Â ATL 3100A and Virtual Via Zoom: https://umd.zoom.us/j/96122257395?pwd=n8g1abBEJLfan4dmeErJBZAQdbMrLV.1
We discover two complementary linear-combination-of-Hermitian-matrices (LCHM) formulations to achieve a general non-normal matrix eigenvalue transformation. Vanilla LCHM contains LCHS as a special case. Wely LCHM gives an exact formulation of matrix powers and polynomials without truncation and angular quadrature error. Â LCHM formulas lead to new quantum eigenvalue transformation (QET) algorithms with optimal circuit depth, ancilla qubits, and postselection repetitions. LCHM-based QETs unify various quantum linear algebraic problems, including driven ODEs, iterative methods, resolvents, log functions, shifted fractional powers, Sign and ReLU transforms, and Faber approximation on non-circular domains. Besides optimal asymptotic scaling, Weyl LCHM with numerical-radius rescaling of weighted-shift matrices can exponentially reduce the prefactor in query complexity over prior QET methods. Reference: https://arxiv.org/abs/2607.25812
*We strongly encourage attendees to use their full name (and if possible, their UMD credentials) to join the zoom session.*