Explore cutting-edge quantum algorithms for numerical linear algebra through this comprehensive workshop featuring leading experts in the field. Discover recent developments in quantum computing applications for solving linear systems, eigenvalue decomposition, singular value decomposition, and matrix function evaluation. Learn about efficient quantum algorithms for dissipative nonlinear differential equations, time-dependent Hamiltonian simulation, and quantum information processing tasks. Examine approaches to quantum computing with cold atoms, fast algorithms for quantum signal processing, and new results in quantum linear algebra. Delve into dequantization concepts, quantum advantage in machine learning, quantum polar decomposition, and improved complexity estimation for Hamiltonian simulation. Understand optimal scaling quantum linear systems solvers, arithmetic tensor networks, and classical versus quantum algorithms for estimating traces and partition functions. Investigate problem-tailored variational quantum algorithms, Heisenberg-limited ground state energy estimation, and early fault-tolerant quantum computing applications. Study classical and quantum algorithms for isogeny problems, efficient quantum approaches to nonlinear ODEs and PDEs, and variational quantum architectures for linear algebra. Analyze the Quantum Approximate Optimization Algorithm (QAOA), practical quantum circuits for block encodings of sparse matrices, and training quantum neural networks with unbounded loss functions, all presented by researchers from leading institutions addressing both near-term quantum devices and future fault-tolerant quantum architectures.

Syllabus

Andrew Childs - Efficient quantum algorithm for dissipative nonlinear differential equations
Di Fang - Time-dependent Hamiltonian Simulation of Highly Oscillatory Dynamics - IPAM at UCLA
Andras Gilyen - Quantum Algorithms for Quantum Information Processing Tasks - IPAM at UCLA
Dan Stamper-Kurn - Approaches to quantum information processing with cold atoms - IPAM at UCLA
Yulong Dong - Fast algorithms for quantum signal processing - IPAM at UCLA
Rolando Somma - The Quantum Linear Systems Problem - IPAM at UCLA
Iordanis Kerenidis - New results in quantum linear algebra - IPAM at UCLA
Jarrod McClean - Dequantization and quantum advantage in learning from experiments - IPAM at UCLA
Ewin Tang - On quantum linear algebra for machine learning - IPAM at UCLA
Seth Lloyd - Quantum polar decomposition - IPAM at UCLA
Dong An - Improved complexity estimation for Hamiltonian simulation with Trotter formula
Dominic Berry - Optimal scaling quantum linear systems solver via discrete adiabatic theorem
Garnet Chan - Arithmetic tensor networks and integration - IPAM at UCLA
Anirban Chowdhury - Classical and quantum algorithms for estimating traces and partition functions
Sophia Economou - Problem-tailored variational quantum algorithms - IPAM at UCLA
Yu Tong - Heisenberg-limited ground state energy estimation & early fault-tolerant quantum computers
Kirsten Eisentraeger - Classical and quantum algorithms for isogeny problems - IPAM at UCLA
Jin-Peng Liu - Efficient quantum algorithms for nonlinear ODEs and PDEs - IPAM at UCLA
Carlos Bravo-Prieto - Variational quantum architectures for linear algebra applications
Alexandra Kolla - Quantum Approximate Optimization Algorithm (QAOA) and Local Max-Cut - IPAM at UCLA
Chao Yang - Practical Quantum Circuits for Block Encodings of Sparse Matrices - IPAM at UCLA
Maria Kieferova - Training quantum neural networks with an unbounded loss function - IPAM at UCLA