Explore cutting-edge quantum algorithms for scientific computation through this comprehensive workshop featuring 25 expert presentations from leading researchers in quantum computing and computational science. Delve into quantum algorithms for numerical linear algebra tasks including solving large-scale linear systems, eigenvalue problems, matrix function evaluation, and trace estimation, while examining applications to high-dimensional differential equations and topological data analysis. Discover recent advances in quantum Markov Chain Monte Carlo methods, quantum walks, Hamiltonian simulation, and quantum signal processing techniques. Learn about practical implementations including fast multipole methods on quantum computers, ground state preparation via Lindbladians, and quantum algorithms for the Fermi-Hubbard model. Examine quantum approaches to machine learning, nonlinear dynamics, and open quantum systems, with discussions on quantum phase estimation optimization and modular quantum signal processing. Investigate quantum speedups for continuous sampling, optimization problems, and Monte Carlo methods, while exploring applications in chemistry, inertial fusion target design, and dynamical observables computation. Gain insights into the complexity of implementing Trotter steps, efficient block encoding quantum circuits, and time-marching strategies for differential equations in quantum computing contexts.

Syllabus

Anthony (Chi-Fang) Chen - “Quantum” Markov Chain Monte Carlo algorithm - IPAM at UCLA
Jingbo Wang - Quantum walk, efficient implementation, and potential application - IPAM at UCLA
Kianna Wan - Fast multipole method on a quantum computer - IPAM at UCLA
Yu Tong - Recent progress in Hamiltonian learning - IPAM at UCLA
Lin Lin - Single-ancilla ground state preparation via Lindbladians - IPAM at UCLA
Christian Mendl - Aspects of quantum simulation of the Fermi-Hubbard model - IPAM at UCLA
Jianfeng Lu - Lindblad Equations: Variational Analysis and Numerical Methods - IPAM at UCLA
Jin Peng Liu - Provably Efficient Quantum Algorithms for Nonlinear Dynamics and Machine Learning
Rolando Somma - Quantum algorithm for simulating coupled classical oscillators - IPAM at UCLA
Xiantao Li - Open quantum systems in quantum computing - IPAM at UCLA
Zhiyan Ding - Optimized signal for Quantum phase estimation on early fault-tolerant quantum computer
Dong An - Linear combination of Hamiltonian simulation for non-unitary dynamics - IPAM at UCLA
Lexing Ying - Q-PDO and Robust QPE - IPAM at UCLA
Zane Rossi - Modular quantum signal processing with gadgets - IPAM at UCLA
Mario Berta - Quantum state preparation without coherent arithmetic - IPAM at UCLA
Chao Yang - An Efficient Block Encoding Quantum Circuit for a Pairing Hamiltonian - IPAM at UCLA
Andras Gilyen - Quantum algorithmic tools for simulating open quantum systems - IPAM at UCLA
Konstantina Trivisa - Efficient Quantum algorithms for linear and non-linear differential equations
Di Fang - Time-marching strategy can work quantumly for differential equations - IPAM at UCLA
Robin Kothari - Mean estimation when you have the source code; or, quantum Monte Carlo methods
Ruizhe Zhang - Quantum Speedups of Continuous Sampling and Optimization Problems - IPAM at UCLA
Peter Johnson - In pursuit of the first useful quantum computations for chemistry - IPAM at UCLA
Alexander Kemper - Quantum algorithms for dynamics and dynamical observables - IPAM at UCLA
Andrew Baczewski - Quantum computation of stopping power for inertial fusion target design
Yuan Su - On the complexity of implementing Trotter steps - IPAM at UCLA