Discovering Quantum Error Correcting Codes with Reinforcement Learning
Explore reinforcement learning techniques for discovering quantum error correcting codes, enhancing quantum computing reliability and performance.
Numerical Analysis for Hamiltonian Simulation and Hamiltonian Learning
Explore numerical analysis for Hamiltonian simulation and learning, covering accuracy measures, semiclassical Schrödinger equation, and efficient algorithms for learning interacting N-qubit local Hamiltonians.
Hybrid Variational Algorithms for Classical and Quantum Complex Systems
Exploring hybrid classical-quantum variational algorithms for complex systems, focusing on QAOA applications in NISQ era. Discusses implementations on classical emulators and Rydberg atom quantum machines.
Introduction to Quantum Information Science - Part II
Explore advanced quantum information science concepts with UCLA expert Wes Campbell in this comprehensive lecture, part of IPAM's Winter School on Quantum Information Science for Chemistry.
Introduction to Quantum Information Science - Part I
Comprehensive introduction to quantum information science, covering fundamental concepts and their applications in chemistry, presented by expert Wes Campbell at UCLA's IPAM Winter School.
Designing an Architecture for Physics-Informed Machine Learning - Part 3
Explore various neural network architectures for physics-informed machine learning, including ResNets, UNets, and PINNs. Learn how to incorporate physical principles and symmetries into model design for improved performance.
Quantum Simulation of Condensed Matter Using Trotterized Entanglement Renormalization
Explore quantum simulation of strongly-correlated matter using MERA-based variational quantum eigensolver. Learn about implementation on NISQ devices, gradient-based optimization, and experimental tests on ion-trap devices.
Tensor Networks and the Negative Sign Problem
Explore tensor networks and the negative sign problem in quantum systems, examining simulation challenges and correlation implications for systems with negative Hamiltonian entries.
Physics-Informed Machine Learning: Curating Training Data - Part 2
Explore physics-informed data curation for machine learning, covering data augmentation, coordinate systems, simulation vs. experiments, and strategies for handling expensive, biased, and rare data in AI/ML applications.
Optical Cycling Centers in Quantum Information Science - Part 2
Explore advanced concepts in optical cycling centers with Nick Hutzler, focusing on applications in quantum information science for chemistry.
Optical Cycling Centers in Quantum Information Science - Part I
Explore optical cycling centers in quantum information science for chemistry with Nick Hutzler's comprehensive lecture at IPAM's Winter School.
Training and Optimisation of Large Transformer Models - An ATLAS and CERN Use Case
Explore CERN's implementation of transformer models for Jet Flavour Tagging, utilizing Kubeflow for machine learning and Katib for hyperparameter optimization in large-scale particle physics data analysis.
Expansion of Higher-Dimensional Cubical Complexes with Application to Good Quantum Locally Testable Codes
Explore quantum locally testable codes, their optimal parameters, and connections to expander codes and qLDPC codes in this advanced talk on quantum complexity theory.
Two Quantum Tales - Exploring Quantum Computing Concepts
Explore quantum computing through personal anecdotes and scientific insights, honoring Michael Ben-Or's contributions to the field.
Quantum Information Theory and Computation
Explore quantum computing insights with Charles H. Bennett of IBM Research, delving into cutting-edge concepts and their implications for future technological advancements.
Michael Ben-Or's Contributions to Error Correction in Quantum Computing
Explore quantum error correction and its impact on quantum computing with insights from Michael Ben-Or's groundbreaking work in the field.
Holographic Complexity in Quantum Information Theory
Explore holographic complexity with Umesh Vazirani, delving into advanced concepts at the intersection of quantum information and theoretical physics.
Quantum Computational Advantage With Constant-Temperature Gibbs Sampling
Explore quantum computational advantage through constant-temperature Gibbs sampling, examining thermalization processes and their implications for quantum systems and classical algorithms.
Oracle Separations Between QMA and QCMA
Explores recent non-standard oracle separations between QMA and QCMA, discussing connections to sparse Hamiltonians and potential paths towards standard oracle separation.
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Explores SINDy-RL, a framework combining sparse identification of nonlinear dynamics with reinforcement learning for efficient, interpretable control policies in complex environments.
Symmetries in Machine Learning for Materials Science
Exploring machine learning techniques incorporating crystallographic symmetries for materials science, enhancing property prediction and discovery of new materials and meta-materials.