Institute for Pure & Applied Mathematics (IPAM) Courses

Classical Verification of Quantum Learning - IPAM at UCLA

Explore classical verification of quantum learning, discussing interactive proofs, mixture-of-superpositions examples, and scenarios where quantum data enhances or matches classical learning capabilities.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Do Quantum Computers Have Applications in Machine Learning and Combinatorial Optimization

Explores quantum computing's potential in machine learning and optimization, discussing recent developments, comparative power, limitations, and future prospects in these fields.

• YouTube
• 51 minutes
• On-Demand
• Free Video

On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks

Explore quantum speedups for nonconvex optimization using quantum tunneling walks, offering advantages over classical algorithms for problems with high, thin barriers between local minima.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Generating Approximate Ground States of Molecules Using Quantum Machine Learning

Explore quantum machine learning for generating molecular ground states, focusing on potential energy surfaces and efficient state preparation using variational quantum circuits and neural networks.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Learning to Predict Arbitrary Quantum Processes - IPAM at UCLA

Efficient machine learning algorithm for predicting quantum processes, combining state and observable learning techniques. Demonstrates potential for fast prediction of complex quantum dynamics outputs.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Learning Beyond Stabilizer States - IPAM at UCLA

Exploring efficient learning algorithms for quantum states produced by Clifford circuits with T gates, using Bell difference sampling and property testing for stabilizer nullity.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Overview of Learning Structured Quantum States - IPAM at UCLA

Explore efficient methods for learning structured quantum states, focusing on models and classes that require fewer copies for estimation, with insights on Boolean functions and alternative learning approaches.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Should We Use Parameterized Quantum Circuits for Machine Learning?

Exploration of parameterized quantum circuits for machine learning, discussing evidence for and against their use, focusing on learning output distributions and supervised learning applications.

• YouTube
• 1 hour
• On-Demand
• Free Video

Exponential Concentration in Quantum Generative Modeling and Quantum Kernel Methods

Explore exponential concentration in quantum generative modeling and kernel methods, examining causes, consequences, and the impact of shot noise on these quantum machine learning models.

• YouTube
• 1 hour
• On-Demand
• Free Video

A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits

Unified theory explaining barren plateaus in deep parametrized quantum circuits, covering expressiveness, entanglement, locality, and noise effects on variational quantum computing schemes.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Single-Ancilla Ground State Preparation via Lindbladians - IPAM at UCLA

Explore a novel Monte Carlo-style quantum algorithm for ground state preparation using Lindblad dynamics, offering efficient simulation with a single ancilla qubit and unique fixed-point convergence.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Open Quantum Systems in Quantum Computing - IPAM at UCLA

Explore efficient methods for simulating open quantum systems, utilizing quantum channels for non-Markovian dynamics, and scaling noise in quantum circuits with expert Xiantao Li.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Lindblad Equations: Variational Analysis and Numerical Methods - IPAM at UCLA

Explore variational analysis and numerical methods for Lindblad equations in quantum algorithms, presented by Duke University's Jianfeng Lu at IPAM's workshop on scientific computation.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Quantum Markov Chain Monte Carlo Algorithm - IPAM at UCLA

Explore quantum Markov Chain Monte Carlo algorithms for sampling Gibbs states, featuring a novel continuous-time quantum Markov chain with efficient simulation and practical applications in lattice Hamiltonians.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Quantum Algorithmic Tools for Simulating Open Quantum Systems - IPAM at UCLA

Explore advanced quantum algorithmic techniques for simulating open quantum systems, including improved methods for Lindbaldian simulation and preparing purified Gibbs states with enhanced efficiency.

• YouTube
• 49 minutes
• On-Demand
• Free Video