Institute for Pure & Applied Mathematics (IPAM) Courses

An Efficient Block Encoding Quantum Circuit for a Pairing Hamiltonian - IPAM at UCLA

Explore efficient block encoding quantum circuits for pairing Hamiltonians, discussing ancilla qubits, circuit structure, and extensions to Fermionic two-body Hamiltonians without Jordan-Wigner transformations.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

Linear Combination of Hamiltonian Simulation for Non-Unitary Dynamics - IPAM at UCLA

Explore a novel method for simulating non-unitary dynamics using linear combination of Hamiltonian simulation, offering optimal state preparation cost and applications in open quantum systems.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

Learning of Neural Networks with Quantum Computers and Learning of Quantum States with Graphical Models

Explores quantum algorithms for neural network learning and quantum state learning using graphical models, showcasing advancements in computational efficiency and sample complexity for specific quantum states.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

Signal and Noise - Learning with Random Quantum Circuits and Other Agents of Chaos

Explores quantum learning in noisy computation, discussing error mitigation limitations and the surprising benefits of non-unital noise in quantum machine learning, challenging conventional understanding.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

Training Binary Neural Networks in Quantum Superposition - IPAM at UCLA

Explore quantum hypernetworks for training binary neural networks, unifying parameter, hyperparameter, and architecture optimization in a single quantum-based approach for efficient deep learning deployment.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

How to Rethink Quantum Machine Learning - IPAM at UCLA

Explore the intersection of quantum computing and machine learning, challenging conventional approaches and examining core quantum routines from a generalization perspective.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Nathan Wiebe - Quantum Machine Learning

Explore quantum machine learning concepts and applications in this comprehensive lecture by Nathan Wiebe, delving into mathematical aspects and cutting-edge research in the field.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• 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
• Self-Paced
• Free Video