Machine Learning in Quantum Science and Computing

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Related lectures (31)

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Quantum Computational Advantage: Recent Progress and Next Steps

Explores recent progress in quantum computational advantage and discusses the challenges in achieving quantum supremacy.

Quantum Computing: Introduction

Covers quantum computing basics, quantum algorithms, error correction, and quantum bit manipulation.

Quantum Computing Basics

Covers the basics of quantum computing, focusing on superconducting qubits, scaling challenges, and potential applications.

Quantum Principles

Covers the fundamental principles of quantum computing and unitary evolution in quantum systems.

Trapped Ions Quantum Computing

Explores analog and digital quantum computing, quantum jumps, cooling of atoms with light, quantum computer technologies, and trapped ion quantum bits.

Cryo-CMOS electrical interfaces for large-scale quantum computers

Delves into Cryo-CMOS technology for quantum computers, focusing on challenges and advancements in cryogenic CMOS circuits.

Quantum Computation: Mechanisms and Advantages of Restricted Models

Covers the mechanisms behind quantum computation and explores the advantages of restricted computational models.

Quantum Computing: Hardware Technologies and Applications

Explores quantum computing hardware technologies, applications, classical computing development, investments, and societal impact.

The Deutsch and Deutsch-Jozsa Algorithms

Covers the Deutsch and Deutsch-Jozsa algorithms in quantum computing, explaining the principles behind them and their implications.

Stochastic numerical methods for many-body quantum systems

Explores stochastic methods for quantum systems, including exact diagonalization, variational methods, neural networks, and machine learning.