Quantum computing represents a shift in computational logic, utilizing the principles of quantum mechanics to process information. This course establishes the necessary foundation for AI engineers to understand how quantum systems operate. The material focuses on the transition from classical bits to quantum bits (qubits), explaining the mathematical structure that governs quantum states.
You will learn linear algebra essentials required for quantum mechanics, including vectors, matrices, and tensor products. The curriculum examines the properties of superposition and entanglement, demonstrating how these phenomena allow for unique computational capabilities. Through a combination of theory and Python-based implementation, you will construct quantum circuits, apply quantum gates, and simulate measurement outcomes. This material serves as a technical primer for understanding quantum algorithms.
Prerequisites Basic Python knowledge
Level:
Quantum Fundamentals
Define qubits and distinguish their behavior from classical bits using Dirac notation.
Mathematical Foundations
Apply linear algebra concepts including complex numbers, vector spaces, and unitary matrices.
Quantum Gates
Construct quantum circuits using single-qubit and multi-qubit gates to manipulate state vectors.
Entanglement
Create and measure entangled states to demonstrate quantum correlation.
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