Quantum Computing Fundamentals & Qiskit is a cutting-edge, beginner-to-intermediate course designed to demystify quantum computing and equip learners with the skills to build, simulate, and run quantum algorithms using IBM’s open-source Qiskit framework. The course begins with an intuitive explanation of the principles that differentiate quantum computing from classical computing, focusing on core concepts such as qubits, superposition, entanglement, interference, and quantum measurement. Learners study how qubits, unlike classical bits, can exist in multiple states simultaneously, enabling exponential computation power for certain problems. The course explains quantum gates and circuits, including Pauli-X, Y, Z, Hadamard, Phase, CNOT, and Toffoli gates, and how they manipulate qubit states through unitary transformations. Using Qiskit, students simulate circuits in Python and visualize their outputs using Bloch spheres, state vectors, and probability histograms. The course covers quantum circuit design principles, including the creation, composition, optimization, and measurement of quantum circuits using Qiskit’s Terra and Aer modules. Hands-on labs teach learners how to write code that builds, runs, and tests circuits on both simulators and real quantum hardware via IBM Quantum Experience. Learners implement foundational quantum algorithms such as the Deutsch-Jozsa algorithm, Grover’s search, and Shor’s factoring algorithm, while understanding their speedups over classical counterparts. Quantum teleportation, entangled states, and Bell inequality experiments are also demonstrated, offering insight into the truly non-intuitive aspects of quantum information. The course introduces the basics of quantum error correction, noise models, and the importance of coherence time and gate fidelity in near-term quantum devices. Learners also explore the emerging area of hybrid quantum-classical algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), which are crucial for solving chemistry, optimization, and machine learning problems on noisy intermediate-scale quantum (NISQ) devices. The Qiskit Machine Learning, Finance, and Nature modules are used to demonstrate real-world applications, from portfolio optimization to molecule simulation. Quantum development environments and IDEs, including Qiskit Notebooks and IBM’s Qiskit Runtime on cloud platforms, are introduced to streamline experimentation. The course also emphasizes industry trends and the road ahead, discussing quantum supremacy, error-corrected qubits, superconducting circuits, trapped ion qubits, and photonic quantum computing. Ethical considerations and cybersecurity implications of post-quantum cryptography are also addressed. Learners analyze the strengths and current limitations of quantum hardware, interpret circuit results statistically, and understand how to structure problems to gain a quantum advantage. By the end of the course, students will have built a foundational understanding of quantum theory, practiced with real quantum circuits, and gained experience with the Qiskit SDK to prototype algorithms and conduct simulations. This course is ideal for computer scientists, physicists, software developers, data scientists, and technology strategists eager to enter the frontier of quantum computing and position themselves at the leading edge of next-generation computing paradigms.