Quantum Computing for Natural Sciences with IBM Quantum (openHPI)

A course on quantum simulation applied to chemistry and materials science, developed by the Hasso Plattner Institute with IBM Quantum. Free and self-paced on openHPI, the course addresses one of the most compelling near-term applications for quantum computers: simulating quantum systems that are intractable for classical methods.

Quantum chemistry is widely considered the most promising early application area for quantum computing. This course gives you the conceptual framework and practical Qiskit tools to understand and experiment with quantum simulation of molecular systems.

What you’ll learn

• Why quantum computers for chemistry: the exponential classical cost of simulating quantum systems, Richard Feynman’s original proposal for quantum simulation, and which problems in chemistry and materials science are most likely to benefit
• The second quantization: fermionic and bosonic operators, the Hamiltonian in second quantized form, and why this representation is needed for describing electrons in molecules
• Molecular Hamiltonians: the Born-Oppenheimer approximation, the electronic Hamiltonian, and how molecular structure determines the Hamiltonian
• Mapping to qubits: the Jordan-Wigner and Bravyi-Kitaev transformations that convert fermionic Hamiltonians to qubit Hamiltonians that quantum circuits can process
• The Variational Quantum Eigensolver (VQE) for chemistry: the unitary coupled cluster (UCCSD) ansatz, how VQE finds the ground state energy of a molecule, and the classical-quantum hybrid optimization loop
• Qiskit Nature: IBM’s open-source framework for quantum chemistry simulations, including tools for defining molecular systems, constructing VQE experiments, and interpreting results
• Practical examples: computing the ground state energy of hydrogen (H2) and lithium hydride (LiH) molecules using VQE on simulators

Course structure

The course progresses from the motivation for quantum simulation through the necessary chemistry and physics background, then builds up to the full VQE for chemistry workflow using Qiskit Nature. Programming assignments accompany each topic, with a Record of Achievement available upon completion.

Who is this for?

• Chemists, biologists, and materials scientists curious about what quantum computing might eventually offer their field
• Quantum computing students looking for a scientifically grounded application domain
• Developers interested in quantum simulation who want to go beyond abstract circuit examples to real molecular problems
• Anyone interested in the path from current NISQ devices to fault-tolerant quantum chemistry simulations

Prerequisites

Quantum computing basics and familiarity with Qiskit are recommended. Basic chemistry knowledge (atoms, molecules, electrons) is helpful. The course introduces the necessary physics from scratch, but some comfort with mathematical notation is needed. Python programming experience is required for the coding exercises.

Hands-on practice

Coding exercises use Qiskit and Qiskit Nature:

• Define a molecular system (H2) in Qiskit Nature and inspect the resulting Hamiltonian
• Apply the Jordan-Wigner transformation and verify the qubit Hamiltonian
• Construct a UCCSD ansatz circuit and observe how it encodes electron correlations
• Run VQE for H2 and compare the result to the exact classical ground state energy
• Repeat for a larger molecule (LiH) and observe the increased circuit complexity
• Explore how bond length variation changes the ground state energy landscape

Why take this course?

Drug discovery, materials design, and catalysis research all involve quantum systems that defeat classical simulation at industrially relevant scales. Quantum simulation is the application area where quantum advantage is most theoretically certain, and VQE is the leading near-term approach. This course teaches the full stack from first principles to working code, giving you the foundation to follow the field as hardware improves.

The IBM Quantum collaboration means the Qiskit Nature implementations are accurate and maintained. The course has been rated 4.23 stars by over 250 learners, reflecting both the quality of the content and the genuine demand for this material.