Quantum Computing for Natural Sciences with IBM Quantum (openHPI)

A course on quantum simulation applied to chemistry and materials science, taught by IBM Quantum researchers on the Hasso Plattner Institute’s openHPI platform. Free and self-paced, 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 Schrödinger equation, the electronic structure problem, and how molecular structure determines the Hamiltonian
• Mapping to qubits: transforming second quantized Hamiltonians into qubit Hamiltonians that quantum circuits can process
• The Variational Quantum Eigensolver (VQE) for chemistry: how VQE finds the ground state energy of a molecule through a classical-quantum hybrid optimization loop, and embedding routines for treating larger molecular systems
• Qiskit Nature: IBM’s open-source framework for quantum chemistry simulations, including tools for defining molecular systems, constructing VQE experiments, and interpreting results
• Beyond ground states: quantum algorithms for excited states and time-dependent properties, efficient measurement of expectation values, and methods for working around device noise

Course structure

Week 1 progresses from the motivation for quantum simulation through the second quantization and qubit mappings to the VQE workflow for molecular ground states, closing with embedding routines for larger systems. Week 2 covers excited states and time-dependent properties, efficient measurement of expectation values, and methods for working around device noise. The course is currently in self-paced mode, where graded assignments are not available; learners can earn a Confirmation of Participation by completing at least half of the course material.

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 small molecular system in Qiskit Nature and inspect the resulting Hamiltonian
• Transform a second quantized Hamiltonian into its qubit representation
• Construct a variational ansatz circuit and run VQE to estimate a molecular ground state energy on a simulator
• Experiment with measuring expectation values and handling device noise

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.