Applied quantum computing

Proteins are crucial for the development of new drugs and therapies. To better understand their effects, predicting their three-dimensional structure – known as protein folding – is of central importance.

The Center for Applied Quantum Computing (ZAQC) is investigating in a current project to what extent quantum computing can contribute to calculating this folding. In a recently published study, the suitability of quantum annealing was tested – a special quantum method that uses quantum-mechanical effects to search for the best solution in a very large search space.

For this purpose, various folding models proposed in the scientific literature were implemented and compared with classical algorithms. The calculations were carried out on D-Wave hardware. In addition, the team developed a new model that opens up promising perspectives for future applications.

The result: Current quantum annealing hardware is not yet capable of processing complex proteins beyond proof-of-concept – but the research approaches show clear potential for the future.

Further details on the study can be found here: Exploring Quantum Annealing for Coarse-Grained Protein Folding (Timon Scheiber, Matthias Heller and Andreas Giebel, 2025, arXiv quant-ph).

Measurement-based quantum computing (MBQC) is an alternative computational model to the more familiar quantum circuit model. While in the circuit model all computational steps are carried out sequentially using so-called quantum gates, MBQC takes a different approach: It begins with a special, highly entangled initial state. The actual computation is then performed through a series of measurements, where the outcomes determine the subsequent steps.

In a recent publication with the participation of the Center for Applied Quantum Computing (ZAQC), researchers examined how MBQC can be combined with the circuit model. The result is a hybrid simulation method:

• Part of the computations can be simulated efficiently on a classical computer.

• The other part is described as a so-called graph state with corresponding measurements.

This approach opens up new possibilities for simulating quantum algorithms in a more flexible and, in some cases, more efficient manner. In the study, the method was applied, among other things, to the simulation of the H₂O (water) molecule and compared with conventional methods.

Further details can be found in the two publications:

Mapping quantum circuits to shallow-depth measurement patterns based on graph states
(Thierry N. Kaldenbach and Matthias Heller, 2025, Quantum Sci. Technol. 10 015010)

Efficient Preparation of Resource States for Hamiltonian Simulation and Universal Quantum Computation
(Thierry N. Kaldenbach, Isaac D. Smith, Hendrik Poulsen Nautrup, Matthias Heller and Hans J. Briegel, 2025, arXiv quant-ph)

In a recently published study, the ZAQC presented a quantum-inspired approach for simulating molecules. This method uses so-called tensor networks to describe the basis functions—the mathematical building blocks used to represent electrons—more precisely.

Unlike conventional methods, where molecular orbitals are described by large combinations of atomic orbitals (the LCAO method), the new approach requires only as many basis functions as there are electrons in the system. The tensor-network-based basis functions are therefore significantly more expressive.

Tests on atoms and molecules with up to ten electrons show that the results agree very well with classical LCAO calculations. This method thus offers a promising alternative to established procedures and could pave the way for highly accurate, fully numerical, and molecule-specific basis sets in the future.

Further details can be found in the publication:
Fully numerical Hartree-Fock calculations for atoms and small molecules with quantics tensor trains
(Paul Haubenwallner and Matthias Heller, 2025, Electron. Struct. 7 025006)

QCVIS was developed at the Center for Applied Quantum Computing (ZAQC) as a web application for interactive visualization of quantum algorithms, with the aim of simplifying understanding of quantum algorithms for learners of quantum computing by representing them as quantum circuits that can be edited in the application. QCVIS currently offers four different visualizations. The quantum circuits can be executed step-by-step, with all state transitions being animated to improve the comprehensibility of what happens during state transitions. Furthermore, insights from scientific and information visualization are used, e.g., the use of a perceptually uniform color space for the color representation of the phase.

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