The pipeline

From theory, through implementation, to real-world impact

Quantum technology promises a speedup over classical computers in a wide range of applications.

But, realising and utilizing this advantage requires breaking barriers in development, optimization, benchmarking and targeting of quantum algorithms

Quantum machine learning and artificial intelligence

Artificial intelligence applications are rapidly becoming some of the main consumers of our computational resources. Fortunately, quantum computing may provide substantial boost in large data analysis, AI planning and reasoning problems alike. Our research in aQa is providing new ways to utilize quantum resources for such complex AI problems. For more information, read here, here, or here.

Quantum chemistry and quantum simulation

The molecules that make up the world around us follow the laws of quantum mechanics. This makes them difficult to simulate and understand on a classical computer. At aQa, we are designing and optimizing quantum algorithms to target quantum chemistry, and finding new targets for a future quantum computer. You can read more here, here, and here.

Hybrid quantum-classical computing

Quantum computers work best when leveraging classical machines to perform all but the most challenging tasks. This is of critical importance in the near-term, where short coherence times and limited numbers of qubits prohibit long quantum circuits from being run. Read more here, here, and here.

Near-term implementations and benchmarking

To make the transition from paper to reality, quantum algorithms need demonstration on prototype quantum devices, and benchmarking to probe the scalability of any approach. At aQa, we use state-of-the-art experimental technology in collaborating research groups to test the quantum algorithms of tomorrow. See our results in more detail here, here, and here.

Quantum error correction and error mitigation

Quantum states are incredibly fragile, and quantum computers need large amounts of error correction and error mitigation to reach the coherence requirements for speedups over their classical counterparts. At aQa, we study efficient QEC implementations, exploiting symmetries of quantum systems, and utilizing AI methods for more efficient codes and decoders. Read more here, here, here, and here.



aQa is active in pursuing a number of academic and industry-involved collaborations. Here is a listing of our most recent funded projects


3-year European FET-Open project dedicated to coherently link different individual superconducting quantum computing devices in a resource-efficient and modular way.


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4 year H2020 Europe-wide project in the scope of the Quantum Flagship involving 11 participants from academia and industry, dedicated to achieving quantum advantages in concrete industrially-relevant case studies.


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Joint project between SURFSara, aQa and the SAILS Leiden initiative, developing everything from software infrastructure to new quantum algorithms, which help us exploring the potential of quantum computing for quantum chemistry problems.


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A project dedicated to the development of hybrid divide and conquer methods for machine learning on near-term quantum computers, supported by the Google through an unrestricted gift.


For more information on the project contact Vedran Dunjko.

Applied Quantum Algorithms 

A Leiden University collaboration