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
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.