In conventional (classical) computers, information is encoded in bits. These are numeric variables that can take just one of two values at a time: either 0 or 1. In contrast, quantum computers use quantum (i.e. atomic or subatomic) particles to store and process information, giving rise to the notion of qubits. These allow us to exploit counter-intuitive properties such as quantum superpositions and entanglement for information processing, which opens a vast scope of possibilities beyond the classical binary logic of bits.

In particular, this leads to quantum algorithms for solving important problems that provide polynomial or even exponential run-time speed-ups over their classical counterparts. Such exponential speed-ups come ultimately from the fact that the number of parameters required for a classical computer to describe an N-qubit system grows exponentially in N. To give a concrete idea, this implies that a quantum system with a mere 280 qubits (today’s classical processors have over 500 million bits) requires more parameters than the estimated number of atoms in the observable universe!

Future, full-fledged universal quantum computers will have huge implications in artificial intelligence and big-data science, heavy industry and energy production and distribution, finance, pharmaceutics and material design, agriculture, genomics, and logistics and planning, just to name a few examples. In fact, even near-term quantum processors or simulators are expected to dramatically impact important sectors such as quantum chemistry, quantum machine learning, or combinatorial optimisations.

The Quantum Algorithms group is part of a global effort to devise procedures able to solve practical problems more efficiently than with classical devices and to find ways to implement them on near-term quantum hardware or future, full-fledged universal quantum computers. This requires a close dialogue with quantum information theory and experiment, computer science, mathematics, and engineering.

We are looking for extremely well-qualified and highly-motivated researchers at the lead-scientist (Download Job Description), postdoctoral, and PhD levels. If your professional background and experience overlap with our research topics, please reach out to leandro.aolita@tii.ae to learn more.