• o.kyriienko@sheffield.ac.uk

 

  • Quantum Dynamics

    The evolution of interactive quantum systems is notoriously difficult to predict. This is due to exponentially increasing space of states and the complexity of matrix exponentiation. We propose quantum simulation methods where dynamics is generated by engineered Hamiltonians in various quantum systems. These include analogue and digital quantum simulators with superconducting circuits, strongly coupled semiconductor materials, molecules and proteins, Rydberg atoms, etc. The proposed simulators help understanding fundamental questions (for instance, time crystallisation) and learn properties of simulated models relevant from the industrial point of view (Fermi-Hubbard simulators).

  • Quantum Optics

    Quantum theory has begun with the discovery of photons as quanta of light. Optical systems represent an excellent platform for quantum technologies, thanks to fast room-temperature operation. However, as photons are non-interacting particles, many quantum optical applications require introducing effective nonlinearity for photons. We study systems where light couples strongly to excitations in various materials (semiconductor quantum wells, atomically-thin monolayers, organics, etc.) Specifically, we develop the area of polaritonics, where hybrid light-matter quasiparticles – exciton-polaritons – enable strong nonlinear optical effects in high-quality systems.

  • Quantum Computing

    Classical computers operate information encoded in the binary form, and process it using semiconductor microchips with billions of transistors. While classical computers are successful in solving various computational tasks, certain problems require exponential increase of their runtime with the system size, making some calculations very long or even impossible. Quantum computers can offer the solution by encoding infromation in quantum states and processing it by well-controlled quantum devices. We develop algorithms for quantum computers that are currently being built and operate in the presence of noise. Our goal is to offer efficient quantum software and novel use-cases.

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Solving fluid dynamics with differential quantum circuits

Nonlinear differential equations (DEs) are used in all branches of science, including chemistry, biology and fluid dynamics. In many cases DEs are difficult to solve for systems with numerical instabilities and multidimensional variables. This limits the computational capabilities of classical methods for solving nonlinear DEs. As a part of collaboration with PASQAL (and Qu&Co team who merged with PASQAL in 2022), we developed the quantum algorithm that uses differentiable quantum circuits to represent solutions of nonlinear differential equations. The approach can be seen as quantum version of physics-informed machine learning for solving DEs in large computation space provided by quantum encodings. Dubbed as the DQC solver, it is designed to be compatible with the near-term quantum hardware and operate in the presence of noise. We applied the system to the problem in fluid dynamics and solved an instance of Navier-Stokes equations. Specifically, we accurately predicted the airflow in a nozzle of a prototype rocket turbine.

Read more in the Research and Innovation blog, and see the portfolio of algorithms by PASQAL.

 

Meet quantum theory researchers from

Our team

  • We collaborate with industries to develop useful quantum algorithms. Do you know an important computational problem that is difficult to solve? Are you running HPC simulations and see that runtimes have unfavourable scaling with the system size? We are always interested in solving challenging problems and improving state-of-the-art in scientific computing and machine learning.
    Oleksandr Kyriienko
    Group Leader