• Skip navigation
  • Skip to navigation
  • Skip to the bottom
Simulate organization breadcrumb open Simulate organization breadcrumb close
Friedrich-Alexander-Universität Chair for Quantum Theory
  • FAUTo the central FAU website
  1. Friedrich-Alexander-Universität
  2. Naturwissenschaftliche Fakultät
  3. Department Physik
Suche öffnen
  • Campo
  • StudOn
  • FAUdir
  • Jobs
  • Map
  • Help
  1. Friedrich-Alexander-Universität
  2. Naturwissenschaftliche Fakultät
  3. Department Physik
Friedrich-Alexander-Universität Chair for Quantum Theory
Navigation Navigation close
  • Home
  • Research
    • Quantum Algorithms for near term Hardware
    • Superconducting Circuits
    • Simulation Methods for Quantum Many-Body Systems
    Portal Research
  • People
    • Current Members
    • Junior Research Group Palffy-Buß
    • Vacancies
    • Alumni
    Portal People
  • Teaching
  • Publications
    • Hartmann Group
    • Palffy-Buß Group
    • Reinhard Group
    Portal Publications
  • Seminars
    • Group Seminar
    • Journal Club
    Portal Seminars
  • Intranet

Chair for Quantum Theory

In page navigation: Research
  • Quantum Algorithms for near term Hardware
  • Simulation Methods for Quantum Many-Body Systems
  • Superconducting Circuits

Quantum Algorithms for near term Hardware

The quest of fault tolerant quantum computing is estimated to lie further in the future. Nevertheless recent work has already shown that some computations that are classically not feasible can be executed  on noisy intermediate-scale quantum (NISQ) hardware, hence introducing the vision of near term quantum computing. This created both, need and opportunity to develop quantum algorithms for NISQ devices that outperform their potential classical counterpart, yet mitigating errors. Challenges with these include non-negligible read-out, single qubit and 2-qubit gate errors as well as limited coherence time, which restrict applicable quantum circuits to several 10s to few 100s of gates in depth. Consequently, current quantum algorithms need to include some kind of error mitigation.

In particular, variational algorithms are intensively studied as hardware efficient solutions for a still rather restricted class of problems. These consist of Quantum Chemistry tasks as well as hardware efficient Hamiltonian simulations that also can encode efficiently combinatorial optimisation such as the Max-3-Cut or travelling salesman problem.

In our research, we explore applications to NISQ hardware of algorithms for time evolution, ground state preparation and combinatorial optimization amongst others. We investigate how to mitigate errors and reduce noise therein and how to exploit symmetries to construct larger quantum circuits.

Furthermore, we investigate quantum neural networks (QNN) which are the analogous to classical neural networks, but are based on quantum circuits. We explore how a quantum perceptron could be implemented using superconducting quantum hardware. We also study various architectures of QNNs such as quantum convolutional neural networks.

FAU Erlangen-Nürnberg
Lehrstuhl Theoretische Physik II

Staudtstr. 7
91058 Erlangen
  • Legal notice
  • Privacy
  • Accessibility
  • Facebook
  • RSS Feed
  • Twitter
  • Xing
Up