Quantum Computing at Fraunhofer IAF

• Quantum simulation: 
  • Quantum chemical calculations and simulations
  • Research into material and natural sciences
• Combinatorial optimization: 
  • Static and dynamic portfolio optimization on NISQ systems
  • Combination of classical and quantum calculations using QAOA
• Quantum machine learning: 
  • AI-supported quantum systems
  • Quantum computing for AI
  • Use cases in generative learning and classifications

• are hybrid-integrable
• function near room temperature; bulky vacuum setups or cryogenic cooling are not required
• offer longer operation times and smaller error rates than comparable, commercially available systems based on the state of the art

Software hardware co-design

In addition to developing and optimizing algorithms for different hardware platforms, Fraunhofer IAF also performs benchmarking.

Goals

• Successful calculations on Noisy Intermediate Scale Quantum (NISQ) systems despite the inherent error-proneness
• Evaluate hardware approaches for different applications

Methods

• Fault characterization: Crosstalk, improved fault models
• Fault mitigation: Inverted Circuits - Zero Noise Extrapolation (IC-ZNE), Grover
• Benchmarking of superconducting systems from IBM and diamond-based systems

Process development for Quantum Computing

At Fraunhofer IAF, semiconductor processes for quantum technology components are being developed, such as photonic diamond components and the generation of nitrogen vacancy centers for spin-based quantum computing.

Goals

• Production of photonic diamond components
• Local generation of color centers

Methods

• Local generation of color centers in CVD diamond
• Production of free-standing optical structures by dry etching
• Lithography (optical, electron beam)
• Metallization