Solve your data tasks even faster and more precisely

Quantum Computing as AI Booster

Achieve an enormous speed advantage

Quantum computers can solve so-called BQP problems (bounded-error, quantum, polynomial time) in polynomial time. This includes many problems for which verification by a classical computer would be slower than solving with a quantum computer. Pharmaceuticals, chemistry, medicine would all benefit from molecular simulations at the quantum mechanics level (high impact).

Four main capabilities of quantum computing (QC):

  • Prediction of dynamic systems
  • Finding solutions for optimisation problems
  • Simulation
  • Encryption

Essential quantum algorithms

Grover

  • Quick search of large databases
  • Search for target objects with specific properties
  • Simultaneous evaluation of all objects by superposition

Harrow-Hassidim-Lloyd (HHL)

  • Fast solving of large systems of equations
  • Solving optimisation problems
  • Computing time logarithmic (exponential advantage)

Adiabatischer QC (AQC)

  • Fast solving of complex optimisation problems
  • Discrete optimisation problems
  • Goal: energetically favourable state
Single route planning versus Route planning in context

Possible areas of application

Sector

Application

Logistics and mobility
  • Optimisation of transport routes and schedules
  • Delivery and storage cost reduction
  • Delivery time minimisation
  • Enhanced monitoring through tracking
  • Automation of manual processes and document processing
  • Improvement of planning
  • Increased customer satisfaction

Pharma and industry
  • Bioinformatics, medical research

Finance
  • Risk analysis portfolio optimisation

Engineering
  • Flow simulations, material sciences

IT security
  • encryption technologies

What are Quantum projects like?

Dynamic systems, Optimisation, Simulation
  1. Identification of a (mathematical) problem and associated use cases in a workshop.

  2. Conversion of the problem into algorithms / parameter sets that can be simulated or calculated on a quantum computer.

  3. Solution optimisation with the help of the quantum computer and, if necessary, manual adjustment of the parameters

  4. Productive deployment of the solution

  5. Documentation and ensuring the transfer of knowledge

Status quo & outlook

The goal is to exploit the Quantum Advantage, i.e. the ability to use quantum computing to solve tasks faster and more accurately than the best classical algorithm on the fastest classical supercomputers.

  • We are currently still in an early phase
  • Very large and complex data volumes can already be processed with QC
  • Algorithms should be chosen carefully
  • Developer frameworks, such as Amazon Braket Python SDK or Qiskit open-source SDKs
  • Integration of these frameworks in cloud architectures