Machine Learning

Quantum Machine Learning

Quantum machine learning is leveraging quantum mechanics to perform calculations in light speed and with algorithms much faster than with classical machine learning algorithms based on classical gates (0 and 1). The main concepts of quantum mechanics are 

  1. Qubits, which can represent anything in between 1 and 0 
  2. Superposition, which enables parallel computing of several possible paths and
  3. Entanglement, were particles in one state affect the state of another particle 

Quantum Computing hardware is available as a service in the cloud from most hyperscalers, operating systems and programming languages are available from large tech companies and quantum algorithms are available as open source libraries. Quantum machine learning implementation is not light years away, it is possible today and computational capabilities are scaling exponentially with the amount of Qubits.


Quantum Computing Capabilities

Quantum machine learning covers several AI capabilities where the quantum algorithms outperforms the classical machine learning algorithms. The main capabilities are:

1. Unstructured search and finding unknown information (e.g. prime factors)

2. Recognizing anomalies in much data

3. Predicting in real-time

4. Finding optimal solutions

5. Optimizing complex processes

6. Simulating material and substance behavior 



Quantum Computing Use Cases

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Quantum Computing Lab

We have founded the Quantum Computing Lab to provide advisory and hands-on implementation service to our customers. This covers several quantum computing maturity levels and phases:

  1. Quantum Computing Introduction and Awareness
  2. Quantum Computing Frameworks and Tooling
  3. Quantum Computing Use Case Analysis and Design
  4. Quantum Computing Proof of Concepts

We are partnering with the leading technology providers and are working closely together with ARIC and the Hamburger Quantum Computing Initiative „Quantum Innovation Capital Hamburg“ (QUIC).


Quantum Lab

Quantum Team



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