/Quantum computing for optimization (via Qpute.com)
Quantum computing for optimization

Quantum computing for optimization (via Qpute.com)


QC solves mathematical optimization problems in X-ray computed tomography

At BayQS, our goal is to enable quantum-computer-driven image and signal processing for imaging, sensor technology and signal processing at the Development Center for X-ray Technology. We focus on the use of QC to solve mathematical optimization problems efficiently. These kinds of problems occur both in signal reconstruction and in planning the optimum use of sensor technology.

What this means specifically can be explained using X-ray computed tomography – a 3D-imaging technique used in industrial measurement technology – as an example. Here, quantum computing can solve problems that arise in computed tomography reconstruction, as well as complex combinatorial optimization problems for CT measurement planning.

Quantum-based learning methods deliver results for wireless IoT systems faster and more efficiently

In the fields of localization and connectivity, we examine hybrid quantum algorithms for solving dynamic – that is, time-dependent – optimization problems. These include, for instance, sequential decision problems and problems relating to control complex systems.

We are already successfully using reinforcement learning methods to automate data-driven learning of appropriate rule and control strategies for these kinds of problems. Furthermore, because they specifically exploit quantum mechanical effects, quantum algorithms and quantum machine-learning methods offer the potential to accelerate these learning methods and make them more efficient, thus reducing the amount of data required, for instance.

Specifically, we investigate quantum algorithms and quantum machine-learning methods for the MIMO (multiple in, multiple out) beamforming use case, a very computationally intensive technique for producing directional radio channels.

One conceivable scenario for wireless IoT systems in industry might be considerably more efficient computation for learning methods. This would make it possible, for instance, to realize faster, more energy-efficient and more precise adjusting of antenna and radio systems to situationally changing environmental conditions for mobile industrial applications. Quantum-based learning methods can deliver the decisions and results for this faster and more efficiently.

QC offers advantages in computing complex optimization tasks in logistics

Many issues and challenges in logistics, such as those in planning and routing, can be formulated as mathematical problems and subsequently solved with algorithmic methods.

One fundamental problem that many common quantum algorithms share is the demands they place on hardware: for realistic problem sizes, these demands are so great that they cannot be met by existing or even planned quantum computers. In an attempt to circumvent this problem, hybrid variational approaches have been used, such as the Variational Quantum Eigensolver. In this approach, classical algorithmic methods for solving optimization problems are combined with quantum algorithms to best exploit even the smallest quantum resources.


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