Beyond Silicon: computing technologies of the future

Quantum computing: Computing with superposition and entanglement

Quantum computing extends the classical computing model by using quantum mechanical states. Qubits allow the representation and processing of information in superimposed states and their entanglement. This allows certain calculations to be carried out faster and more efficiently than on conventional computers.

The benefit is not for general applications, but for clearly defined problem classes, such as the simulation of quantum mechanical systems, certain optimization problems or specific linear algebra operations. Compared to today's high-performance computers, the advantage lies not in short-term performance, but in the potentially fundamentally different scaling of certain algorithms. Companies are already looking at quantum computing in order to identify use cases, develop hybrid classical-quantum approaches and realistically assess technological maturity.

Photonic computing: computing with light

Photonic computing uses light as an information carrier, especially for data transmission and linear operations. Photons allow high parallelism and bandwidth with low interaction, which makes them particularly suitable for data-intensive tasks. In practice, hybrid systems are often created in which photonic and electronic components are combined.

The advantage over electronic architectures lies primarily in high parallelism, low latency and the energy-efficient implementation of certain mathematical operations. These approaches are particularly relevant for data centers, AI accelerators and high-speed communication. Photonic computing is interesting for companies as it can be gradually integrated into existing systems and is already being used productively in specific areas.

Neuromorphic computing: inspired by the brain

Neuromorphic computing follows an approach that is based on the structure and functioning of the human brain by modeling neuronal and synaptic structures and their functions. Information processing is asynchronous and event-based: States are only activated when required - similar to neurons in the brain - which leads to considerable energy savings. In neuromorphic systems, data processing and storage are mapped in a common hardware topology, eliminating the need for data transfer between processor and memory and reducing latency.

The added value compared to current architectures lies less in general computing power than in the ability of neuromorphic systems to react flexibly to changing inputs without necessarily requiring complete retraining.

Neuromorphic systems are particularly suitable for processing sensor data, for energy-efficient edge applications and for tasks with temporal patterns or closed control loops in which systems have to react continuously to inputs and constantly adapt their behavior.

Biological computing: computing with the molecular building blocks of life

Biological computing comprises computing approaches in which biological, biochemical or molecular processes are used to process information. These include molecular reaction networks, chemical logic systems, synthetic-biological circuits and DNA- and RNA-based processes. These approaches differ fundamentally from electronic computers, as information is not represented and processed by electrical states, but by concentrations, reaction dynamics and physico-chemical interactions.

The potential benefit of biological computing approaches does not lie in high clock frequency or low latency, but in the possibility of massive physical parallelism and very high information density at the molecular level. Computational operations are generated by many simultaneous reactions instead of sequential instruction execution. Most approaches are currently still at the research stage, but in the long term they open up new perspectives for special problem classes and new forms of information processing that can only be mapped to a limited extent using classic electronic architectures.