Computing on Dynex
Despite the incredible power of today’s supercomputers, many complex computing problems cannot be addressed by conventional systems. Dynex solves those problems.
Data growth and the quest for understanding drive the need for new tools. Quantum and Neuromorphic computing offer innovative solutions to complex problems.
Quantum computing is an emerging technology with enormous potential to solve complex problems, because it effectively applies the properties of quantum mechanics, such as superposition and entanglement. However, like any technology, there are disadvantages:
Quantum error correction is challenging due to infinite qubit states of the processor.
Hardware & Temperature:
Quantum computers require near absolute zero temperatures for stable operation, which is a challenge to maintain.
Quantum computers are small compared to classical ones, hindering scaling to hundreds/thousands of qubits with high coherence and low errors.
Neuromorphic computers overcome limitations, operating on traditional hardware with no bit calculation restrictions. They efficiently handle problems with millions of variables. Best part is they do it without having to rely on expensive scientific technologies to resolve problems.
Neuromorphic computing is based on gates supporting superposition of input and output signals, long-range correlations and instantonic jumps.
Neuromorphic systems utilize a phenomenon called inherent parallelism that can occupy unlimited coordinates simultaneously.
With neuromorphic approach to computing, systems can scale indefinitely, thanks to their multiple simultaneous operation capabilities.
The Dynex Platform leverages this dynamics to accelerate and enable new methods for solving discrete optimization, sampling, and machine learning problems. Dynex uses a process called neuromorphic annealing to search for solutions to a problem. Neuromorphic annealing is fundamentally different from classical computing.
It harnesses the natural tendency of real-world physical systems to find low-energy states. If an optimization problem is analogous to a landscape of peaks and valleys, for instance, each coordinate represents a possible solution and its elevation represents its energy. The best solution is that with the lowest energy corresponding to the lowest point in the deepest valley in the landscape.