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The World’s first n.quantum computing blockchain platform based on the DynexSolve algorithm, a Proof-of-Useful-Work (PoUW) approach to solving real-world problems. Proof-of-Useful-Work is prized for combining the trustless systems of traditional mining with active "Useful-Work" - forming the additional layer of Neuromorphic computing power. The Dynex platform then distributes the Neuromorphic computing system, available on demand, as a product for organizations or individuals to purchase. This allows both users and the Dynex platform to become more efficient and economical in a world of ever-increased scrutiny to waste electricity.

Our proprietary Dynex Chip design is built based on ideal memristors. Memristors are two-terminal resistive devices with memory. In general, their nonlinear dynamic behavior is mathematically modeled by means of a differential algebraic equation (DAE) set, in which an ordinary differential equation (ODE) governs the time evolution of the memory state. In contrast, an algebraic relation captures the state- and input-dependent Ohm law.

The memristor, an acronym for memory resistor, was theoretically introduced in 1971 by L.O. Chua. Presented in the 1971 pioneering paper, presently referred to as the ideal memristor, is the fourth fundamental two-terminal circuit element, the other three being the resistor, the capacitor, and the inductor. Since then, the interest in memristors and their applications has been growing exponentially, with academia and industry deploying a huge amount of funds and personnel to fabricate, model, and explore the full potential of these devices in electronics applications.

The DynexSolve PoUW algorithm utilizes the unprecedented performance of such memristors and performs ODE integration (simulations) of our Dynex Chips. By utilizing the massive parallelism of all participating Graphic Processing Units (GPUs), we can achieve close to real-time performance of the original chip design. This allows computations of constraint satisfaction problems, mixed integer linear programming, quadratic unconstraint binary optimization, maximum satisfiability problem, federated machine learning, efficient pre-training of restricted Boltzmann machines and deep neural networks, subset sum problems or integer factorization.

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