Neuromorphic quantum computing is a special type of computing that combines ideas from brain-like computing with quantum technology to solve problems. It works differently from regular quantum computing by using a network of connected components that can quickly react to changes, helping the system to swiftly find the best solutions.
Dynex is applying a digital twin of a physical neuromorphic quantum computing machine, which is operated on hundreds of thousands of GPUs in parallel, delivering unparalleled quantum computing performance at scale for real-world applications.
This technology is exciting because it leads to computer systems that are faster and capable of handling complex tasks more efficiently than traditional computers.
The Dynex SDK natively supports both n.quantum gate-based circuits and n.quantum annealing-based sampling, seamlessly integrating with any Python code. Programmers who are familiar with quantum gate circuit languages such as Qiskit, Cirq, Pennylane, OpenQASM, or quantum annealing tools like the Dimod framework, PyQUBO, and other QUBO frameworks, will find it easy to run computations on the Dynex neuromorphic computing platform. The Dynex SDK supports both quantum circuits and quantum annealing, but without the typical constraints associated with conventional quantum machines. The Dynex SDK is a suite of open-source Python tools for solving hard problems with neuromorphic computing which helps reformulate your application’s problem for solution by the Dynex computing platform. It also handles communication between your application code and the Dynex neuromorphic computing platform automatically.
The following videos are available to explain how to use the Dynex SDK:
- Tutorial: Compute on Dynex: "Hello, world" (using Github CodeSpace)
- Tutorial: Compute on Dynex: "Hello, world" (using pip install dynex)
Neuromorphic Computing for Computer Scientists: A complete guide to Neuromorphic Computing on the Dynex Neuromorphic Cloud Computing Platform, Dynex Developers, 2024, 249 pages, available as eBook, paperback and hardcover
A free sampler designed for prototyping and testing, enabling users to sample computing problems locally on their machines before deploying to the Dynex Neuromorphic Computing cloud is available. This tool is particularly useful for developers and researchers who are in the initial stages of project development, allowing them to fine-tune and test their code in a controlled, cost-free environment. By providing this capability, we aim to facilitate seamless code development and ensure that your applications are fully optimized and error-free prior to scaling up operations on the cloud. This not only enhances development efficiency but also helps in significantly reducing potential overheads associated with live deployments.
Customers can also experience the power of Dynex n.quantum computing with just one click by trying out a variety of examples for free. Simply register an account to access these advanced computational capabilities. The user-friendly platform makes it easy to get started, allowing exploration of the potential of neuromorphic quantum computing through practical, real-world examples.
Dynex' subscription model is designed to provide users with continuous access to its advanced neuromorphic computing platform. This model allows businesses and researchers to leverage Dynex’s powerful computing capabilities through a recurring payment structure, offering a scalable and cost-effective solution for complex computational needs. Subscribers can choose from various tiers, each tailored to different usage requirements and budgets, ensuring that users only pay for the computing power they need. This subscription model not only simplifies budgeting and planning but also ensures that users can consistently rely on Dynex for high-performance computing resources without the need for large upfront investments. Each tier provides progressively more powerful computing capabilities and enhanced support features to meet the diverse needs of our users, from individual developers to large enterprises.
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