At QuMatrix, we focus on quantum and classical data research projects. Quantum computing and AI are both transformational technologies, and some AI algorithms do require quantum computing to achieve significant progress. Although AI produces functional applications with classical computers, such a setup is limited by the computational capabilities of classical computers. For some algorithms, quantum computing provides a computational boost to traditional AI, enabling it to tackle more complex problem while significantly reducing the runtime.
Our objective is to convert data into facts and facts into insights and assets. We focus on innovative and comprehensive quantum computing, classical data analytics, and quantum robotics related projects. We fuse business needs and technology and focus on comprehensive data solutions that are based on sound science and engineering principles.
We specialize in customizing data requirements into a comprehensive, high fidelity, transparent, and cost-effective solution. We do this by applying science and technology strategies applicable to a particular problem to provide our clients with a wide spectrum of vertically and horizontally aligned data and hardware solutions for quantum, classical, and hybrid computing environments. All our hardware and software is designed, developed, and maintained in the US.
Quantum & Classical Data Research
Quantum & Classical AI
We are at the forefront of quantum computing and quantum mechanics, focusing on research and development projects that address quantum simulations on HPC, quantum high energy physics, as well as classical and quantum AI. Further, our focus is on quantum AI computing in robotics where we work on solutions for quantum image processing, quantum path planning, quantum task planning, and quantum movement planning, respectively.
While AI has made significant progress over the past few decades, traditional AI cannot overcome some of the technological limitations embedded into classical computing. Quantum computing is already being used to train machine learning models. We aid our clients in developing quantum AI algorithms to optimize the processing cycle. Such an optimized and stable AI infrastructure, provided by quantum computing, can for certain algorithms provide exponential speedup compared to traditional AI solutions. Neuromorphic cognitive models, adaptive machine learning, or reasoning under uncertainty are some fundamental challenges of today’s AI. It is our believe that quantum AI is one of the most likely solutions for next-generation AI problems.
To aid research as well as commercial companies, QuMatrix developed their own quantum computing simulators. The QuMatrix Quantum Simulator (QuMSim) is universally applicable to any quantum problem and is developed in Python. We also developed an HPC quantum simulator (QuMHPCSim) that scales to 1000nds of GPU cores and allows simulating larger quantum problems. QuMatrix has been using the simulators in actual quantum projects to study quantum neural networks, high energy physics, robotics, or quantum random walks.