SavantX is an industry leader in quantum computing and one of the first companies to apply quantum computing systems to solve real-world optimization problems.
Quantum Computing Elevated
SavantX brings a suite of powerful machine learning (ML) and mathematical algorithmic tools to solve the most difficult challenges in understanding the contents of “Big Data” and extremely complex processes. Machine learning systems provide intelligent search and discovery solutions using iterative processes to learn by ingesting data, and continuously refining understanding of that data corpus. Leveraging Quantum computing also greatly enhances the quality and the speed at which the models are adapted and corrected over time, eventually getting to an almost real-time corrective process. SavantX’s advanced analytics toolkit helps researchers, analysts and decision makers create predictive analytics hypotheses and uniquely powerful discoveries of otherwise unknown relationships across large disparate data sets. Our patented ML processes are approaching the achievement of Artificial Intelligence (AI).
SavantX applies ML and quantum computing to large and complex datasets to find hidden relationships, solve optimization problems, and improve organizational performance. Here are just a few of the many benefits of SavantX QUANTUM:
- Patented Quantum Computing ML / data visualization
- ML solutions eliminate inherent human biases in data analysis
- Automatically generates link analysis against discrete queries
- Highly Scalable
- Optimized and proven against very large, heterogeneous, noisy data sets in the commercial nuclear power industry
QUANTUM RESEARCH PAPERS FROM THE SAVANTX RESEARCH CENTER
The SavantX Research Center has been hard at work developing cutting-edge technologies and writing research papers in any spare time found.
Grover's Algorithm with Diffusion and Amplitude Steering
As featured on Cornell University Quantum Physics website:
Written by Robert L. Singleton JR, Michael L. Rogers, and David L. Ostby of the SavantX Research Center based in Santa Fe, NM
We review the basic theoretical underpinnings of the Grover algorithm, providing a rigorous and well motivated derivation. We then present a generalization of Grover’s algorithm that searches an arbitrary subspace of the multi-dimensional Hilbert space using a diffusion operation and an amplitude amplification procedure that has been biased by unitary {\em steering operators}. We also outline a generalized Grover’s algorithm that takes into account higher level correlations that could exist between database elements. In the traditional Grover algorithm, the Hadamard gate selects a uniform sample of computational basis elements when performing the phase selection and diffusion. In contrast, steered operators bias the selection process, thereby providing more flexibility in selecting the target state. Our method is a generalization of the recently proposal pattern matching algorithm of Hiroyuki et al.
Click here for the full 33 page PDF.
Floating-Point Calculations on a Quantum Annealer: Division and Matrix Inversion
As featured on the Frontiers in Physics Quantum Engineering and Technology website:
Written by Robert L. Singleton JR and Michael L. Rogers of the SavantX Research Center based in Santa Fe, NM
Systems of linear equations are employed almost universally across a wide range of disciplines, from physics and engineering to biology, chemistry, and statistics. Traditional solution methods such as Gaussian elimination are very time consuming for large matrices, and more efficient computational methods are desired. In the twilight of Moore’s Law, quantum computing is perhaps the most direct path out of the darkness. There are two complementary paradigms for quantum computing, namely, circuit-based systems and quantum annealers. In this paper, we express floating point operations, such as division and matrix inversion, in terms of a quadratic unconstrained binary optimization (QUBO) problem, a formulation that is ideal for a quantum annealer. We first address floating point division, and then move on to matrix inversion. We provide a general algorithm for any number of dimensions, and, as a proof-of-principle, we demonstrates results from the D-Wave quantum annealer for 2 × 2 and 3 × 3 general matrices. In principle, our algorithm scales to very large numbers of linear equations; however, in practice the number is limited by the connectivity and dynamic range of the machine.
Click here to read the full research paper.
Company
SavantX is one of the first organizations in the world to leverage the enormous power of quantum computing to solve commercial applications with significant cost savings and efficiency gains.
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