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Compute on Dynex
DYNEX CORE
Tech Primer
Industries
Compute on Dynex
DYNEX CORE
Tech Primer
Industries
Compute on Dynex
DYNEX CORE
Tech Primer
Dynex Benchmarks
Dynex Benchmarks
Dynex Benchmarks
Benchmarking the Dynex n.quantum Platform with the Q-Score
In the context of our study, we employ the Q-score to benchmark the computing capabilities of the Dynex Neuromorphic Quantum Computing Platform, facilitating a comparative evaluation with contemporary state-of-the-art quantum computers. Our findings demonstrate that the Dynex platform exhibits remarkable performance superiority over the presently largest quantum computing systems. While physical quantum computing systems such as Google’s Sycamore, IBM’s Osprey, D-Wave’s Advantage, and Rigetti’s Aspen-M-2 have reportedly achieved Q-scores not surpassing 140, the Dynex Neuromorphic Platform has demonstrated a Q-score exceeding 15,000.
→ Medium: Benchmarking the Dynex Neuromorphic Platform with the Q-Score
Benchmark: Quantum-Support-Vector-Machine
The Dynex QSVM PyTorch Layer outperforms D-Wave Quantum Machines (HQPU, QPU), Simulated Annealing and Scikit-Learn with 100.00% on all metrics. In this example, a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem (QSVM). Data-points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset.
Benchmark: Quantum Restricted Boltzmann Machine
This example demonstrates a Quantum-Boltzmann-Machine (QBM) implementation using the Dynex platform to perform the computations and compare it with a traditional Restricted-Boltzmann-Machine (RBM). RBM is a well-known probabilistic unsupervised learning model which is learned by an algorithm called Contrastive Divergence. An important step of this algorithm is called Gibbs sampling – a method that returns random samples from a given probability distribution. We decided to conduct our experiments on the popular MNIST dataset considered a standard benchmark in many of the machine learning and image recognition subfields. The implementation follows a highly optimised QUBO formulation.
→ Medium: Computing on the Dynex Neuromorphic Platform: Image Classification
Benchmarking the Dynex n.quantum Platform with the Q-Score
In the context of our study, we employ the Q-score to benchmark the computing capabilities of the Dynex Neuromorphic Quantum Computing Platform, facilitating a comparative evaluation with contemporary state-of-the-art quantum computers. Our findings demonstrate that the Dynex platform exhibits remarkable performance superiority over the presently largest quantum computing systems. While physical quantum computing systems such as Google’s Sycamore, IBM’s Osprey, D-Wave’s Advantage, and Rigetti’s Aspen-M-2 have reportedly achieved Q-scores not surpassing 140, the Dynex Neuromorphic Platform has demonstrated a Q-score exceeding 15,000.
→ Medium: Benchmarking the Dynex Neuromorphic Platform with the Q-Score
Benchmark: Quantum-Support-Vector-Machine
The Dynex QSVM PyTorch Layer outperforms D-Wave Quantum Machines (HQPU, QPU), Simulated Annealing and Scikit-Learn with 100.00% on all metrics. In this example, a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem (QSVM). Data-points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset.
Benchmark: Quantum Restricted Boltzmann Machine
This example demonstrates a Quantum-Boltzmann-Machine (QBM) implementation using the Dynex platform to perform the computations and compare it with a traditional Restricted-Boltzmann-Machine (RBM). RBM is a well-known probabilistic unsupervised learning model which is learned by an algorithm called Contrastive Divergence. An important step of this algorithm is called Gibbs sampling – a method that returns random samples from a given probability distribution. We decided to conduct our experiments on the popular MNIST dataset considered a standard benchmark in many of the machine learning and image recognition subfields. The implementation follows a highly optimised QUBO formulation.
→ Medium: Computing on the Dynex Neuromorphic Platform: Image Classification
Benchmarking the Dynex n.quantum Platform with the Q-Score
In the context of our study, we employ the Q-score to benchmark the computing capabilities of the Dynex Neuromorphic Quantum Computing Platform, facilitating a comparative evaluation with contemporary state-of-the-art quantum computers. Our findings demonstrate that the Dynex platform exhibits remarkable performance superiority over the presently largest quantum computing systems. While physical quantum computing systems such as Google’s Sycamore, IBM’s Osprey, D-Wave’s Advantage, and Rigetti’s Aspen-M-2 have reportedly achieved Q-scores not surpassing 140, the Dynex Neuromorphic Platform has demonstrated a Q-score exceeding 15,000.
→ Medium: Benchmarking the Dynex Neuromorphic Platform with the Q-Score
Benchmark: Quantum-Support-Vector-Machine
The Dynex QSVM PyTorch Layer outperforms D-Wave Quantum Machines (HQPU, QPU), Simulated Annealing and Scikit-Learn with 100.00% on all metrics. In this example, a classical classification model, Kernel-Support Vector machine, is implemented as a Quadratic Unconstrained Binary Optimisation problem (QSVM). Data-points are classified by a separating hyperplane while maximizing the function margin. The problem is solved for a public Banknote Authentication dataset.
Benchmark: Quantum Restricted Boltzmann Machine
This example demonstrates a Quantum-Boltzmann-Machine (QBM) implementation using the Dynex platform to perform the computations and compare it with a traditional Restricted-Boltzmann-Machine (RBM). RBM is a well-known probabilistic unsupervised learning model which is learned by an algorithm called Contrastive Divergence. An important step of this algorithm is called Gibbs sampling – a method that returns random samples from a given probability distribution. We decided to conduct our experiments on the popular MNIST dataset considered a standard benchmark in many of the machine learning and image recognition subfields. The implementation follows a highly optimised QUBO formulation.
→ Medium: Computing on the Dynex Neuromorphic Platform: Image Classification