Dynex

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# Dynex Benchmarks

### Benchmarking the Dynex Neuromorphic 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 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 Neuromorphic 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 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 Neuromorphic 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 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

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