Dynex

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Compute on Dynex

Dynex

Industries

Compute on Dynex

Dynex

Industries

Compute on Dynex

# 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: CFD vs. Quantum-CFD

Computational Fluid Dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and algorithms to solve and analyze problems involving fluid flows. By utilizing computational software, CFD simulates the interaction of liquids and gases with surfaces, defined by boundary conditions and initial conditions. This technique allows engineers and scientists to visualize and predict fluid flow behavior, heat transfer, and related phenomena in a virtual setting, without the need for costly and time-consuming physical experiments. CFD is widely used in industries such as aerospace, automotive, and energy, for designing and optimizing processes and products like aircraft wings, engine components, and wind turbines. The Quantum-CFD algorithm is superior to traditional CFD methods, resulting in 95% less computation time required and 90% less costs.

> Github: QCFD Benchmark (independent repo)

### 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: CFD vs. Quantum-CFD

Computational Fluid Dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and algorithms to solve and analyze problems involving fluid flows. By utilizing computational software, CFD simulates the interaction of liquids and gases with surfaces, defined by boundary conditions and initial conditions. This technique allows engineers and scientists to visualize and predict fluid flow behavior, heat transfer, and related phenomena in a virtual setting, without the need for costly and time-consuming physical experiments. CFD is widely used in industries such as aerospace, automotive, and energy, for designing and optimizing processes and products like aircraft wings, engine components, and wind turbines. The Quantum-CFD algorithm is superior to traditional CFD methods, resulting in 95% less computation time required and 90% less costs.

> Github: QCFD Benchmark (independent repo)

### 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: CFD vs. Quantum-CFD

Computational Fluid Dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and algorithms to solve and analyze problems involving fluid flows. By utilizing computational software, CFD simulates the interaction of liquids and gases with surfaces, defined by boundary conditions and initial conditions. This technique allows engineers and scientists to visualize and predict fluid flow behavior, heat transfer, and related phenomena in a virtual setting, without the need for costly and time-consuming physical experiments. CFD is widely used in industries such as aerospace, automotive, and energy, for designing and optimizing processes and products like aircraft wings, engine components, and wind turbines. The Quantum-CFD algorithm is superior to traditional CFD methods, resulting in 95% less computation time required and 90% less costs.

> Github: QCFD Benchmark (independent repo)

### 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