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)


Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70 Using Quantum Computing

We conducted an experiment on the G70 MaxCut problem, a complex unweighted MaxCut challenge with 10,000 nodes and 99,999 edges sourced from the publicly available Gset dataset provided by Stanford University. Various algorithms and solvers were evaluated, including commercial solvers like Gurobi and IBM CPLEX, and specialized algorithms such as the SDP solver DSDP, the graph neural network-based PI-GNN, and the tabu search-based KHLWG. Utilizing Dynex’s neuromorphic quantum computing cloud with a single Nvidia RTX3080 GPU, and without any MaxCut-specific enhancements, Dynex achieved a Maximum Cut value of 9,506 — outperforming other algorithms and demonstrating the potential of the Dynex platform. The experiment underscores that additional computational resources or bespoke algorithmic modifications could potentially lead to even higher performance, potentially setting new benchmarks for MaxCut solutions and showcasing the scalability and adaptability of the Dynex platform for tackling such optimization problems.

> Medium: Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70 Using Quantum Computing
> Github: Jupyter Notebook


World Record: MaxCut Problem

Dynex announced a significant breakthrough in the field of quantum computing, achieved through the Dynex neuromorphic quantum computing platform. By employing a sophisticated quantum algorithm, we successfully solved a graph containing 10,000 vertices, setting a new record that surpasses the previous benchmark by threefold.

> Dynex Sets New World Record for Quantum Computing, Breaking NVIDIA’s Previous Record


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.

> Jupyter Notebook


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)


Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70 Using Quantum Computing

We conducted an experiment on the G70 MaxCut problem, a complex unweighted MaxCut challenge with 10,000 nodes and 99,999 edges sourced from the publicly available Gset dataset provided by Stanford University. Various algorithms and solvers were evaluated, including commercial solvers like Gurobi and IBM CPLEX, and specialized algorithms such as the SDP solver DSDP, the graph neural network-based PI-GNN, and the tabu search-based KHLWG. Utilizing Dynex’s neuromorphic quantum computing cloud with a single Nvidia RTX3080 GPU, and without any MaxCut-specific enhancements, Dynex achieved a Maximum Cut value of 9,506 — outperforming other algorithms and demonstrating the potential of the Dynex platform. The experiment underscores that additional computational resources or bespoke algorithmic modifications could potentially lead to even higher performance, potentially setting new benchmarks for MaxCut solutions and showcasing the scalability and adaptability of the Dynex platform for tackling such optimization problems.

> Medium: Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70 Using Quantum Computing
> Github: Jupyter Notebook


World Record: MaxCut Problem

Dynex announced a significant breakthrough in the field of quantum computing, achieved through the Dynex neuromorphic quantum computing platform. By employing a sophisticated quantum algorithm, we successfully solved a graph containing 10,000 vertices, setting a new record that surpasses the previous benchmark by threefold.

> Dynex Sets New World Record for Quantum Computing, Breaking NVIDIA’s Previous Record


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.

> Jupyter Notebook


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)


Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70 Using Quantum Computing

We conducted an experiment on the G70 MaxCut problem, a complex unweighted MaxCut challenge with 10,000 nodes and 99,999 edges sourced from the publicly available Gset dataset provided by Stanford University. Various algorithms and solvers were evaluated, including commercial solvers like Gurobi and IBM CPLEX, and specialized algorithms such as the SDP solver DSDP, the graph neural network-based PI-GNN, and the tabu search-based KHLWG. Utilizing Dynex’s neuromorphic quantum computing cloud with a single Nvidia RTX3080 GPU, and without any MaxCut-specific enhancements, Dynex achieved a Maximum Cut value of 9,506 — outperforming other algorithms and demonstrating the potential of the Dynex platform. The experiment underscores that additional computational resources or bespoke algorithmic modifications could potentially lead to even higher performance, potentially setting new benchmarks for MaxCut solutions and showcasing the scalability and adaptability of the Dynex platform for tackling such optimization problems.

> Medium: Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70 Using Quantum Computing
> Github: Jupyter Notebook


World Record: MaxCut Problem

Dynex announced a significant breakthrough in the field of quantum computing, achieved through the Dynex neuromorphic quantum computing platform. By employing a sophisticated quantum algorithm, we successfully solved a graph containing 10,000 vertices, setting a new record that surpasses the previous benchmark by threefold.

> Dynex Sets New World Record for Quantum Computing, Breaking NVIDIA’s Previous Record


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.

> Jupyter Notebook


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


Copyright © 2024 Dynex. All rights reserved.

Copyright © 2024 Dynex. All rights reserved.

Copyright © 2024 Dynex. All rights reserved.