Examples

Welcome to our comprehensive resource page, designed to guide you through both beginner and advanced examples of computing with the Dynex neuromorphic quantum computing platform. Whether you're just starting out or looking to deepen your understanding of quantum computations, our examples are tailored to help you effectively harness the power of Dynex technology. Explore step-by-step tutorials that demonstrate the platform's capabilities, and gain valuable insights into how to apply these techniques to solve real-world problems. This resource is your gateway to mastering the revolutionary potential of neuromorphic quantum computing with Dynex.

Using the Dynex SDK for n.quantum computing is straight forward. The following code executes a quantum gate based circuit on Dynex (we are using a PennyLane circuit here, but a range of other languages are supported:

import dynex
from dynex import dynex_circuit
import pennylane as qml

def circuit(params):
    qml.Hadamard(wires = 0)
    qml.CNOT(wires = [0,1])
    qml.RZ(params[0], wires = 0)
    return qml.probs(wires = [0,1])

# We set up the values for the input parameter
params = [0.1]

measure = dynex_circuit.execute(circuit, params, method='measure', shots=1, mainnet=True)
print(measure)

To perform a quantum annealing problem, the following example shows how to compute a QUBO problem:

import dynex

# create a QUBO problem:
N = 15 
K = 3 
numbers = [4.8097315016016315, 4.325157567810298, 2.9877429101815127, 
           3.199880179616316, 0.5787939511978596, 1.2520928214246918, 
           2.262867466401502, 1.2300003067401255, 2.1601079352817925, 
           3.63753899583021, 4.598232793833491, 2.6215815162575646, 
           3.4227134835783364, 0.28254151584552023, 4.2548151473817075] 
  
q = Array.create('q', N, 'BINARY') 
H = sum(numbers[i] * q[i] for i in range(N)) + 5.0 * (sum(q) - K)**2 
model = H.compile() 
Q, offset = model.to_qubo(index_label=True)

# Compute:
sampleset = dynex.sample_qubo(Q, offset, mainnet=True, 
                              num_reads=1024, annealing_time=200)
print('Result:',sampleset)

Explore Dynex's Quantum Computing Capabilities with Our Video Demos

Discover the power and versatility of Dynex's neuromorphic quantum computing platform through our curated list of example videos. These demos showcase a range of applications, including:

> Quantum Chatbot: Showcase of a Q-NLP trained special purpose chatbot for Canadian building codes.
> Quantum Sudoku: See how Dynex's platform effortlessly solves Sudoku puzzles, demonstrating the future of problem-solving.
> Quantum Transformer Algorithms: Learn how to use Quantum Transformers for advanced AI tasks, achieving unprecedented processing speeds and performance.
> Aircraft Loading Optimization: Explore how Dynex optimizes aircraft loading, streamlining aerospace operations.
> Optimized Satellite Positioning: Delve into the world of quantum algorithms enhancing satellite positioning accuracy.
> Job and Flow Shop Scheduling: Witness the efficient solutions for complex scheduling problems using Dynex's quantum computing technology.
> Workforce Scheduling: Discover how Dynex optimizes workforce resource allocation, improving operational performance and employee satisfaction.
> Multi-Vehicle Routing: Experience the cutting-edge solutions for optimizing vehicle routing, enhancing logistics and transportation efficiency.

Additionally, find tutorials for getting started with Dynex, customer testimonials, and more. These videos highlight the diverse and impactful capabilities of our platform, providing insights into how quantum computing can solve real-world problems.

Examples for beginners

To get familiar with the computing possibilities on the Dynex Platform, we have prepared a number of Python Jupyter Notebooks. Here are some of our beginner guides demonstrating the use of the Dynex SDK to formulate and build the quadratic models, sampling them on the Dynex Platform and retrieve the results:

> Computing on Dynex with Circuits: PennyLane Circuit on Dynex
> Computing on Dynex with Circuits: OpenQASM Circuit on Dynex
> Computing on Dynex with Circuits: Qiskit Circuit on Dynex
> Computing on the Dynex Platform with Python – BQM
> Computing on the Dynex Platform with Python – BQM K4 Complete Graph
> Computing on the Dynex Platform with Python – Logic Gates
> Computing on the Dynex Platform with Python – QUBO
> Computing on the Dynex Platform with Python – Anti-crossing problem
> Computing on the Dynex Platform with Python – Maximum Independent Set
> Computing on the Dynex Platform with Python – SAT
> Computing on the Dynex Platform with Python – NAE3SAT

Advanced Examples

As quantum computing technology matures and becomes more accessible, its integration into various industries is set to revolutionize multiple fields. In the pharmaceutical industry, Dynex enables breakthroughs in drug discovery and personalized treatments through advanced examples like Quantum Protein Folding and Quantum RNA Folding, improving our ability to combat disease. The automotive and aerospace sectors benefit from enhanced Computational Fluid Dynamics (CFD) simulations and optimized satellite positioning, pushing the boundaries of design and efficiency. Financial services can harness Dynex for quantum portfolio optimization, leading to superior risk management and fraud detection. In telecommunications, Dynex enhances network performance with applications like optimal WiFi hotspot positioning. Furthermore, algorithms such as MaxCut, Quantum Single Image Super-Resolution, and Quantum Integer Factorization showcase Dynex's prowess in solving complex computational problems.

Example implementations for use cases in algorithms, pharmaceutical, finance, telecommunication and aerospace:

> Advanced Examples

Machine Learning Examples

Quantum computing algorithms for machine learning harness the power of quantum mechanics to enhance various aspects of machine learning tasks. As both, quantum computing and neuromorphic computing are sharing similar features, these algorithms can also be computed efficiently on the Dynex platform – but without the limitations of limited qubits, error correction or availability.

> Machine Learning Examples