Powerful tools and services for utility-scale workloads
Quantum computing resources include physical quantum hardware such as superconducting qubits and trapped ions, providing direct access to quantum processors via cloud platforms.
Optimize Complex Problems
Service
Solve intricate optimization challenges more efficiently than classical methods, enhancing decision-making in logistics and finance.
Simulate Molecular Structures
Tool
Model and analyze molecular interactions with high precision to advance drug discovery and material science.
Enhance Machine Learning Models
Tool
Boost machine learning performance with quantum-enhanced techniques for better data analysis and pattern recognition.
Solve Cryptographic Challenges
Service
Develop robust cryptographic solutions and secure communication protocols using quantum computing capabilities.
Accelerate Quantum Simulations
Tool
Perform detailed simulations of quantum systems to explore and validate fundamental physical phenomena.
Innovate in Quantum Algorithms
Service
Design and experiment with novel quantum algorithms to push the boundaries of computational problem-solving.
Analyze Financial Models
Service
Use quantum computing to refine financial models and improve risk assessment and portfolio optimization.
Enhance Drug Discovery
Tool
Leverage quantum simulations to accelerate drug discovery processes and identify potential therapeutic compounds.
Hardware and simulator apps that can be deployed as compute resources.
Get Started with Quapp
New to Functions
New to Quapp Store
New to Documentation
Join us on Slack
Show cases
Q-FLIGHT PLANNER: TRAVELING SALESMAN PROBLEM
Discover the future of air travel with Q-Flight Planner, a cutting-edge quantum application designed to solve the Traveling Salesman Problem (TSP) for the aviation industry. This innovative app leverages the unparalleled power of quantum computing to optimize flight paths and fuel consumption, offering a solution that significantly reduces operational costs and environmental impact.
Q-Flight Planner utilizes sophisticated algorithms to analyze multiple flight routes, efficiently determining the most cost-effective and environmentally friendly path among various destinations. By optimizing these routes, airlines can achieve a remarkable decrease in fuel usage, leading to substantial savings and a notable reduction in carbon emissions.
Q-PORTFOLIO: FINANCE OPTIMIZATION
Transform your investment strategy with Quantum Portfolio Optimizer, utilizing cutting-edge quantum computing technologies like VQE and QAOA for unparalleled portfolio optimization. Our app delivers comprehensive risk and return analysis, real-time optimization, and customizable constraints to maximize your returns while minimizing risk. Enjoy intuitive visualizations of asset allocations, robust backtesting against historical data, and scenario analysis to prepare for various market conditions.
With seamless data integration and a user-friendly interface, Quantum Portfolio Optimizer provides the tools you need to stay ahead in the investing world. Experience the future of portfolio management today.
Q-FLEET: VEHICLE ROUTING PROBLEM
Introducing Q-Fleet, the ultimate quantum application designed to revolutionize fleet management through the power of quantum computing. By tackling the complex Vehicle Routing Problem (VRP), Q-Fleet offers an unparalleled solution for optimizing vehicle usage, ensuring maximum efficiency, and reducing operational costs.
Q-Fleet employs advanced quantum algorithms to analyze and optimize routes for a fleet of vehicles, considering various constraints such as delivery times, vehicle capacities, and distance minimization. This not only enhances the efficiency of vehicle operations but also contributes significantly to reducing fuel consumption and carbon emissions, aligning with environmental sustainability goals.
QUANTUM - QKMEAN
K-mean is the popular unsupervised machine learning algorithm, given M points and k (number of clusters), this algorithm will find k centroids, and then predict the label's new points by comparing the distance between the new point with the centroids. The new points belong to the cluster whose centroid is nearest to the new point.
The Quantum K-mean is an enhanced version of the K-mean by replacing the normal Euclidean distance function (O(N^2)) with the quantum Euclidean distance function (O(log(N)) (N is dimensional of point).
