APPLICATIONS

Hundreds of Quantum Applications

The D-Wave quantum computer leverages quantum dynamics to accelerate and enable new methods for solving complex problems. Our customers are building quantum applications for a broad spectrum of industries and use cases such as logistics, financial services, drug discovery, materials sciences, scheduling, fault detection, mobility, and supply chain management. Learn more about today’s quantum computing use cases below.

Featured Applications

  • Application

    Designing Peptide Therapeutics on a Quantum Computer

    Application
    Designing Peptide Therapeutics on a Quantum Computer

    Peptides are mid-size molecules composed of amino acids and constitute some of nature's best drugs. Designing these mid-size molecules computationally remains a challenge due to the astronomical search space and complex energy dynamics. Here, we explore how quantum annealing and hybrid approaches may offer a new alternative toolkit for designing peptide therapeutics that could hold the key to groundbreaking new drugs.

    COMPANY : Menten AI
    INDUSTRY : Life Sciences
    DISCIPLINE : Optimization
  • Application

    Efficient Earth Observation Satellites Mission Planning with Quantum Algorithm

    Application
    Efficient Earth Observation Satellites Mission Planning with Quantum Algorithm

    Earth observation satellites (EOS) collect vital data for various applications such as weather forecasting, disaster management, environmental monitoring, etc. Maximizing the value of this data requires designing optimal EOS missions to capture targets with high business value or priority while satisfying complex constraints such as storage capacity, energy limits, weather, etc. However, traditional computing methods often struggle with the complexity of optimizing EOS mission schedules, leading to suboptimal target selection and reduced data collection efficiency.

    In this paper, we demonstrate the potential of our quantum algorithm to optimize EOS mission schedules and improve the efficiency of multiple EOS in real-time. The aim is to maximize the acquisition of high-priority targets with significant business value within the constraints of limited resources. We evaluated the performance of our quantum algorithm by comparing it with two classical optimization algorithms: simulated annealing and Gurobi optimizer. Our quantum algorithm outperformed the Gurobi optimizer by 23.46%.

    COMPANY : Artificial Brain
    INDUSTRY : Aerospace
    DISCIPLINE : Optimization