D-Wave Leap™ Quantum Cloud systems can be used solve optimization problems —specifically those with practical applications. D-Wave systems use a process called quantum annealing to search for solutions to a problem.

It also has an open-source hybrid workflow platform for building and running quantum-classical hybrid applications. D-Wave offers a hybrid platform that lets developers leverage the most appropriate computing resources for each part of their application, without worrying about the size and topology of the QPU. The hybrid platform also provides the flexibility for developers to experiment with different strategies for hybridizing their applications.

The CQM solver supports continuous variables, enabling better representation of an even broader mix of constrained problem types. With continuous variables, developers can determine optimal vehicle routes by considering capacity, travel/wait times and distances; pharmaceutical companies can more deeply analyze patient outcomes of drug trials by reviewing trial duration, time-to-patient outcomes and number of iterations; and energy operators can more effectively deliver power to customers through models that address generator output, fuel consumption and emission, and storage levels.

The D-Wave Leap™ Quantum Cloud Service delivers immediate, real-time access to D-Wave’s Advantage quantum computer and quantum hybrid solver service, all with enterprise class performance and scalability. Leap provides access to a portfolio of hybrid solvers, enabling enterprises to address all kinds of business problems that range in size and complexity. Solvers include the binary quadratic model solver, the discrete quadratic model solver, and the constrained quadratic model solver.

Classical computing processes data in a binary space, which limits the volume of data it can handle and the decisions it can produce. This is also known as serial processing. Quantum computing, however, uses multidimensional processing.

Here are several practical applications of quantum computing we could see in the future:

AI and machine learning (ML)

The capability of calculating solutions to problems simultaneously, as opposed to sequentially, has huge potential for AI and ML. Organizations today use AI and ML to discover ways to automate and optimize tasks. When used in combination with quantum computing, optimization can happen much faster and at scale, especially when processing and analyzing highly complex or even unstructured big data sets.

Financial modeling

With the modeling capabilities of quantum computing, financial organizations could use the technology to better model the behavior of investments and securities at scale. This could help reduce risk, optimize large-scale portfolios and help financial organizations better understand the trends and movements of the global financial economy.

Cybersecurity

Quantum computing could have a direct impact on privacy and encryption. Given the rapidly evolving nature of the cybersecurity landscape, quantum computers could help keep­­ data encrypted while in use, providing both in-transit and at-rest protections.

Route and traffic optimization

Optimal route planning is key to smooth supply chain logistics and transportation. The biggest challenge is harnessing all the real-time data — from changing weather patterns to traffic flow — that affects this planning. This is where quantum computers can excel. They could process all that data in real time and adjust routes for an entire fleet of vehicles at once, putting each on the optimal path forward.

Manufacturing

Quantum computers can run more accurate and realistic prototyping and testing. In the manufacturing space, this could help reduce the cost of prototyping and result in better designs that don’t need as much testing.

Drug and chemical research

Quantum computers can create better models for how atoms interact with one another, leading to a superior and more precise understanding of molecular structure. This may directly impact drug and chemical research and impact the way new products and medicines are developed. The predictive power of quantum computers could also provide foresight into how chemical compounds and drugs would develop, evolve, and interact with other elements over time.

Batteries

Quantum computing could help manufacturers better understand how to incorporate new materials into products such as batteries and semiconductors. This could provide more insight into how to optimize batteries for longevity and efficiency. Quantum computing can also help manufacturers gain a better understanding of lithium compounds and battery chemistry. For example, quantum computing could tap into and understand how the docking energy of proteins works, which results in better batteries for electric vehicles.

Power consumption for computation is a serious and growing issue for the world. We rely more and more on computing for mobile computing, automation, machine intelligence, cloud computing, and supercomputers that require increasingly more energy consumption. Highly specialized coprocessors such as D-Wave’s quantum processing units (QPUs) show promise in significantly increasing the power efficiency of computing.

In a recent study, D-Wave’s 2000-qubit system was shown to be up to 100 times more energy efficient than highly specialized algorithms on state-of-the-art classical computing servers when considering pure computation time, suggesting immediate relevance to large-scale energy efficient computing.