The course runs for one week, with live instructor office hours offered each day during the week. If needed, learners have an additional one-week grace period to complete assignments. Most learners report spending about 25-35 hours in total to complete the course, depending on previous experience. After the week of training,  all participants will have unlimited LeapTM quantum cloud access for one (1) month for the registered class attendee.

Yes. Currently, the program is available to all students and faculty in post-secondary institutions in Canada. 

No. You don’t need to be a quantum physicist to get started with Leap. All you need to know is Python. From beginner to advanced, any developer can get started building and running quantum applications. Connect seamlessly and securely in the cloud, and easily start solving complex problems of up to 1 million variables and 100,000 constraints.

Monthly

Training session dates for 2023* are:

• August 14-18
• September 18-22
• October 16-20
• November 13-17
• December 4-8

Please note: these dates are references only. To confirm the sessions offered by DWave, please visit:  https://learn.dwavesys.com/courses/quantum-programming-101-core

https://learn.dwavesys.com/courses/quantum-programming-101-core

The program has space for up to 40 people in 2023. Additional spaces will open in 2024.

Training sessions are currently for up to 30 participants. Sessions are kept small to help foster communication and brainstorming between participants and the instructor.

Access is available for one month during the Quickstart training. After that time, additional time on the system can be made available by obtaining a QCaaS Educational license through D-Wave.  Please note that training through the QAI program cannot be taken a second time.

As a practical quantum computing company, D-Wave builds and delivers quantum systems, cloud services, application development tools, and professional services to support the end-to-end quantum journey. It is selling time on its systems as a way to solve optimization problems more generally—specifically those with practical implications. 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.

D-Wave had a different architecture in the past to most quantum computing companies but made an announcement at Qubits 2021 that the company was working on its first universal quantum computers that can run Shor’s algorithm and other gate-model algorithms like QAOA and VQE.

Now, 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.

Power consumption for computation is a serious and growing issue for the world. We rely more and more on computing in everything we do as we try to satisfy our ever-increasing thirst for mobile computing, automation, machine intelligence, cloud computing, and increasingly powerful supercomputers. 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.

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.