D-Wave Quantum Inc. – a global leader in quantum computing software, services, and systems, and the only quantum computing company building both annealing and gate model quantum computers – today announced two key updates to its constrained quadratic model (CQM) hybrid solver in the Leap™ quantum cloud service. The CQM hybrid solver can address real-world commercial-scale optimization problems of up to one million variables (including continuous variables) and 100,000 constraints. With today’s updates, businesses now can further leverage the power of quantum computation to run quadratic optimization problems with weighted constraints and benefit from presolve techniques that streamline and simplify problem formulation.
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The updated constrained quadratic model (CQM) hybrid solver from D-Wave enables quantum developers to more accurately model problems where it is not possible to satisfy all constraints. It expands the addressable use cases across various industries, e.g. logistics (employee scheduling), manufacturing (bin packing), and financial services (portfolio optimization). (Graphic: Business Wire)
The updated CQM solver allows quantum developers to better represent commercial problems, enabling them to more easily and accurately model problems where it is not possible to satisfy all constraints through classical computing logic. For example, in an employee scheduling scenario where employees should work 8 hours per shift with optional overtime, the solver is now able to allow for a soft “weighted” constraint of
In addition to supporting weighted constraints, the updated CQM solver introduces a new set of fast classical algorithms that reduces the size of the problem and allows for larger models to be submitted to the hybrid solver. Presolve techniques remove unnecessary variables and constraints to achieve a cleaner dataset, resulting in better quality solutions by narrowing the problem set/size and streamlining problem formulation. These techniques are now automatically applied to all CQM problems in the CQM solver in Leap and are also available in the Ocean SDK.
“As more companies turn to quantum computing today to solve their increasingly complex business challenges, they’re seeking ways to quickly and efficiently codify problems in order to expedite time-to-solution,” said Murray Thom, vice president of product management at D-Wave. “These enhancements to our CQM solver, which were driven in large part by our customers’ feedback, are designed to accelerate the quantum-hybrid workflow and help companies more quickly derive business value from quantum computing.”
D-Wave continues to innovate on the CQM solver, first introduced in October 2021, to meet customer and market commercial application needs. The CQM solver enables users to benefit from a simplified expression of their constrained problems by directly incorporating problem constraints. With the CQM solver, customers can address larger and more complex real-world business problems. In addition to today’s update, D-Wave added support of continuous variables to the CQM solver in May 2022.
To learn more about the updates to our CQM quantum hybrid solver, we invite you to attend the demo webinar on November 17 and review the benchmarking report, which was published today.
About D-Wave Quantum Inc.
D-Wave is a leader in the development and delivery of quantum computing systems, software, and services, and is the world’s first commercial supplier of quantum computers—and the only company building both annealing quantum computers and gate-model quantum computers. Our mission is to unlock the power of quantum computing today to benefit business and society. We do this by delivering customer value with practical quantum applications for problems as diverse as logistics, artificial intelligence, materials sciences, drug discovery, scheduling, cybersecurity, fault detection, and financial modeling. D-Wave’s technology is being used by some of the world’s most advanced organizations, including NEC Corporation, Volkswagen, DENSO, Lockheed Martin, Forschungszentrum Jülich, University of Southern California, and Los Alamos National Laboratory.
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