CGI and D-Wave team up to bring quantum computing to businesses

CGI and D-Wave Quantum have formalized a partnership built on over a year of prior collaboration to accelerate the adoption of quantum computing for enterprise clients. The firms will focus initially on applying D-Wave’s unique dual-platform quantum systems, offering both annealing and gate-model approaches, to complex challenges in logistics, transportation, and retail. “Organizations are moving beyond research to evaluate where quantum technologies can improve optimization and enhance decision-making,” said Dave Henderson, Chief Technology Officer, CGI, as the companies plan to co-develop solutions for areas like train scheduling and supply chain planning.

D-Wave and CGI Partnership Advances Enterprise Quantum Adoption

CGI will integrate D-Wave’s Advantage2 quantum computer and hybrid solvers into its technology services, enabling clients to test and deploy quantum solutions for challenges like industrial production planning and energy grid optimization. This collaboration extends beyond simple access; the firms plan to co-develop specific use cases where quantum computing can demonstrably improve upon existing classical methods and deliver quantifiable business results.

Initial efforts center on transportation and rail systems, targeting improvements in train scheduling, network operations, and resource allocation, alongside retail applications focused on supply chain optimization and workforce management. D-Wave, the only company offering both annealing and gate-model quantum computing systems, brings specialized hardware and software to the table.

This dual-platform capability is important as organizations move beyond theoretical exploration and seek practical applications for quantum technology, the company says. “Quantum computing’s business value is no longer theoretical,” said Lorenzo Martinelli, chief revenue officer at D-Wave, pointing to the current successes of D-Wave customers in generating tangible results with annealing quantum computing.

CGI’s scale and industry expertise are intended to bridge the gap between quantum potential and real-world implementation. Through this partnership, CGI aims to help clients identify valuable use cases and validate them through experimentation, ultimately driving broader enterprise adoption. earlier this year further solidified D-Wave’s position, adding gate-model quantum computing capability to its existing annealing technology and enabling the shipment of its first dual-rail gate-model machine from a new research center in New Haven, Connecticut.

Recent investments also demonstrate D-Wave’s momentum; the company secured up to $100 million from the US government to build quantum systems and received a $1.5 million NSF grant to advance fault-tolerant quantum computing, according to the company. A $25 million+ series C funding round advanced fabrication of components for improved materials used in quantum computing, specifically for scalability, while a $20 million agreement with Florida Atlantic University will establish a quantum computer for research and education.

Martinelli added that D-Wave customers are using annealing quantum computing today to improve operations and generate tangible results on problems that challenge legacy systems. Combining D-Wave’s quantum technology with CGI’s scale, industry expertise and delivery capabilities will give more organizations a clear path from exploration to production and measurable business value.

As AI adoption advances and organizations seek new ways to solve increasingly complex business objectives, quantum computing is emerging as a practical complement to classical computing for selected use cases.

Dave Henderson, Chief Technology Officer, CGI
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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