
D-Wave RPO soars 1,221% as quarterly revenue reaches $4.5M
Key points
- D-Wave Quantum reported third-quarter 2026 revenue of $4.5 million, up 21% year-over-year.
- Remaining performance obligations (RPO) reached $38.3 million, representing a 1,221% increase from the prior-year period. Nine-month bookings increased 611% to $37.4 million.
- D-Wave secured access to up to $100 million in U.S. government funding and expanded partnerships and commercial deployments across quantum computing applications.
D-Wave Quantum (NASDAQ:QBTS) reported third-quarter 2026 revenue of $4.5 million, up 21% year-over-year, as demand for its quantum computing technology and commercial applications continued to expand.
The company reported remaining performance obligations of $38.3 million, representing a 1,221% increase from the same period last year.
Bookings for the first nine months of 2026 reached $37.4 million, up 611% year-over-year, reflecting increased commercial activity and customer demand.
Among its major developments, D-Wave finalized an agreement with the U.S. Department of Commerce providing access to up to $100 million in funding under the CHIPS and Science Act.
The funding will be released in installments subject to specified milestones and conditions, supporting development of next-generation annealing and gate-model quantum computers.
The company also announced that NTT DOCOMO, Japan's largest mobile operator, deployed its second production application powered by D-Wave's hybrid quantum technology.
The application reduced daily peak location registration signals by 65.3% and paging signals by 7%, improving telecommunications network efficiency.
D-Wave also reported a successful proof-of-technology project with SkyWater, where its annealing quantum computing technology reduced manufacturing lot delays by up to 30% and improved factory throughput.
The company continues to advance its dual-platform strategy, developing annealing systems for optimization applications and gate-model technology targeting more complex quantum computing workloads.
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