Deep Dive on Quantum Firm Rigetti ($RGTI)
Ahead of its March 4 earnings, a new Substack analysis takes a deep dive into quantum computing company Rigetti Computing. The report covers its technology, competition, financials, and ties to DARPA after a volatile stock run.
Rigetti is pursuing a technology roadmap with plans to deploy a 150+ qubit system by 2026 and a 1,000+ qubit system by 2027, targeting fidelities of 99.7% and 99.8% respectively. The company's strategy is centered on a modular, multi-chip architecture using superconducting qubits, which it fabricates in-house at its own facility, Fab-1. The company has secured multiple contracts with the U.S. Defense Advanced Research Projects Agency (DARPA). One project is for DARPA's Quantum Benchmarking Initiative (QBI) to help establish performance standards for utility-scale quantum computers. Another, part of the IMPAQT program, focuses on developing quantum algorithms for complex combinatorial optimization problems. On the commercial front, Rigetti recently secured an $8.4 million purchase order from India's Centre for Development of Advanced Computing (C-DAC) for a 108-qubit quantum computer. This adds to a business model that primarily derives revenue from government contracts and research partnerships while it builds toward broader enterprise adoption. For its upcoming fourth-quarter 2025 earnings, Wall Street analysts expect Rigetti to report a narrower loss of $0.03 per share, compared to a loss of $0.08 in the prior-year quarter, with revenues projected to rise 2.6% to $2.33 million. The report follows competitor IonQ's better-than-expected results, which has seemingly increased investor expectations for Rigetti. Rigetti competes with tech giants like Google and IBM, which also utilize superconducting qubits, as well as companies with different core technologies like IonQ (trapped ions) and D-Wave (quantum annealing). Unlike diversified players, Rigetti is a pure-play, full-stack company, controlling everything from chip design to its cloud delivery platform. For the financial sector, the promise of this technology lies in solving complex optimization and simulation problems beyond the scope of classical computers. Potential applications include more accurate derivatives pricing, advanced risk profiling for portfolios, and optimizing trading strategies by analyzing vast, multi-variable datasets more efficiently.
