Rigetti SPAC Presentation Deck

Partnering on simulation applications Simulate quantum mechanical systems exponentially faster to unlock the complexities of nature, such as predicting intractable dynamics at the core of physical models, and estimating physical properties of materials. Fusion energy with the Department of Energy Problem Area Challenge of developing sustainable fusion energy production, mastering non-linear plasma dynamics and control Path to Advantage Replicate governing physical mechanics with quantum mechanical effects² Operation Impact Design more efficient fusion reactors based on realistic physical modeling PARA ENER TEPST RUPAME More applications • Predicting molecular structures for novel catalysts³ Optimizing chemical reaction dynamics for fertilizers³ • Engineering functional proteins for drug design4 • Navigating the nuclear shell model for safer reactor design5 Calculating intractable Monte Carlo in high energy particle physics Increasing the efficiency of solar cells via solid-state materials design7 Modeling physical systems with UK government Example Problem Area Designing solid-state materials, e.g., batteries, due to strongly correlated electronic behavior Path to Advantage Apply hybrid variational techniques to solve electronic structure calculations, mapping exponential entanglements onto quantum native hardware Potential operational Impact Practical improvements for battery energy density and lifetime via predictable nano-scale innovations UK Research and Innovation $1.4B+ Annual revenue opportunity by 20261 Impact sectors Aerospace & defense Energy, utilities & climate Manufacturing Healthcare & life sciences Logistics & transportation Financial services Scientific research 1 Baul, Supradip, et al. Global Enterprise Quantum Computing Market Opportunity Analysis and Industry Forecast, 2018-2025. Allied Market Research. 2 Lykken, Joseph D. "Quantum Information for Particle Theorists." ArXiv:2010.02931 [Hep-Lat, Physics:Hep-Ph, Physics:Quant-Ph], Dec. 2020. arXiv.org. 3 Cao, Yudong, et al. "Quantum Chemistry in the Age of Quantum Computing." Chemical Reviews, vol. 119, no. 19, Oct. 2019, pp. 10856-915. arXiv.org, doi:10.1021/acs.chemrev.8b00803. 4 Outeiral, Carlos, et al. "The Prospects of Quantum Computing in Computational Molecular Biology." WIRES Computational Molecular Science, vol. 11, no. 1, Jan. 2021. arXiv.org, doi:10.1002/wcms.1481. 5 Dumitrescu, E. F., et al. "Cloud Quantum Computing of an Atomic Nucleus." Physical Review Letters, vol. 120, no. 21, May 2018, p. 210501. arXiv.org, doi:10.1103/PhysRevLett. 120.210501. 6 Joseph, Ilon. "Koopman-von Neumann Approach to Quantum Simulation of Nonlinear Classical Dynamics." Physical Review Research, vol. 2, no. 4, Oct. 2020, p. 043102. arxiv.org. doi:10.1103/PhysRevResearch.2.043102. 7 "The Promise and Challenges of Quantum Computing for Energy Storage." Joule, vol. 2, no. 5, May 2018, pp. 810-13. www.sciencedirect.com, doi:10.1016/j.joule.2018.04.021. rigetti

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