Rigetti SPAC Presentation Deck

Partnering on quantum machine learning applications Quantum machine learning integrates quantum algorithms and ML programs by improving predictive accuracy or reducing training time by encoding data in the exponential Hilbert space of the quantum computer Image classification with Department of Defense Problem area Large amounts of satellite data with low quality and missing or incomplete images Path to advantage Improved accuracy and speed of classification using QNNs Operation impact Enhance rapid decision-making and fill in knowledge gaps by providing more complete, clear image data More applications Detect fraudulent financial transactions² Accelerate drug discovery by identifying promising drug candidates from high volumes of data³ Safeguard network systems with autonomous cyberwarfare and adversarial intent prediction4 Financial risk management with global bank Example of a problem area Limited data to create accurate risk models and backtest current models Path to advantage Quantum Born Machines for synthetic data generation $1.1B+ Annual revenue opportunity by 20261 Potential operational impact Testing of trading strategies with a larger number of scenarios to enable enhanced risk management Impact sectors Aerospace & defense Energy, utilities & climate Manufacturing Scientific research Healthcare & life sciences Logistics & transportation Financial services rigetti 1 Baul, Supradip, et al. Global Enterprise Quantum Computing Market Opportunity Analysis and Industry Forecast, 2018-2025. Allied Market Research. 2 Hodson, Mark, et al. "Finding the Optimal Nash Equilibrium in a Discrete Rosenthal Congestion Game Using the Quantum Alternating Operator Ansatz." ArXiv:2008.09505 [Quant-Ph], Aug. 2020. arXiv.org. 3 Li, Junde, et al. "Quantum Generative Models for Small Molecule Drug Discovery." ArXiv:2101.03438 [Quant-Ph], Jan. 2021. arXiv.org. 4 Chen, Samuel Yen-Chi, et al. "Variational Quantum Circuits for Deep Reinforcement Learning." ArXiv:1907.00397 [Quant-Ph, Stat], July 2020. arXiv.org. 54

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