Rigetti SPAC Presentation Deck
Partnering on optimization applications
Solve hard, constrained combinatorial optimization problems faster and within a defined error tolerance.
The quantum approximate optimization algorithm (QAOA) is a path to quantum supremacy.²
Optimal spectrum allocation with DARPA
Problem area
Establish and maintain communication networks in hostile
environments through optimized spectrum allocation
Path to advantage
Use QAOA to solve hard constraint, discrete optimization problems
faster than conventional heuristics
Operation impact
Maintain global persistent awareness despite adversarial spectrum
tactics and/or resource scarcity
DARPA
Enabling space exploration with NASA
Problem area
Optimization problems arising in NASA's missions,
e.g., interplanetary spacecraft landing controls
Path to advantage
Exploit hybrid quantum-classical models to maximize solvable
problem size
Operation impact
Safely realize ambitious space exploration through
transformational mission design practices
NASA
More applications
Portfolio optimization over discrete lots and under investment constraints³
• Job sequencing and scheduling, such as single machine scheduling
Traffic flow optimization for air traffic management
Vehicle routing including the capacity constraint
Impact sectors
$1.5B+
Annual revenue
opportunity by 20261
Aerospace & defense
Energy, utilities & climate
Manufacturing Scientific research
Healthcare & life sciences
Logistics & transportation
Financial services
rigetti
Baul, Supradip, et al. Global Enterprise Quantum Computing Market Opportunity Analysis and Industry Forecast, 2018-2025. Allied Market Research. 2 Brandão, Fernando G. S. L., et al. "Faster Quantum and Classical SDP Approximations for Quadratic Binary Optimization." ArXiv:1909.04613 [Quant-Ph], Aug.
2020. arXiv.org. 3 Hodson, Mark, et al. "Portfolio Rebalancing Experiments Using the Quantum Alternating Operator Ansatz." ArXiv:1911.05296 [Quant-Ph], Nov. 2019. arXiv.org. 4 Hadfield, Stuart, et al. "From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz." Algorithms,
vol. 12, no. 2, Feb. 2019, p. 34. arXiv.org, doi:10.3390/a12020034. Stollenwerk, Tobias, et al. "Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management." IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 1, Jan. 2020, pp. 285-97. arxiv.org.
doi:10.1109/TITS.2019.2891235.6 Irie, Hirotaka, et al. "Quantum Annealing of Vehicle Routing Problem with Time, State and Capacity." ArXiv:1903.06322 [Quant-Ph], Mar. 2019. arXiv.org.View entire presentation