Rigetti Computing and US Air Force Research Laboratory

Introduction

The partnership between Rigetti Computing and the U.S. Air Force Research Laboratory represents a significant milestone in applying quantum computing to defense and national security challenges. Rigetti, a leading full-stack quantum computing company based in Berkeley, California, has been at the forefront of developing both quantum hardware and software solutions. The company’s quantum cloud services platform provides access to their quantum processors, enabling researchers and organizations to develop and test quantum algorithms remotely. The U.S. Air Force, recognizing the transformative potential of quantum computing for military applications, has been actively investing in quantum research through AFRL. This partnership aims to explore how quantum computing can solve complex optimization problems, enhance machine learning capabilities, and strengthen cryptographic systems. The collaboration brings together Rigetti’s expertise in superconducting quantum processors and quantum software development with AFRL’s deep understanding of defense-specific computational challenges. By working together, both organizations seek to accelerate the practical application of quantum computing in areas critical to national security.

Challenge

The U.S. Air Force faces numerous computational challenges that classical computers struggle to solve efficiently. These include complex optimization problems such as mission planning, resource allocation, and supply chain logistics, where the number of variables and constraints can create computational bottlenecks. Additionally, the Air Force requires advanced machine learning capabilities for pattern recognition, threat detection, and predictive maintenance of aircraft and systems. The exponential growth in data from sensors, satellites, and communication systems demands new computational approaches that can process and analyze information more effectively. Cryptographic security presents another critical challenge, as the advent of quantum computers threatens current encryption standards while also offering opportunities for quantum-secured communications. The Air Force also needs to simulate complex physical systems for materials science and aerodynamics research, where quantum effects play a crucial role. Traditional computing approaches often require approximations or excessive computational time for these problems. The partnership with Rigetti aims to address these challenges by developing quantum algorithms and applications that can provide computational advantages over classical methods, potentially offering exponential speedups for certain problem classes.

Solution

Rigetti and AFRL developed a comprehensive quantum computing solution centered around Rigetti’s Quantum Cloud Services (QCS) platform, which provides access to their superconducting quantum processors. The solution includes the development of hybrid classical-quantum algorithms tailored to Air Force-specific use cases. For optimization problems, they implemented Quantum Approximate Optimization Algorithm (QAOA) variants designed to handle mission planning and resource allocation scenarios. The team created quantum machine learning algorithms using variational quantum circuits to enhance pattern recognition and anomaly detection capabilities. For cryptographic applications, they explored quantum key distribution protocols and post-quantum cryptography algorithms that could secure communications against future quantum threats. The solution leverages Rigetti’s Forest SDK and PyQuil programming framework, allowing AFRL researchers to develop, simulate, and run quantum algorithms on actual quantum hardware. The partnership also established a framework for benchmarking quantum algorithms against classical approaches, ensuring that quantum solutions provide genuine advantages. Additionally, they developed noise mitigation techniques to improve the reliability of results from near-term quantum devices, addressing the challenge of quantum decoherence and gate errors that affect current quantum processors.

Implementation

The implementation began with establishing secure cloud access protocols that meet Department of Defense security requirements, allowing AFRL personnel to access Rigetti’s quantum processors remotely while maintaining data security. The teams conducted extensive training sessions to familiarize Air Force researchers with quantum programming using PyQuil and the Forest SDK. Implementation proceeded in phases, starting with proof-of-concept demonstrations on small-scale problems before scaling to more complex applications. For optimization problems, the team first tested QAOA on simplified logistics scenarios with reduced variables, gradually increasing complexity as quantum hardware improved. Machine learning implementations began with quantum feature mapping experiments, progressing to full variational quantum classifier implementations for specific Air Force datasets. The partnership established regular benchmarking cycles, comparing quantum algorithm performance against classical baselines on identical problems. They implemented error mitigation strategies including zero-noise extrapolation and probabilistic error cancellation to improve result accuracy. The teams also developed custom quantum circuits optimized for Rigetti’s specific quantum processor architecture, taking advantage of the connectivity and gate fidelities of the hardware. Regular workshops and collaborative sessions ensured knowledge transfer between Rigetti’s quantum experts and Air Force domain specialists.

Results and Business Impact

The partnership yielded significant results in demonstrating quantum advantage for specific Air Force applications. In optimization tasks, quantum algorithms showed promising speedups for certain problem instances, particularly in scenarios with complex constraint structures typical of mission planning. The quantum machine learning implementations achieved comparable accuracy to classical methods while using significantly fewer training parameters, suggesting potential advantages as quantum hardware scales. For cryptographic applications, the team successfully demonstrated quantum key distribution protocols that could enhance secure communications. The collaboration resulted in multiple research publications and patents, advancing the field of quantum computing for defense applications. From a business perspective, the partnership helped the Air Force build internal quantum computing expertise, creating a cadre of quantum-literate personnel who can evaluate and implement quantum solutions. For Rigetti, the collaboration validated their quantum cloud platform for high-security government applications and provided valuable feedback for hardware and software improvements. The partnership also identified specific metrics for quantum advantage in military applications, establishing benchmarks for future quantum computing investments. The project demonstrated that current noisy intermediate-scale quantum (NISQ) devices can provide value for certain applications while highlighting areas where further quantum hardware development is needed.

Future Directions

The partnership continues to evolve with plans to explore more advanced quantum algorithms as hardware capabilities improve. Future directions include investigating quantum simulation for materials discovery relevant to aerospace applications, potentially accelerating the development of new materials for aircraft and spacecraft. The teams plan to expand quantum machine learning applications to include quantum neural networks and quantum reinforcement learning for autonomous systems. As quantum processors scale to more qubits with lower error rates, the partnership aims to tackle larger optimization problems that are currently intractable. There are plans to explore distributed quantum computing, connecting multiple quantum processors to solve problems beyond the capacity of single devices. The collaboration will also focus on developing quantum-classical hybrid architectures that leverage the strengths of both computing paradigms. Future work includes establishing quantum computing standards and best practices for defense applications, potentially influencing broader Department of Defense quantum initiatives. The partnership will continue to monitor advances in quantum error correction, preparing to implement these techniques as they become practical, which could dramatically expand the range of solvable problems.