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Rigetti Computing provides full-stack quantum computing systems and cloud-based access to quantum processors for enterprise, government, and research organizations worldwide. Its Quantum Cloud Services (QCS) platform integrates Rigetti quantum processing units with public, private, and hybrid cloud environments, offering ultra-low latency connectivity of less than one millisecond between classical hardware and quantum systems.
The company's quantum processors are built on superconducting qubit technology, manufactured in-house at Fab-1, the industry's first dedicated quantum device fabrication facility. Rigetti systems feature tunable couplers, high single-qubit and two-qubit gate fidelities, and a square lattice Ankaa-class architecture designed to support both near-term NISQ applications and the requirements of quantum error correction and fault-tolerant computing.
Rigetti's software stack includes pyQuil, a Python library for quantum programming using the Quil quantum instruction language; Quilc, an optimizing quantum compiler that accepts Quil and QASM input; and the Quantum Virtual Machine (QVM), an open-source quantum computation simulator. These tools allow developers to write, compile, and simulate quantum programs before executing them on real hardware.
The Novera QPU, Rigetti's commercially available on-premises quantum processing unit, gives research teams direct access to a 9-qubit system based on the Ankaa-class architecture with tunable couplers for high-fidelity two-qubit operations. Systems are also accessible through distribution partners including AWS and Microsoft Azure Quantum. Rigetti's technology partners include MIT, NASA, Oxford Instruments, and Standard Chartered, among others.
- Running hybrid quantum-classical algorithms on cloud-connected quantum processors for research and enterprise applications
- Executing quantum approximate optimization algorithms to address combinatorial and real-world optimization problems
- Simulating quantum chemistry and molecular properties using quantum hardware with advanced problem decomposition techniques
- Developing and testing quantum error correction protocols on superconducting quantum processors
- Accessing on-premises quantum computing systems via the Novera QPU for university and national laboratory research
- Building and deploying quantum machine learning models using hybrid quantum-classical computation frameworks
- Benchmarking quantum device performance for application development across scientific and commercial domains
- Performing quantum circuit compilation and optimization using Quilc for targeted Rigetti processor architectures
- Simulating quantum system dynamics and plasma wave propagation on superconducting quantum chips
- Integrating Rigetti quantum systems with public cloud infrastructure through AWS and Microsoft Azure Quantum
- Developing quantum algorithms for financial data analysis and prediction using quantum feature maps
