NVIDIA cuQuantum is an SDK of optimized libraries and tools designed to accelerate quantum computing emulations at both the circuit and device level by orders of magnitude. It provides flexible, scalable, and fast solutions for quantum circuit simulation, leveraging the power of NVIDIA GPUs. cuQuantum supports various quantum simulation methods, including state vector, tensor network, and density matrix methods, making it a versatile tool for quantum computing research.

Features

• Flexible Simulation Methods: Choose from algorithm-agnostic accelerated quantum circuit simulation methods. State vector method features optimized memory management and gate application kernels. Tensor network method features accelerated tensor network contraction, order optimization, and approximate contractions. Density matrix method features arbitrary operator action on the state.
• Scalable Performance: Leverage the power of multi-node, multi-GPU clusters using the latest GPUs on premises or in the cloud. Low-level C++ APIs provide increased control and flexibility for single GPU and single-node multi-GPU clusters. The high-level Python API supports drop-in multi-node execution.
• High-Speed Simulations: Simulate bigger problems faster and get more work done sooner. Using an NVIDIA H200 Tensor Core GPU over CPU implementations delivers orders-of-magnitude speedups on key quantum problems, including random quantum circuits, Shor’s algorithm, and the Variational Quantum Eigensolver. Leveraging the NVIDIA Eos supercomputer, cuQuantum generated a sample from a full-circuit simulation of the Google Sycamore processor in less than five minutes.
• Framework Integrations: cuQuantum is integrated with leading quantum simulation frameworks, allowing users to dramatically accelerate performance using their framework of choice with zero code changes.
• Time Dynamics Functionality: Includes time dynamics functionality, enabling users to accelerate analog Hamiltonian dynamics to unprecedented scales. This allows for better optimization of device design where quantum phenomena occur faster than before.

Benefits

• Enhanced Research Capabilities: Accelerates quantum computing research by providing high-performance simulation tools.
• Increased Productivity: Reduces the time required for quantum simulations, allowing researchers to achieve results faster.
• Cost Efficiency: Utilizes the power of NVIDIA GPUs to deliver significant performance improvements, reducing the need for extensive hardware resources.
• Scalability: Supports large-scale quantum simulations, making it suitable for complex quantum computing research.
• Versatility: Applicable to various quantum computing problems and methods, providing a comprehensive solution for researchers.