cuDensityMat: A High-Performance Library for Analog Quantum Dynamics Computations#

Welcome to the cuDensityMat library documentation!

NVIDIA cuDensityMat is a high-performance library for accelerating analog quantum dynamics solvers, a component of the NVIDIA cuQuantum SDK. Functionalities of cuDensityMat are described in Overview with the installation and usage guide provided in Getting Started.

Key Features

Provides APIs for:
- Defining arbitrary, possibly batched pure/mixed quantum states in arbitrary tensor-product spaces (any number and dimensions of quantum degrees of freedom), including matrix-product-state (MPS) factorization of the quantum state.
- Defining arbitrary, possibly batched quantum many-body operators and superoperators in the form of a sum of tensor products of elementary tensor operators, full matrix operators, matrix product operators (MPO), with optional scalar coefficients. Each elementary tensor operator or matrix product operator acts on one or more specific quantum degrees of freedom whereas each full matrix operator acts on all quantum degrees of freedom (full matrix operators, elementary tensor operators, matrix product operators, and associated scalar coefficients may depend on time and user-provided real parameters).
- Computing the action of a many-body operator/superoperator on a quantum state.
- Computing the vector-jacobian product (VJP) and gradients of the many-body operator/superoperator action on a quantum state with respect to the user-provided real parameters parameterizing elementary tensor operators, matrix product operators, full matrix operators, and scalar coefficients inside the operator/superoperator.
- Composing and computing the r.h.s. of a desired quantum dynamics master equation (ordinary differential equation) or a system of coupled master equations (system of ordinary differential equations).
- Computing properties of operators and quantum states such as expectation values.
- Applying a matrix product operator (MPO) to a matrix-product state (MPS) via variational ALS fitting.
- Propagating a quantum state by a single time step, using one of the supported time propagation methods (including the single-site and two-site TDVP methods for MPS states).
- Computing the extreme eigenspectrum of a quantum many-body operator.
- Computing selected eigenpairs (e.g., the ground state) of a quantum many-body operator, including DMRG-based eigen-decomposition for matrix-product-state (MPS) representations.

Prerequisites

One of the following CUDA Toolkits and a compatible driver are required:

CUDA Toolkit

Minimum Required Linux Driver Version

CUDA® 12.x

>= 525.60.13

CUDA® 13.x

>= 580.65.06

Please refer to CUDA Toolkit Release Notes for the details.
cuTENSOR v2.5.0
cuTensorNet v2.12.0

Known Issues and Limitations

Computation of gradients via cudensitymatOperatorComputeActionBackwardDiff() currently does not support differentiation with respect to multidiagonal (DIA) elementary tensor operators, as well as distributed execution.
Computation of the extreme eigenspectrum of a quantum many-body operator is currently only supported for non-batched Hermitian operators and pure non-batched quantum states.
While cudensitymatComputeType_t is exposed as a parameter in some APIs, currently the actual compute type is solely inferred from the data type.
The tensor callback feature currently requires CUDA stream synchronization between successive operator action compute calls.
The use of the Host-side (CPU) scalar/tensor callbacks (or gradient callbacks) currently triggers synchronization of the CUDA stream.
Eigenspectrum computation currently triggers synchronization of the CUDA stream.
Distributed execution of compute calls currently triggers synchronization of the CUDA stream on each process.
The number of parallel processes (equivalently, number of GPUs) in parallel distributed runs must be a power of two, one GPU per parallel process.

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