Overview¶
This section describes the basic working principle of the cuStateVec library. For a general introduction to quantum circuits, please refer to Introduction to quantum computing.
Description of state vectors¶
In the cuStateVec library, the state vector is always given as a device array and its data type is specified by a cudaDataType_t constant. It’s users’ responsibility to manage memory for the state vector.
This version of cuStateVec library supports 128-bit complex (complex128) and 64-bit complex (complex64) as datatypes of the state vector. The size of a state vector is represented by the nIndexBits argument which corresponds to the number of qubits in a circuit. Therefore, the state vector size is expressed as \(2^{\text{nIndexBits}}\).
The type custatevecIndex_t is provided to express the state vector index, which is a typedef of the 64-bit signed integer.
Bit ordering¶
In the cuStateVec library, the bit ordering of the state vector index is defined in the little endian order. The 0-th index bit is the least significant bit (LSB). Most functions accept arguments to specify bit positions as integer arrays. Those bit positions are specified in the little endian order. Values in bit positions are in the range \([0, \text{nIndexBits})\).
In order to represent bit strings, a pair of bitString and bitOrdering arguments are used. The bitString argument specifies bit string values as an array of 0 and 1. The bitOrdering argument specifies the bit positions of the bitString array elements in the little endian order.
In the following example, “10” is specified as a bit string. Bit string values are mapped to the 2nd and 3rd index bits and can be used to specify a bit mask, \(*\cdots *10*\).
int32_t bitString[] = {0, 1}
int32_t bitOrdering[] = {1, 2}
Supported data types¶
By default, computation is executed by the corresponding precision of the state vector, double float (FP64) for complex128 and single float (FP32) for complex64.
The cuStateVec library also provides the compute type, allowing computation with reduced precision. Some cuStateVec functions accept the compute type specified by using custatevecComputeType_t.
Below is the table of combinations of state vector and compute types available in the current version of the cuStateVec library.
State vector / cudaDataType_t |
Matrix / cudaDataType_t |
Compute / custatevecComputeType_t |
|---|---|---|
Complex 128 / CUDA_C_F64 |
Complex 128 / CUDA_C_F64 |
FP64 / CUSTATEVEC_COMPUTE_64F |
Complex 64 / CUDA_C_F32 |
Complex 128 / CUDA_C_F64 |
FP32 / CUSTATEVEC_COMPUTE_32F |
Complex 64 / CUDA_C_F32 |
Complex 64 / CUDA_C_F32 |
FP32 / CUSTATEVEC_COMPUTE_32F |
Note
CUSTATEVEC_COMPUTE_TF32 is not available at this version.
Workspace¶
The cuStateVec library internally manages temporary device memory for executing functions, which is referred to as context workspace.
The context workspace is attached to the cuStateVec context and allocated when a cuStateVec context is created by calling custatevecCreate(). The default size of the context workspace is chosen to cover most typical use cases, obtained by calling custatevecGetDefaultWorkspaceSize().
The extra workspace is user-managed device memory and required when the context workspace cannot provide enough amount of temporary memory or when a device memory chunk is shared by two or more functions. Functions that need the extra workspace have their sibling functions suffixed by _bufferSize(). If these functions return a nonzero value via the extraBufferSizeInBytes argument, users are requested to allocate a device memory and supply the pointer to the allocated memory to the corresponding function. The extra workspace should be 256-byte aligned, which is automatically satisfied by using cudaMalloc() to allocate device memory. If the size of the extra workspace is not enough, CUSTATEVEC_STATUS_INSUFFICIENT_WORKSPACE is returned. Please refer to custatevecApplyMatrix_bufferSize() and custatevecApplyMatrix() for examples.
Gate fusion¶
Gate applications account for large proportion of the computation cost in quantum simulators. We can reduce the overall memory footprint required in gate applications by fusing multiple gates into one larger gate.
cuStateVec API supports these general gate applications with multiple qubits.
For the detailed availability, please refer to custatevecApplyMatrix().