Accelerate MPS simulator by using cuQuantum

[doichanj]

What is the expected behavior?

MPS
simulation method (https://github.com/Qiskit/qiskit-aer/tree/main/src/simulators/matrix_product_state) becomes important to simulate circuits with large number of qubits. Currently MPS simulation method only supports device=CPU and it takes long time to simulate circuits with large number of qubits.

cuQuantum (https://developer.nvidia.com/cuquantum-sdk) is a SDK for Quantum computing that accelerates simulation on NVIDIA's GPUs. cuQuantum has APIs for statevector simulation (cuStateVec) and tensor network simulation (cuTensorNet) on GPUs, and Aer currently supports cuQuantum in method=statevector and method=tensor_network but we do not have for MPS method.

MPS method can be accelerated by using cuTensorNet and there are some code examples here https://docs.nvidia.com/cuda/cuquantum/latest/cutensornet/examples.html

[MozammilQ]

anyone trying this one? :)

[Randl]

So if I understand correctly, what is required is to implement additional set of MPS_Tensor methods

qiskit-aer/src/simulators/matrix_product_state/matrix_product_state_tensor.hpp

Line 50 in 9bfd110

class MPS_Tensor {

that utilize cuQuantum, similarly to, for example, tensor network contractor? I think it could be nice to discuss the details of architecture and design before starting tackling this issue.

For cuQuantum side, this example that uses cutensornetStateApplyTensorOperator and cutensornetStateFinalizeMPS probably should be a good place to start building the relevant routines.

[doichanj]

I think Decompose is one of the most time consuming kernel that can be accelerated by cuTensornet.

qiskit-aer/src/simulators/matrix_product_state/matrix_product_state_tensor.hpp

Lines 593 to 614 in 9bfd110

	double MPS_Tensor::Decompose(MPS_Tensor &temp, MPS_Tensor &left_gamma,
	rvector_t &lambda, MPS_Tensor &right_gamma,
	bool mps_lapack) {
	cmatrix_t C;
	C = reshape_before_SVD(temp.data_);
	cmatrix_t U, V;
	rvector_t S(std::min(C.GetRows(), C.GetColumns()));
	csvd_wrapper(C, U, S, V, mps_lapack);
	double discarded_value = 0.0;
	discarded_value = reduce_zeros(U, S, V, max_bond_dimension_,
	truncation_threshold_, mps_lapack);
	left_gamma.data_ = reshape_U_after_SVD(U);
	lambda = S;
	if (mps_lapack) { // When using Lapack V is V dagger
	right_gamma.data_ = reshape_VH_after_SVD(V);
	} else {
	right_gamma.data_ = reshape_V_after_SVD(V);
	}
	return discarded_value;
	}

There is a wrapper implementation of SVD by using LAPACK in svd.cpp I think we can add wrapper for GPU

[MozammilQ]

@Randl , I was working on this issue., and svd is my target thing since last night.
But, its true that I have to learn cuQuantum, and your previous comment shows you have a better idea of cuQuantum.
Now, who's going to do the PR.
Waiting for you comment :)
I will do as you say :)

[Randl]

Go on, feel free to work on it if you're already in progress.

[MozammilQ]

There are some issues in MacOS CI test.
and, my PR is having some performance issues,
So, It would be good if my PR is not merged now.
Anyways, I will continue to work on the PR, until the performance issue is solved! :)