Performance benchmarks of quantum simulators

There has been a rapid rise in the development of quantum simulators, both to validate the quantum hardware and also to explore the limitations of classical simulation, thereby the regime of quantum advantage. Quantum simulators which are HPC (High Performance Computing) compliant are chosen and their performance is benchmarked on various compute capabilities as offered by the HPC.

Notebooks provide the Time to Solution (TtS) performance of the quantum simulators obtained using a containerized toolchain wherein each simulation package accepts the quantum algorithm in the QASM2 format, the simulation package and the compute capability on the HPC. The containerized toolchain allows for portability of the benchmarking scheme, reproducibility of the performance data, is modular and easily extensible to include other packages.

The benchmarked packages include:

#	Package	Language	Singlethread		Multithread		GPU		Multi-GPU
			Single Precision	Double Precision	Single Precision	Double Precision	Single Precision	Double Precision	Single Precision	Double Precision
1	Qiskit	Python
2	Cirq	Python
3	Qsimcirq	Python
4	Pennylane	Python
5	Pennylane-lightning	C++
6	Qibo	Python
7	Qibojit	Python
8	Yao	Julia
9	Quest	C
10	Qulacs	Python
11	Intel-QS	C++
12	Projectq	Python
13	Qcgpu	Python
14	HiQ	Python
15	Hybridq	Python
16	SV-Sim	Python
17	Qrack	C++
18	Qpanda	Python
19	CuQuantum	Python
20	myQLM (py)	Python
21	myQLM (C++)	C++
22	Braket	Python
23	Q++	C++

The quantum algorithms benchmarked include:

Dynamics of the Heisenberg model
Random Quantum Circuit as in the Google Sycamore quantum advantage experiment
Quantum Fourier Transform