QASMTrans

QASMTrans is a quantum transpiler for effectively parsing and translating general OpenQASM[1] circuits to circuits compiled for a particular NISQ device (e.g., from IBMQ, Rigetti, IonQ, Quantinuum), addressing the contraints of basis gates and qubit topology. QASMTrans is purely developed in C++ without external library dependency, facilitate deployment across platforms. It is specially designed for emerging deep circuits, such as those from HHL, QPE, quantum simulation, etc. QASMTrans is easy to extend for adding new optimization passes and backend devices (see extension). For some examplar QASM circuits, please check our QASMBench.

Please check our paper for details and performance: https://arxiv.org/pdf/2308.07581.pdf

Installation

To install the software, follow the steps below:

git clone https://github.com/pnnl/qasmtrans.git
cd qasmtrans
mkdir build
cd build
cmake ..
make

Execution

To run the transpiler, use the command below:

./qasmtrans -i ../data/test_benchmark/bv10.qasm -m ibmq -c ../data/devices/ibmq_toronto.json -v 1

Correctness verification

We also test our QASMTrans correctness, we use the same simulator from qiskit to test our transpiled qasm file and qiskit own transpiled file, we pass the file when the differences is less than 0.5%, to run the test simply run the command below:

cd test
sh validation_test.sh

All the detailed result will be stored in compare_summary

Options

QASMTrans command-line options:

-i: Input qasm file, e.g. data/test_benchmark/bv10.qasm
-o: Specify the output file location, the default path is data/output_qasm_file/{circuit}_{mode}.qasm.
-b: Sepcify basis gate set {x, y, z} (Future support)
-q: Take a qasm circuit string as input (Future support)
-m: Set the mode that determines the specific basis gate set for a vendor:
- ibmq : The basis gates for IBMQ here is [rz,sx,x,cx] (default)
- ionq: The basis gates for IonQ here is [rx(gpi),ry(gpi2),rz(gz),rxx(ms)]
- quantinuum : The basis gates for Quantinuum here is [U(&#03b8;,&#03d5;),rz,zz]
- rigetti : The basis gates for Rigetti here is [rx,ry,cz]
-c: Specify the backend device with certain topology. The path is "data/devices/"

IBMQ Machines (Heavy-hexagon):
- ibmq_toronto (27 qubits) (default option)
- ibmq_jakarta (7 qubits)
- ibmq_guadalupe (16 qubits)
- ibm_cairo (27 qubits)
- ibm_brisbane (127 qubits)
Rigetti Machine (Ring):
- aspen_m3 (80 qubits)
Quantinuum Machine (All-to-all connected):
- h1_2 (12 qubits)
- h1_1 (20 qubits)
Dummy Machines (All-to-all connected):
- dummy_ibmq12 (12 qubits)
- dummy_ibmq14 (14 qubits)
- dummy_ibmq15 (15 qubits)
- dummy_ibmq16 (16 qubits)
- dummy_ibmq30 (30 qubits)
-limited: Limit the number of qubits used (i.e., avoid using all physical qubits of the device). Due to more limited topology, more gates can be introduced. This option is particularly useful for numerical simulation on a classical system, given less qubits.
-v: Set the verbose level for debugging:
- 0 : No output (default)
- 1 : Output device_name, gate_ops, transpilation time, output file location
- 2 : Detailed information, including initial_mapping, transpilation time for different steps during the routing/mapping pass

Data Structure

The central data structure are:

Circuit: each transpilation pass takes in a circuit object and applies the logic of the pass to the circuit:

n_qubits: The total number of qubits in the circuit.
gates: A vector storing the gates (or quantum operations) applied within the circuit.

Gate: basic data structure for a gate:

op_name: This attribute specifies the type of gate. Examples include 'CX' (CNOT gate), 'Rz' (Pauli-Z rotation gate), etc.
ctrl: This defines the control qubit for controlled operations.
qubit: This represents the target qubit upon which the gate operation is applied.
theta/lambda/phi/gama: These are parameters representing the rotation angle (where applicable) for the gate operation.

External Files:

QASMTrans includes two external source header files:

lexer.hpp: Lexertk, a simple to use, easy to integrate and extremely fast lexicographical generator.
json.hpp: a C++ json operation library.

Developers:

Fei Hua, Pacific Northwest National Laboratory (Main developer)
Meng Wang, Pacific Northwest National Laboratory
Ang Li, Pacific Northwest National Laboratory

Thanks to Gushu Li (University of Pennsylvania) for sharing the Python source code of Sabre[2]).

Citation format:

Fei Hua, Meng Wang, Gushu Li, Bo Peng, Chenxu Liu, Muqing Zheng, Samuel Stein, Yufei Ding, Eddy Z. Zhang, Travis S. Humble, Ang Li. "QASMTrans: A QASM based Quantum Transpiler Framework for NISQ Devices." arXiv preprint arXiv:2308.07581 (2023)

Bibtex:

@misc{hua2023qasmtrans,
      title={QASMTrans: A QASM based Quantum Transpiler Framework for NISQ Devices}, 
      author={Fei Hua and Meng Wang and Gushu Li and Bo Peng and Chenxu Liu and Muqing Zheng and Samuel Stein and Yufei Ding and Eddy Z. Zhang and Travis S. Humble and Ang Li},
      year={2023},
      eprint={2308.07581},
      archivePrefix={arXiv},
      primaryClass={quant-ph}
}

References

[1] Cross, A.W., Bishop, L.S., Smolin, J.A., & Gambetta, J.M. (2017). Open quantum assembly language. arXiv preprint arXiv:1707.03429.
[2] Li, G., Ding, Y., & Xie, Y. (2019). Tackling the qubit mapping problem for NISQ-era quantum devices. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (pp. 1001-1014).(https://dl.acm.org/doi/abs/10.1145/3297858.3304023)

Acknowledgments

PNNL IPID: 32821-E, IR: PNNL-SA-188499, Export Control: EAR99, Software DOI: 10.11578/dc.20230814.4

This software is supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA) under contract number DE-SC0012704. The software is also supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Science Center (QSC). This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231. The Pacific Northwest National Laboratory is operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830.