qdao is a quantum data access optimization framework. It leverages secondary storage to simulate large scale quantum circuits and minimizes the data movement between memory and secondary storage. The memory requirement of full state quantum circuit simulation grows exponentially with the number of qubits. For example, on a typical PC, simulate a circuit with more than 30 qubits can easily result in out-of-memory error. With qdao, the memory occupation of simulation is completely under your control.

git clone https://github.com/Zhaoyilunnn/qdao.git
cd qdao
pip install .

pytest tests/

The following code snippet shows the basic usage of qdao. You can configure the num_primary parameter to reduce the memory occupation.

$\text{memory consumption} = 2^{\text{num-primary}} * 16 \text{ Byte}$

from qdao import Engine
from qiskit.circuit.random import random_circuit
from qiskit import transpile
from qiskit_aer import Aer

num_qubits = 12
num_primary = 10
num_local = 8

# Create a qiskit quantum circuit `circ`
circ = random_circuit(num_qubits, 10, measure=False, max_operands=2)
backend = Aer.get_backend("aer_simulator")
circ = transpile(circ, backend=backend)

# `num_primary`: size of a compute unit
# `num_local`: size of a storage unit
eng = Engine(circuit=circ, num_primary=num_primary, num_local=num_local)
eng.run()

To use GPU for simulation and use host memory to store the entire statevector, try following configurations.

First you need to install qiskit-aer-gpu. Please refer to the official document.

eng = Engine(circuit=circ, num_primary=num_primary, num_local=num_local, sv_location="memory", device="GPU")

You can specify a backend simulator by using backend option, currently only qiskit and pyquafu are supported.

# First transform qiskit circuit to a quafu circuit
from quafu import QuantumCircuit
quafu_circ = QuantumCircuit(1)
quafu_circ.from_openqasm(circ.qasm())

# Create a new engine using quafu backend
eng = Engine(circuit=quafu_circ, num_primary=num_primary, num_local=num_local, backend="quafu")
eng.run()

We're working on to support measurement in qdao, currently please obtain state vector after simulation as follows.

from qdao.util import retrieve_sv
res = retrieve_sv(num_qubits, num_local=num_local)
print(res)

If you find our work useful, please kindly cite our paper as below.

@inproceedings{qdao2023,
  title={Full State Quantum Circuit Simulation Beyond Memory Limit},
  author={Zhao, Yilun and Chen, Yu and Li, He and Wang, Ying and Chang, Kaiyan and Wang, Bingmeng and Li, Bing and Han, Yinhe},
  booktitle={2023 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)},
  year={2023},
  organization={IEEE}
}

See the CONTRIBUTING.md.

There are some key features to be supported in the future

• GPU simulation
• Noisy simulation

Please file an issue or contact [email protected] if you encounter any problems.

Setting smaller memory requirement leads to larger performance overhead. This can be more severe when using qiskit backend as setting initial statevector in qiskit incurs additional data copy, this problem is avoided in pyquafu, although pyquafu is slower than qiskit.