Quantum simulation framework based on PyTorch. It supports statevector simulation and pulse simulation (coming soon) on GPUs. It can scale up to the simulation of 25+ qubits with multiple GPUs.
Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, quantum neural networks.
Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tersorized processing.
- v0.1.2 Available!
- Join our Slack for real time support!
- Welcome to contribute! Please contact us or post in the forum if you want to have new examples implemented by TorchQuantum or any other questions.
- Qmlsys website goes online: qmlsys.mit.edu
- Easy construction and simulation of quantum circuits in PyTorch
- Dynamic computation graph for easy debugging
- Gradient support via autograd
- Batch mode inference and training on CPU/GPU.
- Easy deployment on real quantum devices such as IBMQ
- Easy hybrid classical-quantum model construction
- (coming soon) pulse-level simulation
pip install torchquantum
import torchquantum as tq
import torchquantum.functional as tqf
state = tq.QuantumState(n_wires=2)
state.h(wires=0)
state.cnot(wires=[0, 1])
tqf.h(state, wires=1)
tqf.x(state, wires=1)
# print the current state (dynamic computation graph supported)
print(state)import torchquantum as tq
import torchquantum.functional as tqf
x = tq.QuantumDevice(n_wires=2)
tqf.hadamard(x, wires=0)
tqf.x(x, wires=1)
tqf.cnot(x, wires=[0, 1])
# print the current state (dynamic computation graph supported)
print(x.states)We also prepare many example and tutorials using TorchQuantum.
For beginning level, you may check QNN for MNIST, Quantum Convolution (Quanvolution) and Quantum Kernel Method, and Quantum Regression.
For intermediate level, you may check Amplitude Encoding for MNIST, Clifford gate QNN, Save and Load QNN models, PauliSum Operation, How to convert tq to Qiskit.
For expert, you may check Parameter Shift on-chip Training, VQA Gradient Pruning, VQE, VQA for State Prepration.
Construct parameterized quantum circuit models as simple as constructing a normal pytorch model.
import torch.nn as nn
import torch.nn.functional as F
import torchquantum as tq
import torchquantum.functional as tqf
class QFCModel(nn.Module):
def __init__(self):
super().__init__()
self.n_wires = 4
self.q_device = tq.QuantumDevice(n_wires=self.n_wires)
self.measure = tq.MeasureAll(tq.PauliZ)
self.encoder_gates = [tqf.rx] * 4 + [tqf.ry] * 4 + \
[tqf.rz] * 4 + [tqf.rx] * 4
self.rx0 = tq.RX(has_params=True, trainable=True)
self.ry0 = tq.RY(has_params=True, trainable=True)
self.rz0 = tq.RZ(has_params=True, trainable=True)
self.crx0 = tq.CRX(has_params=True, trainable=True)
def forward(self, x):
bsz = x.shape[0]
# down-sample the image
x = F.avg_pool2d(x, 6).view(bsz, 16)
# reset qubit states
self.q_device.reset_states(bsz)
# encode the classical image to quantum domain
for k, gate in enumerate(self.encoder_gates):
gate(self.q_device, wires=k % self.n_wires, params=x[:, k])
# add some trainable gates (need to instantiate ahead of time)
self.rx0(self.q_device, wires=0)
self.ry0(self.q_device, wires=1)
self.rz0(self.q_device, wires=3)
self.crx0(self.q_device, wires=[0, 2])
# add some more non-parameterized gates (add on-the-fly)
tqf.hadamard(self.q_device, wires=3)
tqf.sx(self.q_device, wires=2)
tqf.cnot(self.q_device, wires=[3, 0])
tqf.qubitunitary(self.q_device0, wires=[1, 2], params=[[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1j],
[0, 0, -1j, 0]])
# perform measurement to get expectations (back to classical domain)
x = self.measure(self.q_device).reshape(bsz, 2, 2)
# classification
x = x.sum(-1).squeeze()
x = F.log_softmax(x, dim=1)
return xTrain a quantum circuit to perform VQE task. Quito quantum computer as in simple_vqe.py script:
cd examples/simple_vqe
python simple_vqe.pyTrain a quantum circuit to perform MNIST task and deploy on the real IBM Quito quantum computer as in mnist_example.py script:
cd examples/simple_mnist
python mnist_example.py| File | Description |
|---|---|
| devices.py | QuantumDevice class which stores the statevector |
| encoding.py | Encoding layers to encode classical values to quantum domain |
| functional.py | Quantum gate functions |
| operators.py | Quantum gate classes |
| layers.py | Layer templates such as RandomLayer |
| measure.py | Measurement of quantum states to get classical values |
| graph.py | Quantum gate graph used in static mode |
| super_layer.py | Layer templates for SuperCircuits |
| plugins/qiskit* | Convertors and processors for easy deployment on IBMQ |
| examples/ | More examples for training QML and VQE models |
- [HPCA'22] QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
- [DAC'22] QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
- [DAC'22] QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning
- [QCE'22] Variational Quantum Pulse Learning
- [ICCAD'22] Quantum Neural Network Compression
- [ICCAD'22] Graph Transformer for Quantum Circuit Reliability Prediction
- 3.9 >= Python >= 3.7 (Python 3.10 may have the
concurrentpackage issue for Qiskit) - PyTorch >= 1.8.0
- configargparse >= 0.14
- GPU model training requires NVIDIA GPUs
TorchQuantum Forum
Hanrui Wang hanrui@mit.edu
@inproceedings{hanruiwang2022quantumnas,
title = {Quantumnas: Noise-adaptive search for robust quantum circuits},
author = {Wang, Hanrui and Ding, Yongshan and Gu, Jiaqi and Li, Zirui and Lin, Yujun and Pan, David Z and Chong, Frederic T and Han, Song},
booktitle = {The 28th IEEE International Symposium on High-Performance Computer Architecture (HPCA-28)},
year = {2022}
}