gogog01-29-2021/torchquantum-20240322

A PyTorch-based framework for Quantum Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.

A PyTorch Library for Quantum Simulation and Quantum Machine Learning

Faster, Scalable, Easy Debugging, Easy Deployment on Real Machine

👋 Welcome

What it is doing

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 30+ qubits with multiple GPUs.

Who will benefit

Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, quantum neural networks.

Differences from Qiskit/Pennylane

Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tersorized processing.

News

• Added support for controlled unitary
• 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

Features

• 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

Installation

pip install torchquantum

Basic Usage 1

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)
print(tq.measure(state, n_shots=1024))

Basic Usage 2

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)

Guide to the examples

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.

Usage

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 x

VQE Example

Train a quantum circuit to perform VQE task. Quito quantum computer as in simple_vqe.py script:

cd examples/simple_vqe
python simple_vqe.py

MNIST Example

Train 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

Files

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

Papers using TorchQuantum

• [HPCA'22] Wang et al., "QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits"
• [DAC'22] Wang et al., "QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization"
• [DAC'22] Wang et al., "QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning"
• [QCE'22] Liang et al., "Variational Quantum Pulse Learning"
• [ICCAD'22] Hu et al., "Quantum Neural Network Compression"
• [ICCAD'22] Wang et al., "QuEst: Graph Transformer for Quantum Circuit Reliability Estimation"
• [ICML Workshop] Yun et al., "Slimmable Quantum Federated Learning"
• [IEEE ICDCS] Yun et al., "Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design"

Manuscripts

Manuscripts

• Yun et al., "Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification"
• Baek et al., "3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications"
• Baek et al., "Scalable Quantum Convolutional Neural Networks"
• Yun et al., "Quantum Multi-Agent Meta Reinforcement Learning"

Dependencies

• 3.9 >= Python >= 3.7 (Python 3.10 may have the concurrent package issue for Qiskit)
• PyTorch >= 1.8.0
• configargparse >= 0.14
• GPU model training requires NVIDIA GPUs

Contact

TorchQuantum Forum

Hanrui Wang [email protected]

Contributors

Hanrui Wang, Jiannan Cao, Jessica Ding, Jiai Gu, Song Han, Zirui Li, Zhiding Liang, Pengyu Liu, Yilian Liu, Mohammadreza Tavasoli

Citation

@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}
}