Quantum C2C

Introduction

In a noisy intermediate-scale quantum (NISQ) era, quantum processors contain about 50 to a few hundred qubits. However, dimension of some classical data could be far larger. Hence, for a project of quantum computing (QC) or quantum machine learning (QML), data compression of classical data would be a must in a NISQ era.

In previous literatures related to QC or QML, most techniques of data compression of classical data are simple or non-parametric, e.g. downsampling image resolutions [1] or principal components analysis [2]. These may lose much information of original classical data. After performing dimension reduction, quantum encoding (quantum embedding) would be used to transform classical data into quantum data. Then quantum data go through a quantum circuit.

Based on universal approximation theorems, deep learning (DL) could approximate any function. Here, we hypothesized that DL would learn the optimized parameters to compress classical data for QC/QML. Information loss would be minimized during the data compression with deep learning. We argued that each quantum circuit would be suitable for different data compression models (both hyperparemeters and parameters). One could train and design different DL data compression model structures for several quantum circuits.

We first proposed to use Multitask Learning on QC/QML to simultaneously minimize the loss of autoencoder and loss of performance of QC/QML. Furthurmore, we developed a tool that one could perform end-to-end training with inputting classical data. Users could use the tool without complicated coding. Please see the document introducing Quantum C2C.

Methods

We proposed the methodology, Quantum C2C , as the following steps:

1. Splitting the data into training data and test data.
2. Using deep learning (autoencoder) to perform dimensionality reduction (pre-training).

3. Performing quantum encoding.

4. Inputting the quantum data into quantum circuit of QC/QML.

5. Performing quantum measurement.

6. Calculating the loss between the measurement result and true label and loss between reconstructed data and classical training data .

7. Repeating the above steps for several epochs, we could get the model with the minimized loss with optimization.

8. Using the proposed deep learning model to perform dimensionality reduction for the future classical data including test data.

Install

cd lib
python setup.py install
python test.py

If "Successful!" is shown lastly, it would be installed successfully.

Usage

1. Input:

a. X_train_autoencoder:

Classical Data for the autoencoder pre-training (step 2 in the Methods). The type is data type of PyTorch. X_train_autoencoder.data is data and X_train_autoencoder.targets is targets.

b. X_train:

Classical Data for main model training. The type is data type of PyTorch. X_train.data is data and X_train.targets is targets.

c. X_test:

Classical Data for main model testing. The type is data type of PyTorch. X_test.data is data and X_test.targets is targets.

d. autoencoder_model:

PyTorch model of autoencoder. Required variables are input_shape (shape of a input data) and encoded_len (length of encoded data). You could use a simple autoencoder by

from quantum_c2c.quantum_c2c import AutoEncoder

e. quantum_curcuit:

Qiskit's "Quantum-Classical Class" with PyTorch (Please see the details in link.) Required variables are encoded_len (length of encoded data). You could use a simple Quantum-Classical Class by

from quantum_c2c.quantum_c2c import Hybrid

The figure is the illustration of example for Hybrid (encoded_len=15)

f. saving_folder:

Folder name for saving the models "pretrained_autoencoder.pth" and "trained_encoder_model.pth".

g. epochs:

Epochs for the autoencoder pre-training and main model training. Default is 10.

h. encoded_len:

length of encoded data

2. Output:

a. model.encoder:

Return of the function. The last trained encoder model

b. y_predicted:

Return of the function. Predicted target values

c. pretrained_autoencoder.pth:

The file saved in saving_folder. Pretrained autoencoder.

d. trained_encoder_model.pth:

The file saved in saving_folder. Trained encoder model.

Example

You could view the example in the folder

Getting started:

Reference

[1] Jiang, Weiwen, Jinjun Xiong, and Yiyu Shi. "A co-design framework of neural networks and quantum circuits towards quantum advantage." Nature communications 12.1 (2021): 1-13.

[2] Wu, Sau Lan, et al. "Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the lhc on ibm quantum computer simulator and hardware with 10 qubits." Journal of Physics G: Nuclear and Particle Physics (2021).

[3] https://qiskit.org/textbook/ch-machine-learning/machine-learning-qiskit-pytorch.html

Contributor

Chih-Han (Robin) Hunag (Team Leader/Co-First Author)

Tsai-Min (Simon) Chen (Co-First Author)

Khushwanth Kumar

Sumitra Pundlik

Vardaan Sahgal

Tathagata Majumdar

Turku Tatar