Overview
Parameterized Quantum Circuits (PQC) have attracted much research attention because of their potential to achieve quantum advantages on the current Noisy-Intermediate Scale Quantum (NISQ) computers. Quantum Neural Networks (QNN) for Quantum Machine Learning (QML) and Variational Quantum Eigensolver (PQC) for molecular dynamics are popular PQC examples. However, there are several challenges for current PQC research. Firstly, the library infrastructure for exploring PQC research is not fast and convenient enough, hindering the development of new techniques for PQC. Secondly, the quantum noise can severely degrade the PQC reliability. Thirdly, the design space for PQC circuit architecture (ansatz) is huge, and it is difficult to find a good one efficiently. The TorchQuantum library and the QuantumNAS framework, published in HPCA 2022, provide practical solutions to the them.
In this tutorial, we will first introduce the basic usage of the TorchQuantum--a general framework for PQC search, construction, training and deployment. All the operations for PQC circuit simulation are implemented with the PyTorch native operators to leverage the supports for GPU acceleration and automatic gradient computations to achieve fast PQC parameter training. We will introduce basic examples on how to construct and train QNN to perform image classification tasks and VQE circuit to estimate the ground state energy of molecules. Secondly, we will introduce the QuantumNAS framework to search for the most noise-robust circuit architecture efficiently. The search is performed with real hardware feedback in the loop to find the circuit most resilient to noise. Then, for highly scalable training of PQC, we will introduce how to train PQC on real quantum devices with the parameter shift rule. Finally we will introduce PQC compression which reduces the size of circuit by removing redundant operations and introduce the pulse-level support of TorchQuantum.
For each section, we will provide hands-on experience in implementing PQC and running on real quantum machines. We will also discuss the existing difficulties, and show our perspective of PQC, especially Quantum Chemistry and QML and in the NISQ era. All attendees will leave with code examples that they can leverage as the backbone implementation of their own research.
In this tutorial, we will first introduce the basic usage of the TorchQuantum--a general framework for PQC search, construction, training and deployment. All the operations for PQC circuit simulation are implemented with the PyTorch native operators to leverage the supports for GPU acceleration and automatic gradient computations to achieve fast PQC parameter training. We will introduce basic examples on how to construct and train QNN to perform image classification tasks and VQE circuit to estimate the ground state energy of molecules. Secondly, we will introduce the QuantumNAS framework to search for the most noise-robust circuit architecture efficiently. The search is performed with real hardware feedback in the loop to find the circuit most resilient to noise. Then, for highly scalable training of PQC, we will introduce how to train PQC on real quantum devices with the parameter shift rule. Finally we will introduce PQC compression which reduces the size of circuit by removing redundant operations and introduce the pulse-level support of TorchQuantum.
For each section, we will provide hands-on experience in implementing PQC and running on real quantum machines. We will also discuss the existing difficulties, and show our perspective of PQC, especially Quantum Chemistry and QML and in the NISQ era. All attendees will leave with code examples that they can leverage as the backbone implementation of their own research.
Schedule (Jun. 18 1-5 PM)
| Time | Topic | Speaker |
|---|---|---|
| TBD | Opening Remark | Prof. Song Han |
| TBD | Quantum Basics and TorchQuantum Usage | Hanrui Wang |
| TBD | A study on gradient computation circuits | Prof. Yongshan Ding |
| TBD | PQC compression | Prof. Weiwen Jiang |
| TBD | Noise-aware ansatz search and gate level optimization | Hanrui Wang |
| TBD | Noise-aware pulse level optimization | Zhiding Liang and Jinglei Cheng |
Registration
This tutorial will be held at the 50th International Symposium on Computer Architecture (ISCA 2023). Check the ISCA2023 site for more information. https://iscaconf.org/isca2023/
Event Location
Orlando World Center Marriott
8701 World Center Dr Orlando, FL 32821
Contact
Hanrui Wang (hanrui@mit.edu)