Scalable Design-Program-Compilation Optimizations for Quantum Algorithms

Tutorial at DAC’22

Abstract

The fast growing on the number of qubits in recent years has brought new challenges in designing, programming, synthesizing, and mapping applications to quantum computers at scale: (1) how to synthesize and map the logical circuit to physical qubits; (2) how to program applications to adapt to quantum computing; (3) how to design a quantum circuit for Q learning with Q advantage?

This tutorial is composed of three sessions to address all these issues. All attendees will leave with code examples that they can use as the backbone implementation to their own projects

Tutorial Sessions

Session 1: Qubit Mapping and Scheduling: Gap Analysis and Optimal Solutions

Researchers from University of California, Los Angeles (UCLA) will introduce the quantum mapper， namely OLSQ. OLSQ refers to the optimal layout synthesis which maps a quantum program to a real quantum computer under its hardware constraints.

Speakers

Jason Cong, cong@cs.ucla.edu

Bochen Tan, bctan@cs.ucla.edu

Resources

[Slides]

[Colab]

https://github.com/UCLA-VAST/OLSQ (Source Code of OLSQ)
https://arxiv.org/pdf/2007.15671.pdf (OLSQ)
https://arxiv.org/pdf/2109.06445.pdf (OLSQ-GA)
https://arxiv.org/pdf/2002.09783.pdf (QUEKO)

Session 2: Enabling Deeper Quantum Compiler Optimization at High Level

Researchers from University of California, Santa Barbara (UCSB) will introduce the quantum compiler, namely Paulihedral. Paulihedral is a block-wise compiler framework that can deeply optimize the quantum simulation kernels.

Speakers

Yufei Ding, yufeiding@cs.ucsb.edu

Gushu Li, gushuli@ece.ucsb.edu

Resources

[Slides]
https://zenodo.org/record/5780204 (Paulihedral Artifact)
https://dl.acm.org/doi/pdf/10.1145/3503222.3507715 (Paulihedral)
https://dl.acm.org/doi/abs/10.1145/3470496.3527381 (QEC Compiler)

Session 3: Towards Quantum Learning Democratization —— Start from Building a Quantum Neural Network Design Stack

Researchers from George Mason University (Mason) will introduce the quantum ML design, namely QuantumFlow. QuantumFlow focuses on design and optimize quantum learning algorithms toward near-term quantum computers.