QCCC-22: The First International Workshop on Quantum Classical Cooperative Computing

May 30, 2022, Virtual.


Held in conjunction with the The 36th IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 30, 2022, Virtual.


The past five years have seen tremendous development in quantum computing technology, remarked by the demonstration of quantum supremacy. Although many quantum algorithms declare exponential speedups over their classical counterparts, today’s quantum devices in the noisy-intermediate scale quantum (NISQ) era are very susceptible to environmental noise, internal interference, manufacturing imperfection, and technology limitation. Consequently, quantum algorithms that are more robust to noise, or can be effectively decomposed into small pieces for incremental or parallel quantum execution become promising.

The purpose of this workshop is to explore innovative ways of quantum-classical cooperative computing (QCCC) to make quantum computing more effective and scalable in NISQ platforms. The workshop will focus heavily on how classical computing can improve NISQ device execution efficiency, scalability, or compensate for noise impact or technology deficiency, with particular emphasis on demonstrable approaches on existing NISQ platforms, such as IBM-Q, IonQ and Rigetti.

 Download the QCCC-22 CFP

Workshop Program

5/30/2022 10:20 to 10:25 AM ET

Workshop Openning

Ang Li and Qiang Guan

5/30/2022 10:25 to 11:25 AM ET

Keynote: Hybrid Quantum / Classical Algorithms for Machine Learning

Prof. Nathan Wiebe, University of Toronto

11:30 to 12:00 PM ET

Talk-1: Methods and Results for Quantum Optimal Pulse Control on Superconducting Qubit Systems

Elisha Siddiqui Matekole, Brookhaven National Laboratory

12:00 to 12:30 PM ET

Talk-2: Locality-aware Qubit Routing for the Grid Architecture

Avah Banerjee, Missouri University of Science and Technology

12:30 to 13:00 PM ET

Talk-3: SQCC: Smart Quantum Circuit Cutting

Betis Baheri, Kent State University

13:00 to 13:30 PM ET

Talk-4: Improving Variational Quantum Algorithms performance through Weighted Quantum Ensembles

Samuel Stein, Pacific Northwest National Laboratory

13:30 to 14:00 PM ET

Talk-5: Quantum Processor Performance through Quantum Distance Metrics Over An Algorithm Suite

Samuel Stein, Pacific Northwest National Laboratory

13:40 PM to 14:45 PM ETWorkshop Closing Comments



Hybrid Quantum / Classical Algorithms for Machine Learning



Prof. Nathan Wiebe, University of Toronto


In this talk I will provide a new approach to quantum machine learning that involves using classical machine learning to learn a representation for a dataset that can be embedded in a quantum computer. We will then consider applying this strategy to train a generative model for groundstates of chemistry Hamiltonians that will allow us to predict groundstates given data through a classically learnt representation that converts nuclear positions into weights for a quantum neural network that generates the state. This work shows that quantum / classical Hybrid methods can be a powerful way to learn how to generate groundstates and potentially even give a cheaper alternative to approximate groundstate preparation than phase estimation provides in some settings.

Speaker Bio

Nathan Wiebe is a researcher in quantum computing who focuses on quantum methods for machine learning and simulation of physical systems. His work has provided the first quantum algorithms for deep learning, least squares fitting, quantum simulations using linear-combinations of unitaries, quantum Hamiltonian learning, near-optimal simulation of time-dependent physical systems, efficient Bayesian phase estimation and also has pioneered the use of particle filters for characterizing quantum devices as well as many other contributions ranging from the foundations of thermodynamics to adiabatic quantum computing and quantum chemistry simulation. He received his PhD in 2011 from the university of Calgary studying quantum computing before accepting a post-doctoral fellowship at the University of waterloo and then finally joining Microsoft Research in 2013. In 2019 he left Microsoft to accept a joint appointment at the university of Washington and Pacific Northwest National Labs. He is now an assistant professor in University of Toronto.


Topics of interest for this workshop include, but not limited to:

Topics that are not relevant include pure quantum or pure classical algorithm/hardware design, benchmarking of quantum algorithm/devices.


This workshop is part of IEEE International Parallel and Distributed Processing Symposium (IPDPS 2022) scheduled from May 30 to June 3, 2022 Virtually.

Important Dates


Authors are invited to submit original 2-page (double-column) extended abstracts. The deadline is Friday, Feb 25, 2022. Accepted papers will be invited to submit a full paper for the workshop proceedings to be published in the IEEE Digital Library as part of the IPDPS-22 workshop proceedings.

Full papers for the workshop proceedings will be due Tuesday, March 15, 2022. The full paper is limited to 6 pages without references. Please use the IEEE Conference Proceedings format for your submissions: IEEE Template.

Accepted papers will be given 15 mins to present in the workshop.

Papers are to be submitted electronically through Easychair at Here.

Workshop Chair

Program Committee