MACH-Q: Modular and Error-Aware Software Stack for Heterogeneous Quantum Computing Ecosystems
Topic 1 - Modular Software Stack
Wibe Albert de Jong, Lawrence Berkeley National Laboratory (Principal Investigator)
Mohan Sarovar, Sandia National Laboratories (Co-Principal Investigator)
Travis Humble, Oak Ridge National Laboratory (Co-Principal Investigator)
Lukasz Cincio, Los Alamos National Laboratory (Co-Principal Investigator)
Paul Hovland, Argonne National Laboratory (Co-Principal Investigator)
Fred Chong, University of Chicago (Co-Principal Investigator)
Gushu Li, University of Pennsylvania (Co-Principal Investigator)
Samah Saeed, City College of City University of New York (Co-Principal Investigator)
Weiwen Jiang, George Mason University (Co-Principal Investigator)

Recent major advances in the development of quantum computing technology are challenging current
computing paradigms and are changing the needs of the quantum computing software stack.
Taking full advantage of the diversity and complexity of next-generation quantum computers requires
a portable and easy-to-retarget quantum software stack to support the integration of critical
concepts with the rapidly evolving diverse quantum hardware landscape.

The proposed research of the MACH-Q project aims to navigate the intricacies of the heterogeneous
quantum computing environment by developing a versatile and expandable quantum software
component, poised to address the dynamic needs of next-generation quantum computers. MACH-Q
will develop modular, readily expandable, and error-aware quantum software capabilities that will
allow for plug-and-play deployment in emerging heterogeneous and distributed quantum computing
environments, and integration with third-party software. The open-source software MACH-Q proposes
to develop will support the evolving diversity in quantum computing, networking and HPC
capabilities, and will focus on supporting Department of Energy science applications. MACH-Q
will integrate approaches into the developed software modules that enable verification across the
software stack.

The MACH-Q team will be led out of LBNL by project Director Wibe Albert de Jong, with
Mohan Sarovar (Sandia) as the deputy, and integrate collaborative research efforts from a multidisciplinary
team of five Department of Energy laboratories (ANL, LANL, LBNL, ORNL, Sandia)
and four academic institutions (The University of Chicago, City College of City University of New
York, George Mason University, The University of Pennsylvania).

MACH-Q will build on the significant advances made by members of its team, developing a
quantum software stack under the AIDE-QC project. Modular components include the widely used
BQSKit synthesis toolkit, which has been demonstrated to scale and deliver near-optimal quantum
operation counts, the Quantum Intermediate Representation (QIR) specification adopted by industry,
the eXtreme-scale Accelerator programming framework (XACC), the SupermarQ benchmark
suite, now a de-facto industry standard for benchmarking machines and software, and the widely
used scikit-quant library of optimizers for noisy quantum systems. Our team has extensive experience
working on domain-specific science applications of QC for various DOE program offices and
a variety of industrial partners, which provide important guideposts toward practical and usable
software supporting scientific discovery. The team will closely collaborate with National Quantum
Initiative Centers, quantum testbeds, and industrial hardware platforms to enable co-design and
broad adoption of the MACH-Q software capabilities.