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Quantum Performance Lab (QPL)

QPL develops and deploys cutting-edge techniques for assessing the performance of quantum computing hardware, serving the needs of the U.S. government, industry, and academia. The research from the QPL produces:

  • insight into the failure mechanisms of real-world quantum computing processors
  • well-motivated metrics of low- and high-level performance
  • predictive models of multi-qubit quantum processors
  • concrete tested protocols for evaluating as-built experimental processors

QPL also supports the open-source pyGSTi software package which provides an extensive suite of cutting edge tools and algorithms for evaluating individual qubits and many-qubit processors. PyGSTi is the reference implementation for gate set tomography (GST), and also integrates implementations of many other protocols for characterizing noise and errors in qubits, and quantifying their performance. In addition to our R&D capabilities, the QPL also provides quantum hardware assessment capabilities directly to DOE and the agencies.

For more information contact: qpl@sandia.gov.


The Quantum Scientific Computing Open User Testbed (QSCOUT)

Qscout is a 5 year DOE program to build a quantum testbed based on trapped ions that is available to the research community. As an open platform, it will not only provide full specifications and control for the realization of all high level quantum and classical processes, it will also enable researchers to investigate, alter, and optimize the internals of the testbed and test more advanced implementations of quantum operations.

QSCOUT will be made operational in stages, with each stage adding more ion qubits, greater classical control, and improved fidelities. We want to collaborate with the broad quantum computing community and Call for Proposals will be forthcoming.

For more information contact qscout@sandia.gov.


OverQC_logo.pngOptimization, Verification, and Engineered Reliability of Quantum Computers (OVER-QC)

Over-QC is a multi-institution research project funded by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under the Quantum Computing Application Teams program.

Quantum technologies, especially quantum computers, show great promise for revolutionizing high-performance computing and simulation. As prototype quantum computers come online, it is becoming clear that obtaining useful output from such devices will require layers of sophisticated classical software that provide interpretation and analysis of the quantum computer's state and output.

OVER-QC will develop critical components in this software stack with a particular focus on enabling near-term Noisy Intermediate-Scale Quantum (NISQ) technologies. We have identified three critical needs for near-term quantum computing platforms, and the project is structured around the following thrusts:

  1. Thrust 1- To develop capabilities to verify and certify translations of abstract quantum algorithms to quantum circuit programs.
  2. Thrust 2- To develop tools to identify the reliable information that can be extracted from near-term devices in the absence of fault-tolerant operation.
  3. Thrust 3 - To develop interfaces for variational hybrid quantum-classical processors (VHPs) that enable applications by connecting classical algorithms to quantum co-processors.

For more information please contact Mohan Sarovar.


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Quantum Optimization and Learning and Simulation (QOALAS)

QOALAS is funded by the DOE Quantum Algorithms Teams program to explore the abilities of quantum computers in three interrelated areas: quantum simulation, optimization, and machine learning. We leverage connections among these areas and unearth deeper ones to fuel new applications of quantum information processing to science and technology.

QOALAS goals:

  1. Aim to develop quantum algorithms for quantum simulation with improved performance and an expanded scope. We also work extensively on applying quantum computation to problems in high-energy physics, where much work remains to develop simulations of general field theories.
  2. Seek to understand how quantum resources may be used to improve the quality of solutions over those produced by classical algorithms. We devise new quantum algorithms for approximating classical discrete optimization problems and ground states of physically motivated quantum Hamiltonians. Our approach is naturally amenable to a hybrid quantum/classical implementation.
  3. Investigate quantum algorithms for decomposing tensors, which are ubiquitous in machine learning. We also explore generalizations of previous work on applications of Gibbs sampling to learning and quantum algorithms for learning from structured data.

For more information contact Ojas Parekh.

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Fundamental Algorithmic Research for Quantum Computing (FAR-QC)

The FAR-QC project is made possible by research and collaboration of DOE National Laboratories, universities, and industry affiliates working together to advance quantum and classical capabilities in quantum simulation, optimization, and machine learning.

FAR-QC’s research effort is realized through three capability thrusts that are complemented by a cross-cutting Theory and Practice Interface thrust, which facilitates close interactions and synergy amongst the capability thrusts. This collaboration structure is built to help transcend institutional silos and maximize the effectiveness of the team.

For more information contact Ojas Parekh.

Advancing Integrated Development Environments for Quantum Computing through Fundamental Research (AIDE-QC)

Sandia is a partner in the Lawrence Berkeley National Lab-led, multi-institution R&D project.  The goal of AIDE-QC is to develop and deliver open-source computing, programming, and simulation environments that support the large diversity of quantum computing (QC) research at DOE. The project will address critical aspects of computer software environments to accelerate the application of near-term, intermediate-scale quantum computers for scientific exploration. These advances include high-level programming languages accessible by domain scientists, novel optimization techniques for variational programs, and leading-edge, platform-agnostic compilers supported by classical numerical simulators and robust tools for validation, verification and debugging.

Sandia leads the Verification and Debugging thrust of AIDE-QC. The goals of this thrust are:

  1. Development of new techniques for noisy program verification and automated certification;
  2. Development of techniques for online program verification for variational computations;
  3. Development of online debugging tools and methods for quantum computers.
For more information contact Mohan Sarova