Center for Advanced Computing

Transforming basic-research into applications to enable technology advancements

Quantum Information Science

Quantum information science has been the focus of increasing research and development over the past decade. This includes federal investments of nearly $880 million by 2022 and is project to continue growing as accomplishments are achieved.

The Advanced Computing CoE is working on several projects in QIS, including quantum algorithms designed to leverage the strengths of quantum computing.

  • QIS Architecture
    • Novel Quantum Error Correcting (QEC) codes from quantum matter, topological order, graph states, LPDC codes
    • Hardware efficient QEC codes o Model Based System Engineering (MBSE) for QIS architecture
  • QIS Benchmarking
    • Quantum circuit optimization, tensor-network methods
    • Quantum algorithms for noisy quantum hardware
    • Quantum algorithms for applications to materials, chemistry, nuclear physics
  • QIS Hardware
    • Qubit characterization, e.g. ion traps, nuclear spin neutral atom
    • Noise modeling and characterization
    • Quantum control for increased coherence times
    • High precision laser manufacturing


  • Mathematical Methods for Quantum Subspace Diagonalization Algorithms

    Quantum mechanical systems form the foundational makeup of matter. Simulating a composition of many interacting quantum systems is classically intractable due to the long-range entanglement present across an exponentially large space of states. However, for many practical applications, such as in the optimization of lithium-ion batteries, and the development of novel materials for superconductors and fusion energy, the ability to simulate quantum many-body systems is of critical importance. Quantum computation has been proposed for the efficient simulation of quantum many-body systems, and a variety of quantum algorithms have been developed for solving the ground state problem. This project is focused on the quantum subspace diagonalization (QSD) algorithms that are a family of hybrid quantum-classical algorithms and may be suitable for deployment on near-term noisy quantum computers. The objective of this project is to develop mathematical methods for QSD algorithms; focusing on understanding and improving robustness, efficiency, and scalability of these algorithms. We acknowledge support from the U.S. Department of Energy, Office of Science, ASCR, DE-SC0023398.

  • Quantum Algorithms for Scattering Physics with Applications to Fusion Energy Science

    The collision processes and scattering physics of particles, atoms, and molecules are crucial to our understanding of the fundamental structure of matter. Fusion is the quantum scattering process and nuclear reaction that occurs when two light nuclei collide and fuse to create a single heavier nucleus with less mass than the two original nuclei. The leftover mass is converted to energy and is responsible for the generation of fusion energy. However, fusion reactions are complex and can have many scattering branches. For example, the deuterium-tritium (D-T) fusion reaction has two branches corresponding to a gamma or neutron emmision. Numerical simulation could serve to guide and accelerate the time to experimental discovery in quantum scattering. However, all known methods for classical simulation of quantum scattering, such as quantum Monte Carlo and tensor network methods, suffer from issues of scalability, and efficiency for general quantum many-body systems. The objective of this project is to demonstrate the potential application of quantum algorithms for simulating scattering physics with future applications to fusion energy science. As a first step, this project will perform quantum resource estimation for an accurate estimation of D-T gamma to neutron branching ratio.

  • Quantum Computing for Fusion Energy Materials

    General Atomics has collaborated with Argonne Leadership Computing Facility to work on this DoE funded program. The program set out to improve quantum computing algorithms so that they can be leveraged to improve materials development and understanding through simulation. Magnetic fusion systems have interactions between the plasma and the first wall material. In the ITER facility, this first wall will be beryllium, and quantum computing has the potential to advance detailed simulations of plasma-wall interaction chemistry. The scientific basis and simulation methods to leverage quantum computing to simulate this system have been advanced over the course of the project as noted in several key publications that resulted from this work. The progress made in this project supports the premise that quantum computation has the potential to revolutionize fusion energy materials chemistry once the needed algorithms have been explored and verified.

  • Fine Scale Metrology

    Investigation of ghost imaging techniques (both classical and quantum aspects) for application to metrology used to identify high-quality target capsules for Inertial Confinement Fusion (ICF). Potential to improve the acquisition time for X-ray imaging of capsules by an order of magnitude is possible through this novel approach. Correlated photons are used to increase signal to noise during data acquisition, thereby reducing the exposure time. The extent of the required correlation (including entanglement) as well as the specific requirements of the material (amorphous diamond and coatings) will be examined in detail, with attention paid to the associated trade-offs. For example, spatially correlated X-ray photons are much easier to achieve than fully entangled ones yet are expected to provide less contrast enhancement. Detection and characterization of individual capsules is crucial for obtaining symmetrical compression and ultimately obtaining high fusion yields.