Quantum computing could accelerate computational tasks in materials science, according to a paper by over 50 authors from a range of companies ranging from IBM to Infleqtion to Phasecraft and some of the most prominent Quantum Labs and supercomputing centers from around the planet. Published in ArXiv: “Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions” explores the use cases for Quantum Computing in Material Science.
“Materials science use cases are being investigated as some of the first applications of quantum computing to show practical quantum advantages. The strongest motivation for this belief is that a great deal of materials science problems are quantum in nature. A computational advancement in the field will have consequences in many areas, from scientific exploration to industrial advancements and sustainability.”
Current high-performance supercomputing centers dedicate significant resources to materials science computations, but these can become unmanageable as the size of quantum systems increases. Quantum computers could offer a solution, as many quantum algorithms avoid the exponential memory overheads of classical computations. The paper suggests that quantum-centric supercomputing could help address computational problems in materials science but also highlights the challenges that need to be overcome to achieve practical quantum advantage.
Computational models are crucial for designing, characterizing, and discovering novel materials. However, the complex computational tasks in materials science push the boundaries of existing high-performance supercomputing centers. Quantum computing, an emerging technology, can accelerate many of these computational tasks. Quantum technology must interact with conventional high-performance computing in several ways, including validating approximate results, identifying hard problems, and creating synergies in quantum-centric supercomputing. This paper provides a perspective on how quantum-centric supercomputing can help address critical computational issues in materials science, the challenges to face, and new suggested directions.
Quantum Computing Applications in Materials Science
Cases of material science use are being investigated as some of the first applications of quantum computing to show practical quantum advantages. This is because many materials science problems are quantum. A computational advancement in the field will have consequences in many areas, from scientific exploration to industrial upgrades and sustainability. Quantum computers present an attractive alternative, as many quantum algorithms avoid the exponential memory overheads of classical quantum matter computations.
Critical algorithms for materials science applications are identified, considering practical applications and potential for quantum advantage. The requirements for quantum-centric supercomputing architectures are discussed, including evaluating the computational and operational demands, scalability, and integration hurdles of deploying these algorithms in combined quantum-classical high-performance computing environments.
“In this paper, we motivate that quantum-centric supercomputing is critical for materials science research and industrial development, we identify the challenges ahead to achieve practical quantum advantage on these use cases and propose some directions to address them.”
Specific use cases in materials science where quantum and high-performance computing algorithms can be most effectively utilized are highlighted. The use case must be classically hard in some limit, amenable to execution on a noisy or fault-tolerant quantum computer, and represent an interesting problem in materials science.
Quantum algorithms that can be used for materials science applications are summarized. These include algorithms that simulate quantum systems and quantum computing approaches to simulate partial differential equations (PDEs). The simulation of quantum systems is the most natural application of quantum computers in the materials science space.
Classical Information Processing in Quantum-Classical Workflows
To simulate a physical quantum system, the governing physical Hamiltonian must be appropriately mapped to a qubit Hamiltonian such that the quantum computer faithfully simulates the physical system of interest. This involves deciding how to encode a nonnegative integer representing the occupation of the mode into qubits.
“One major takeaway from this perspective is to suggest how we can lay the grounds to think about quantum computing to work in synergy with classical high-performance computing in quantum-centric supercomputing centers. Materials science provides a great setting for use cases, which have potential for quantum advantage for scientific and industrial applications.”
Summary
Quantum computing has the potential to significantly accelerate computational tasks in materials science, a field that currently pushes the limits of existing high-performance supercomputing centres. However, to achieve this, quantum technology must effectively interact with conventional high-performance computing, facing challenges such as validating approximate results, identifying complex problems, and creating synergies in quantum-centric supercomputing.
- Quantum computing has the potential to accelerate computational tasks in materials science, which currently stretch the limits of existing high-performance supercomputing centres.
- Quantum computers could help to address critical computational problems in materials science, but they must interact with conventional high-performance computing in several ways.
- Quantum-centric supercomputing is critical for materials science research and industrial development.
- Quantum algorithms for materials science applications have been identified, with potential for quantum advantage.
