Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

TL;DR

This perspective addresses the challenge of scaling ab initio materials simulations by arguing for quantum-centric supercomputing integrated with classical HPC. It surveys fundamental quantum algorithms for dynamics, ground-state, and open-system problems, and details how quantum workflows interact with classical resources via mappings, encodings, measurements, data loading (QRAM), circuit design, and real-time processing. It further elaborates workload management, middleware, and programming models necessary to operate hybrid quantum-classical systems at scale, and discusses classical simulation as a verification and planning tool. By identifying use cases such as ground-state electronic structure, embedding, model systems, and vibrational/excited-state properties, the paper outlines concrete paths toward practical quantum advantage in materials science and emphasizes the need for cross-layer collaboration among algorithms, architectures, and workflows.

Abstract

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

Figures (15)

• Figure 1: $(a)$ An illustration of QRAM made by quantum routers. $(b)$ An illustration of QROM. $(c)$ Hybrid QRAM-QROM architecture.
• Figure 2: QRAM as a binary tree of $\text{Q}^2$ routers. Here, $\text{Q}^2$ router refers to the router with both control and signal states being quantum states.
• Figure 3: $(a)$ Self-similar fractal structure of the H-tree designs (see xu2023systems). One can use the self-similar structure to create a size $2^{n+2}$ QRAM from 4 units of size $2^n$ QRAM. $(b)$: An example of depth-4 tree with $2^4=16$ leaves.
• Figure 4: Example SWAP network for $n=6$ qubits. In $O(n)$ steps, each of the $O(n^2)$ pair of qubits (colors) performs an interaction, even with just linear connectivity.
• Figure 5: Two circuits implementing the same sequence $R_{\operatorname{YY}}(\theta_3)\cdot R_{\operatorname{XZ}}(\theta_2)\cdot R_{\operatorname{XX}}(\theta_1)$.