A Perspective on Quantum Computing Applications in Quantum Chemistry using 25--100 Logical Qubits

TL;DR

This perspective argues that the near-term fault-tolerant quantum era accessible with $25$--$100$ logical qubits offers a practical window to tackle intrinsically quantum chemistry problems that resist classical solutions. It outlines a co-design pathway combining embedding and downfolding to produce compact active-space Hamiltonians, measurement-efficient quantum algorithms (notably structured ansatzes and QPE variants), and hybrid quantum–classical workflows guided by AI and HPC. A robust benchmarking and validation program, including environment-aware protocols and cost-vector reporting, is proposed to ensure auditable progress across platforms. The authors advocate a pragmatic, collaborative roadmap that emphasizes open benchmarks, modular software/hardware ecosystems, and cross-disciplinary cooperation to achieve durable quantum utility in chemistry for catalysis, energy materials, and photochemistry.

Abstract

The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25--100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, for instance, multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.

Figures (4)

• Figure 1: Strategic directions for quantum chemical simulations organized by near-term (25--100 logical qubits) and long-term ($>$1000 logical qubits) scales. The near-term column reflects the scope of this Perspective and is grounded in the resource estimates of Table \ref{['tab:resources']}. The long-term column is included for context and is not the focus here. Strongly correlated systems are treated as near-term opportunities when approached via compact active spaces (Sec. \ref{['sec:oppo']}).
• Figure 2: Schematic complexity proxies vs. active-space size (orbitals). For FCI, we proxy complexity by determinant growth (exponential/combinatorial), anchored by the trillion-determinant C$_3$H$_8$/STO-3G calculation on 256 servers doi:10.1021/acs.jctc.3c01190. Selected CI/DMRG is shown with polynomial scaling and large prefactors, especially in 4c-relativistic DMRG-tailored CC doi:10.1021/acs.jctc.4c00641, where prefactors approach two orders of magnitude above the non-relativistic case. Quantum (QPE/VQE) uses gate/query complexity after factorization ($\sim\mathcal{O}(N^2)$); the shot budget, Eq. \ref{['eq:measure']}, is reported separately in the text. Normalizations are chosen so that within the 25–100 logical-qubit band (shaded) FCI is $\sim4$ orders above Selected CI/DMRG and the Selected CI/DMRG is $\sim2$ orders above QPE/VQE. Curves are qualitative but anchored to published points and scaling laws.
• Figure 3: Hybrid workflow schematic aligning with Sec. \ref{['sec:pathways']}. The central Quantum Kernel connects to four enabling areas: (i) hardware-aware design and hybrid devices, (ii) hybrid workflows and modular co-design, (iii) benchmark design and problem prioritization, and (iv) strategic roadmap and milestones. This structure illustrates how execution-level workflows and co-design infrastructure jointly enable scalable quantum chemistry simulations in the 25--100 logical-qubit regime.
• Figure 4: Capability-phased roadmap keyed to performance-based milestones (not calendar dates). Each phase is summarized by indicative logical-qubit counts and logical-gate depth and is evaluated with three metric bundles: QEC/hardware (code distance, logical error rate per cycle), compiler/runtime ($T$-factory/decoder throughput, end-to-end latency), and validation (resource-annotated benchmarks, stated error bars, stabilizer checkpoints). Benchmarks are defined in Sec. \ref{['sec:benchmark']}; validation workflow is discussed in Sec. \ref{['sec:roadmap']}