Parallel iQCC Enables 200 Qubit Scale Quantum Chemistry on Accelerated Computing Platforms Surpassing Classical Benchmarks in Ruthenium Catalysts
Seyyed Mehdi Hosseini Jenab, Brandon Henderson, Scott N. Genin

TL;DR
This paper presents a GPU-accelerated parallel iQCC method enabling quantum chemistry simulations on 200+ qubits, surpassing classical benchmarks and avoiding barren-plateau issues, thus challenging assumptions about quantum advantage thresholds.
Contribution
The authors develop a scalable, GPU-accelerated parallel implementation of iQCC that allows simulation of large-scale quantum chemistry problems beyond previous classical capabilities.
Findings
Achieved over 100 qubit simulations of ruthenium catalysts
Surpassed classical methods like DMRG in accuracy and efficiency
Demonstrated potential quantum advantage beyond 50 qubits
Abstract
We introduce a parallel, GPU-accelerated implementation of the iterative qubit coupled cluster (iQCC) method that overcomes the exponential growth of the transformed Hamiltonian -- the principal bottleneck for classical emulation of quantum chemistry circuits. By distributing Hamiltonian terms across compute nodes via bit-wise partitioning and offloading Pauli contractions to GPUs, we achieve speedups exceeding two orders of magnitude over the serial CPU approach. Crucially, iQCC confines the variational evolution to a classically simulable operator subspace by selecting entanglers exclusively from the Direct Interaction Space, which guarantees non-vanishing energy gradients at every iteration and thereby naturally avoids the barren-plateau phenomenon that renders highly expressive quantum circuits untrainable. Leveraging these algorithmic and hardware advances, we simulate…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Machine Learning in Materials Science · Quantum many-body systems
