Parallel iQCC Enables 200 Qubit Scale Quantum Chemistry on Accelerated Computing Platforms Surpassing Classical Benchmarks in Ruthenium Catalysts

• URL: http://arxiv.org/abs/2603.08883v1
• Date: Mon, 09 Mar 2026 19:49:37 GMT
• Title: Parallel iQCC Enables 200 Qubit Scale Quantum Chemistry on Accelerated Computing Platforms Surpassing Classical Benchmarks in Ruthenium Catalysts
• Abstract summary: We introduce a parallel, GPU-accelerated implementation of the iterative qubit cluster (iQCC)<n>We achieve speedups exceeding two orders of magnitude over the serial CPU approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 electronic-structure Hamiltonians for industrially relevant ruthenium catalysts in the 100--124 qubit regime, completing full ground-state calculations on NVIDIA GPUs in the ranges of 1.2 - 45 hrs and surpassing the accuracy of Density Matrix Renormalization Group. These results effectively de-quantize a significant portion of the NISQ roadmap: quantum advantage for chemistry is often assumed to emerge beyond ${\sim}50$ qubits, yet our work demonstrates that this frontier lies significantly further -- potentially past 200 qubits -- reshaping expectations for where genuine quantum advantage may first appear.

Related papers

• First-principle predictions of fragmentation functions via quantum computing [0.0]
  We report on an algorithm to compute fragmentation functions from the first principles Quantum Chromodynamics (QCD) Hamiltonian quantized in Light-Front Gauge.<n>We run a demonstration of a heavy-quark leading parton fragmenting into quarkonium, which we benchmark against NRQCD computations.
  arXiv  Detail & Related papers  (2026-08-31T07:31:03Z)
• Towards Quantum Advantage in Chemistry [0.70661060006164]
  We execute the iterative qubit coupled-cluster algorithm on fault-tolerant quantum hardware.<n>We show iQCC achieves the lowest mean absolute error (0.05 eV) and highest R$2$ (0.94) relative to experiment.<n>We find these systems remain classically tractable up to $sim$200 logical qubits.
  arXiv  Detail & Related papers  (2025-12-15T18:49:30Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• QAMA: Scalable Quantum Annealing Multi-Head Attention Operator for Deep Learning [48.12231190677108]
  Quantum Annealing Multi-Head Attention (QAMA) is proposed, a novel drop-in operator that reformulates attention as an energy-based Hamiltonian optimization problem.<n>In this framework, token interactions are encoded into binary quadratic terms, and quantum annealing is employed to search for low-energy configurations.<n> Empirically, evaluation on both natural language and vision benchmarks shows that, across tasks, accuracy deviates by at most 2.7 points from standard multi-head attention.
  arXiv  Detail & Related papers  (2025-04-15T11:29:09Z)
• Challenging the Quantum Advantage Frontier with Large-Scale Classical Simulations of Annealing Dynamics [0.0]
  Recent demonstrations of D-Wave's quantum simulators have established new benchmarks for quantum computational advantage.<n>We demonstrate that time-dependent variational Monte Carlo can efficiently simulate quantum annealing of spin glasses up to system sizes.
  arXiv  Detail & Related papers  (2025-03-11T10:09:37Z)
• Large-scale quantum annealing simulation with tensor networks and belief propagation [0.0]
  We show that quantum annealing for 3-regular graphs can be classically simulated even at scales of 1000 qubits and 5000000qubit gates. For non-degenerate instances, the unique solution can be read out from the final reduced single-qubit states. For degenerate problems, such as MaxCut, we introduce an approximate measurement simulation algorithm for graph tensor-network states.
  arXiv  Detail & Related papers  (2024-09-18T18:00:08Z)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Reducing Unitary Coupled Cluster Circuit Depth by Classical Stochastic Amplitude Pre-Screening [0.0]
  Unitary Coupled Cluster (UCC) approaches are an appealing route to utilising quantum hardware to perform quantum chemistry calculations. We present a combined classical-quantum approach where a classical UCC pre-processing step is used to determine the important excitations in the UCC ansatz.
  arXiv  Detail & Related papers  (2021-08-24T18:34:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.