arXiv:2605.13268v2 Announce Type: replace-cross Abstract: Trotter Suzuki product formulas are the standard route to Hamiltonian evolution on noisy intermediate-scale quantum (\NISQ{}) hardware, but their accuracy depends on three coupled choices: term grouping, product-formula order, and time-step allocation. Grouping and order are discrete, which makes direct gradient optimization infeasible and forces existing compilers to rely on static heuristics. We describe P-GONE, a method that combines a conditional diffusion model (D3PM + DDPM), a graph neural network (\GNN{}) encoder, and closed-loop
The development of more sophisticated AI models and graph neural networks is enabling new approaches to complex quantum computing optimization problems, spurred by ongoing investment in quantum hardware development.
Improved Trotter Suzuki decomposition, a core component of quantum simulation, directly impacts the efficiency and accuracy of quantum computers, accelerating the timeline for useful quantum applications in fields like materials science and drug discovery.
This advancement introduces a new paradigm for optimizing quantum algorithms that moves beyond static heuristics, enabling more efficient utilization of Noisy Intermediate-Scale Quantum (NISQ) hardware and potentially expanding its practical applications.
- · Quantum computing hardware developers
- · Quantum software developers
- · Materials science
- · Pharmaceuticals
- · Developers of less efficient quantum optimization techniques
- · Classical simulation methods (in niche areas)
More accurate and efficient quantum simulations become feasible on current and near-term quantum hardware.
This could accelerate the discovery of new materials or drug compounds, giving an advantage to nations and companies leading in quantum research.
Long-term, improved quantum computing efficiency could contribute to shifts in global technological leadership and economic competitiveness.
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Read at arXiv cs.LG