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Scalable and shallow quantum circuits encoding probability distributions informed by asymptotic entanglement analysis

https://arxiv.org/abs/2412.05202 This paper uses Matrix Product States to convert classical probability distributions into shallow quantum circuits with linear gate counts.

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Efficient Hamiltonian Simulation: A Utility Scale Perspective for Covalent Inhibitor Reactivity Prediction

https://arxiv.org/abs/2412.15804 This work shows how to make deep simulations practical by partitioning them into manageable sub-blocks and subsequently reconstructing the quantum state with Haiqu.

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Accelerating Transpilation in Quantum Machine Learning with Haiqu’s Rivet-transpiler

https://arxiv.org/abs/2508.21342 This paper introduces the Rivet transpiler, which optimizes iterative processes like machine learning by caching and reusing previously transpiled subcircuits.

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Quantum Hamiltonian simulation of linearised Euler equations in complex geometries

https://arxiv.org/abs/2510.17978 This is a research paper on simulating fluid dynamics around obstacles without increasing overall circuit complexity or Trotter error. It touches on data loading with tensor network approaches to prepare shallow circuits for complex initial conditions.

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Distributed Quantum Dynamics on Near-Term Quantum Processors

https://arxiv.org/abs/2502.03542 This is a recent paper from this year on how large Hamiltonian evolution simulations can be executed across multiple smaller quantum processors.