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Efficient explicit gate construction of block-encoding for Hamiltonians needed for simulating partial differential equations

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arxiv 2405.12855 v3 pith:QCDDT52Q submitted 2024-05-21 quant-ph math-phmath.MP

classification quant-phmath-phmath.MP
keywords hamiltoniansefficientequationsexplicitpdesquantumalgorithmblock-encoding
verification ladder T0 review T1 audit T2 compute T3 formal
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One of the most promising applications of quantum computers is solving partial differential equations (PDEs). By using the Schrodingerisation technique - which converts non-conservative PDEs into Schrodinger equations - the problem can be reduced to Hamiltonian simulations. The particular class of Hamiltonians we consider is shown to be sufficient for simulating almost any linear PDE. In particular, these Hamiltonians consist of discretizations of polynomial products and sums of position and momentum operators. This paper addresses an important gap by efficiently loading these Hamiltonians into the quantum computer through block-encoding. The construction is explicit and efficient in terms of one- and two-qubit operations, forming a fundamental building block for constructing the unitary evolution operator for that class of Hamiltonians. The proposed algorithm demonstrates a squared logarithmic scaling with respect to the spatial partitioning size, offering a polynomial speedup over classical finite-difference methods in the context of spatial partitioning for PDE solving. Furthermore, the algorithm is extended to the multi-dimensional case, achieving an exponential acceleration with respect to the number of dimensions, alleviating the curse of dimensionality problem. This work provides an essential foundation for developing explicit and efficient quantum circuits for PDEs, Hamiltonian simulations, and ground state and thermal state preparation.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Distributed Quantum Dynamics on Near-Term Quantum Processors

    quant-ph 2025-02 conditional novelty 6.0 of 10

    dp-VQD combines projected variational quantum dynamics with wire cutting to run Hamiltonian evolution on more qubits than a single device has, using cuttable ansatze and a sliced Trotter step.

  2. Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers

    quant-ph 2025-02 unverdicted novelty 2.0 of 10

    A structured tutorial that introduces quantum machine learning concepts, algorithms, theory, and PennyLane code to classical ML practitioners.

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