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  • Open Access

From Linear Differential Equations to Unitaries: A Moment-Matching Dilation Framework with Near-Optimal Quantum Algorithms

Xiantao Li*

  • *Contact author: Xiantao.Li@psu.edu

PRX Quantum 7, 020350 – Published 4 June, 2026

DOI: https://doi.org/10.1103/xm61-ytf7

Abstract

We introduce a universal dilation framework to simulate arbitrary non-Hermitian dynamics x˙=L(t)x on unitary quantum processors. By imposing algebraic moment-matching conditions on an ancilla, our framework generates diverse families of dilation schemes that unify existing methods and broaden the design space for hardware-aware implementations. A tight-binding dilation that maps the ancilla to a 1D lattice with nearest-neighbor hopping is derived, overcoming the connectivity bottlenecks of dilation schemes. This construction handles general dynamics, including gain, without a priori rescaling and achieves near-optimal complexity, as demonstrated by benchmarks on dissipative wave propagations.

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References (36)

  1. S. Yao and Z. Wang, Edge states and topological invariants of non-Hermitian systems, Phys. Rev. Lett. 121, 086803 (2018).
  2. M. Motta, C. Sun, A. T. K. Tan, M. J. O’Rourke, E. Ye, A. J. Minnich, F. G. S. Brandao, and G. K.-L. Chan, Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution, Nat. Phys. 16, 205 (2020).
  3. A. Vibok and G. Balint-Kurti, Parametrization of complex absorbing potentials for time-dependent quantum dynamics, J. Phys. Chem. 96, 8712 (1992).
  4. G. H. Low and I. L. Chuang, Hamiltonian simulation by qubitization, Quantum 3, 163 (2019).
  5. G. H. Low and N. Wiebe, Hamiltonian simulation in the interaction picture, arXiv:1805.00675.
  6. A. Gilyén, Y. Su, G. H. Low, and N. Wiebe, in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (Association for Computing Machinery (ACM), New York, NY, USA, 2019), pp. 193–204.
  7. S. Jin, N. Liu, and Y. Yu, Quantum simulation of partial differential equations via Schrödingerization, Phys. Rev. Lett. 133, 230602 (2024).
  8. S. Jin, N. Liu, and Y. Yu, Quantum simulation of partial differential equations: Applications and detailed analysis, Phys. Rev. A 108, 032603 (2023).
  9. D. An, J.-P. Liu, and L. Lin, Linear combination of Hamiltonian simulation for nonunitary dynamics with optimal state preparation cost, Phys. Rev. Lett. 131, 150603 (2023).
  10. D. An, A. M. Childs, and L. Lin, Quantum algorithm for linear non-unitary dynamics with near-optimal dependence on all parameters, arXiv:2312.03916.
  11. S. Jin, N. Liu, C. Ma, and Y. Yu, On the Schrödingerization method for linear non-unitary dynamics with optimal dependence on matrix queries, arXiv:2505.00370.
  12. D. An, J.-P. Liu, D. Wang, and Q. Zhao, Quantum differential equation solvers: Limitations and fast-forwarding, Commun. Math. Phys. 406, 189 (2025).
  13. G. H. Low and R. D. Somma, Optimal quantum simulation of linear non-unitary dynamics, arXiv:2508.19238.
  14. S. Jin, X. Li, N. Liu, and Y. Yu, Quantum simulation for quantum dynamics with artificial boundary conditions, SIAM J. Sci. Comput. 46, B403 (2024).
  15. M. Pocrnic, P. D. Johnson, A. Katabarwa, and N. Wiebe, Constant-factor improvements in quantum algorithms for linear differential equations, arXiv:2506.20760.
  16. X. Huang and D. An, Fourier transform-based linear combination of Hamiltonian simulation, arXiv:2508.19596.
  17. R. Lu, H.-E. Li, Z. Liu, and J.-P. Liu, Infinite-dimensional extension of the linear combination of Hamiltonian simulation: Theorems and applications, arXiv:2502.19688.
  18. D. W. Berry, High-order quantum algorithm for solving linear differential equations, J. Phys. A: Math. Theor. 47, 105301 (2014).
  19. D. W. Berry, A. M. Childs, A. Ostrander, and G. Wang, Quantum algorithm for linear differential equations with exponentially improved dependence on precision, Commun. Math. Phys. 356, 1057 (2017).
  20. D. Fang, L. Lin, and Y. Tong, Time-marching based quantum solvers for time-dependent linear differential equations, Quantum 7, 955 (2023).
  21. D. Fang, D. L. George, and Y. Tong, Qubit-efficient quantum algorithm for linear differential equations, arXiv:2507.16995 [quant-ph].
  22. Z.-X. Shang, N. Guo, D. An, and Q. Zhao, Designing a nearly optimal quantum algorithm for linear differential equations via Lindbladians, Phys. Rev. Lett. 135, 120604 (2025).
  23. D. An, A. M. Childs, and L. Lin, Quantum algorithm for linear non-unitary dynamics with near-optimal dependence on all parameters, Commun. Math. Phys. 407, 19 (2026).
  24. S. Jin, N. Liu, and C. Ma, On Schrödingerization based quantum algorithms for linear dynamical systems with inhomogeneous terms, arXiv:2402.14696.
  25. B. Strand, Summation by parts for finite difference approximations for d/dx, J. Comput. Phys. 110, 47 (1994).
  26. K. Mattsson and J. Nordström, Summation by parts operators for finite difference approximations of second derivatives, J. Comput. Phys. 199, 503 (2004).
  27. G. J. Gassner, A skew-symmetric discontinuous Galerkin spectral element discretization and its relation to SBP-SAT finite difference methods, SIAM J. Sci. Comput. 35, A1233 (2013).
  28. R. J. LeVeque, Numerical Methods for Conservation Laws (Springer, Birkhäuser, Basel, Switzerland, 1992), Vol. 132.
  29. E. H. Lieb and D. W. Robinson, The finite group velocity of quantum spin systems, Commun. Math. Phys. 28, 251 (1972).
  30. Y. Mao, M. Chaudhary, M. Kondappan, J. Shi, E. O. Ilo-Okeke, V. Ivannikov, and T. Byrnes, Measurement-based deterministic imaginary time evolution, Phys. Rev. Lett. 131, 110602 (2023).
  31. R. S. Lakes, Viscoelastic Materials (Cambridge University Press, Cambridge, 2009).
  32. X. Li, Quantum dilation algorithms for linear ODEs, 2025, https://github.com/xxl12/quantum-dilation-algorithms, accessed: 2025-07-12.
  33. N. Killoran, J. Izaac, N. Quesada, V. Bergholm, M. Amy, and C. Weedbrook, Strawberry fields: A software platform for photonic quantum computing, Quantum 3, 129 (2019).
  34. H. Li, H. Ni, and L. Ying, On efficient quantum block encoding of pseudo-differential operators, Quantum 7, 1031 (2023).
  35. Q. T. Nguyen, B. T. Kiani, and S. Lloyd, Block-encoding dense and full-rank kernels using hierarchical matrices: Applications in quantum numerical linear algebra, Quantum 6, 876 (2022).
  36. L. Debnath and D. Bhatta, Integral Transforms and Their Applications (Chapman & Hall/CRC, Boca Raton, 2015), 3rd ed. Chapters 10–11 give a self-contained treatment of the Mellin transform, including inversion, convolution, and applications to differential equations.

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