Quantum Differential Equation Solver via Hybrid Oscillator-Qubit Linear Combination of Hamiltonian Simulations
Elin Ranjan Das, Muqing Zheng, Rishab Dutta, Ang Li, Timothy Stavenger, and Yuan Liu

TL;DR
This paper presents a hybrid oscillator-qubit approach to solve linear differential equations efficiently, reducing ancilla overhead and providing analytical error bounds, with demonstrated high fidelity in heat-equation benchmarks.
Contribution
It introduces a continuous-variable ancillary mode in LCHS, eliminating explicit qubit overhead, and derives error bounds showing superalgebraic convergence and resource efficiency.
Findings
Achieves at least 99.90% fidelity in heat-equation benchmarks.
Reduces circuit cost compared to matrix-product-state-based implementations.
Provides analytical bounds on error and resource requirements for the hybrid approach.
Abstract
We introduce a hybrid oscillator-qubit formulation of linear combination of Hamiltonian simulation (LCHS) for solving linear ordinary differential equations. Instead of representing the quadrature rule with a discrete-variable (DV) ancilla register in qubit-only LCHS, the method encodes the LCHS kernel in a continuous-variable (CV) ancillary mode, thereby eliminating the explicit ancilla-qubit overhead, where is the number of discretized integral terms in the DV quadrature rule. We derive analytical error bounds for two main approximation mechanisms for the ideal kernel state preparation, showing superalgebraic convergence for Schwartz-class kernels in the truncation cutoff . The required CV non-Gaussianity is captured by the finite squeezed-Fock kernel state, which generically has stellar rank , identifying the truncation cutoff as a discrete measure of the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
