Hamiltonian Simulation by Qubitization
1Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
2Department of Electrical Engineering and Computer Science, Department of Physics, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| Published: | 2019-07-12, volume 3, page 163 |
| Eprint: | arXiv:1610.06546v3 |
| Doi: | https://doi.org/10.22331/q-2019-07-12-163 |
| Citation: | Quantum 3, 163 (2019). |
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Abstract
We present the problem of approximating the time-evolution operator $e^{-i\hat{H}t}$ to error $\epsilon$, where the Hamiltonian $\hat{H}=(\langle G|\otimes\hat{\mathcal{I}})\hat{U}(|G\rangle\otimes\hat{\mathcal{I}})$ is the projection of a unitary oracle $\hat{U}$ onto the state $|G\rangle$ created by another unitary oracle. Our algorithm solves this with a query complexity $\mathcal{O}\big(t+\log({1/\epsilon})\big)$ to both oracles that is optimal with respect to all parameters in both the asymptotic and non-asymptotic regime, and also with low overhead, using at most two additional ancilla qubits. This approach to Hamiltonian simulation subsumes important prior art considering Hamiltonians which are $d$-sparse or a linear combination of unitaries, leading to significant improvements in space and gate complexity, such as a quadratic speed-up for precision simulations. It also motivates useful new instances, such as where $\hat{H}$ is a density matrix. A key technical result is `qubitization', which uses the controlled version of these oracles to embed any $\hat{H}$ in an invariant $\text{SU}(2)$ subspace. A large class of operator functions of $\hat{H}$ can then be computed with optimal query complexity, of which $e^{-i\hat{H}t}$ is a special case.

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[98] Yukuan Tao, Mădălin Guţă, and Gerardo Adesso, "Trade-off Between Complexity and Energy in Quantum Phase Estimation", PRX Energy 5 3, 033016 (2026).
[99] Shang Gao and Yu-Guang Yang, "A novel quantum recommender system", Physica Scripta 98 1, 010001 (2023).
[100] Olivia Di Matteo, Anna McCoy, Peter Gysbers, Takayuki Miyagi, R. M. Woloshyn, and Petr Navrátil, "Improving Hamiltonian encodings with the Gray code", Physical Review A 103 4, 042405 (2021).
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[106] Kaito Mizukami and Akihisa Koga, "Quantum algorithm for the microcanonical thermal pure quantum state method", Physical Review A 108 1, 012404 (2023).
[107] Sapthagiri Miriyala and Venkata Ramireddy Chirra, "From Quantum Algorithm Primitives to Design Patterns: A Unified Framework for NISQ and Fault‐Tolerant Regimes", Advanced Quantum Technologies 9 6, e70310 (2026).
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[123] Etienne Granet and Henrik Dreyer, "Dilution of Error in Digital Hamiltonian Simulation", PRX Quantum 6 1, 010333 (2025).
[124] Francoise Golse, Shi Jin, and Nana Liu, "Quantum algorithms for uncertainty quantification: Applications to partial differential equations", Science China Physics, Mechanics & Astronomy 68 10, 104704 (2025).
[125] Nhat A. Nghiem and Tzu-Chieh Wei, "Improved quantum algorithms for eigenvalue finding and gradient descent", Physical Review Applied 25 5, 054024 (2026).
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[773] Youle Wang and Yu Luo, "Resource-efficient quantum principal component analysis", Quantum Science and Technology 9 3, 035031 (2024).
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[835] Xiaoming Sun, Guojing Tian, Shuai Yang, Pei Yuan, and Shengyu Zhang, "Asymptotically Optimal Circuit Depth for Quantum State Preparation and General Unitary Synthesis", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 42 10, 3301 (2023).
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[844] Guang Hao Low and Yuan Su, "Quantum Eigenvalue Processing", SIAM Journal on Computing 55 1, 135 (2026).
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