Reliable Analog Quantum Simulation and Quantum Complexity

Karthik Chinni, Pablo M. Poggi, Ivan Deutsch

Nathan Lysne, Kevin W. Kuper, Poul Jessen

Dominik Hangleiter, Jacques Carolan, Karim P. Y. Thébault

Junyu Zhou, Yuhao Liu, Shize Che +5 more

Analog quantum simulation leverages native hardware dynamics to emulate complex quantum systems with great efficiency by bypassing the quantum circuit abstraction. However, conventional compilation methods for analog simulators are typically labor-intensive, prone to errors, and computationally demanding. This paper introduces QTurbo, a powerful analog quantum simulation compiler designed to significantly enhance compilation efficiency and optimize hardware execution time. By generating precise and noiseresilient pulse schedules, our approach ensures greater accuracy and reliability, outperforming the existing state-of-theart approach.

Junyu Zhou, Yuhao Liu, Shize Che +5 more

Analog quantum simulation leverages native hardware dynamics to emulate complex quantum systems with great efficiency by bypassing the quantum circuit abstraction. However, conventional compilation methods for analog simulators are typically labor-intensive, prone to errors, and computationally demanding. This paper introduces QTurbo, a powerful analog quantum simulation compiler designed to significantly enhance compilation efficiency and optimize hardware execution time. By generating precise and noise-resilient pulse schedules, our approach ensures greater accuracy and reliability, outperforming the existing state-of-the-art approach.

Rui-Cheng Guo, Yanwu Gu, Dong E. Liu

Simulating quantum many-body systems is crucial for advancing physics but poses substantial challenges for classical computers. Quantum simulations overcome these limitations, with analog simulators offering unique advantages over digital methods, such as lower systematic errors and reduced circuit depth, making them efficient for studying complex quantum phenomena. However, unlike their digital counterparts, analog quantum simulations face significant limitations due to the absence of effective error mitigation techniques. This work introduces two novel error mitigation strategies—Hamiltonian reshaping and Hamiltonian rescaling—in analog quantum simulation for tasks like eigen-energy evaluation. Hamiltonian reshaping uses random unitary transformations to generate new Hamiltonians with identical eigenvalues but varied eigenstates, allowing error reduction through averaging. Hamiltonian rescaling mitigates errors by comparing eigenvalue estimates from energy-scaled Hamiltonians. Numerical calculations validate both methods, demonstrating their significant practical effectiveness in enhancing the accuracy and reliability of analog quantum simulators.