Improved techniques for preparing eigenstates of fermionic Hamiltonians

By Dominic W. Berry, Mária Kieferová, A. Scherer, Y. Sanders, Guang Hao Low, Nathan Wiebe, Craig Gidney, Ryan Babbush. 2017

159 citations recorded by the source corpus.

Modeling low energy eigenstates of fermionic systems can provide insight into chemical reactions and material properties and is one of the most anticipated applications of quantum computing. We present three techniques for reducing the cost of preparing fermionic Hamiltonian eigenstates using phase estimation. First, we report a polylogarithmic-depth quantum algorithm for antisymmetrizing the initial states required for simulation of fermions in first quantization. This is an exponential improvement over the previous state-of-the-art. Next, we show how to reduce the overhead due to repeated state preparation in phase estimation when the goal is to prepare the ground state to high precision and one has knowledge of an upper bound on the ground state energy that is less than the excited state energy (often the case in quantum chemistry). Finally, we explain how one can perform the time evolution necessary for the phase estimation based preparation of Hamiltonian eigenstates with exactly zero error by using the recently introduced qubitization procedure.Quantum simulation: exponential improvement for simulating fermionic systemsImproved quantum algorithm can now simulate the electronic structure of materials and molecules much faster than what was possible before. A team involving researchers from Macquarie University, Microsoft Research, and Google has developed theoretical tools to efficiently prepare the initial “guess” for the electronic state one wants to simulate, in a way that it correctly includes the property of fermionic systems called “anti-symmetrization”, which means that the total wavefunction changes sign if two particles exchange their positions. The team also managed to reduce the number of physical operations that the quantum computer should perform to evolve the initial guess up to the final solution. Future large-scale quantum simulations should be able to model electronic structure much more efficiently than standard computers, with significant impact in chemistry, biochemistry and material science.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/48a70fcf16686d7e9f306c40381ed9153997dd45

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Citations in the corpus

Quantum phase estimation of multiple eigenvalues for small-scale (noisy) experiments (2018, background): “[23, 40]), the resulting circuit has a temporal length per unitary of T 42 s U m = (with single- (two-) qubit gate times 20 ns (40 ns)).”
Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator (2018, background): “[69] Dominic W Berry, Maria Kieferová, Artur Scherer, Yuval R Sanders, Guang Hao Low, Nathan Wiebe, Craig Gidney, and Ryan Babbush, “Improved Techniques for Preparing Eigenstates of Fermionic Hamiltonians,” npj Quantum Information 4, 22 (2018).”
Variational quantum algorithms (2020, background): “Since long-term algorithms for material simulation require phase estimation [235–237], these lie beyond the scope of near-term devices.”
Low-Depth Quantum Simulation of Materials (2017, background): “With the exception of [9, 11, 13, 74, 78], all prior papers which analyze the time-evolution of electronic structure Hamiltonians use the Trotter-Suzuki decomposition.”