Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator

By Cornelius Hempel, C. Maier, Jonathan Romero, Jarrod R. McClean, Thomas Monz, Heng Shen, P. Jurcevic, B. Lanyon, Peter J. Love, Ryan Babbush and 3 more. Physical Review X, 2018

547 citations recorded by the source corpus.

Quantum-classical hybrid algorithms are emerging as promising candidates for near-term practical applications of quantum information processors in a wide variety of fields ranging from chemistry to physics and materials science. We report on the experimental implementation of such an algorithm to solve a quantum chemistry problem, using a digital quantum simulator based on trapped ions. Specifically, we implement the variational quantum eigensolver algorithm to calculate the molecular ground state energies of two simple molecules and experimentally demonstrate and compare different encoding methods using up to four qubits. Furthermore, we discuss the impact of measurement noise as well as mitigation strategies and indicate the potential for adaptive implementations focused on reaching chemical accuracy, which may serve as a cross-platform benchmark for multi-qubit quantum simulators.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/02ca304b683a04fa71e5824616b3ad5ef1df4de2

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Citations in the corpus

Variational quantum algorithms for nonlinear problems (2019, methodology): “, to simulating the dynamics of strongly correlated electrons through non-equilibrium dynamical mean field theory [22, 23, 33, 34], and quantum chemistry calculations were successfully carried out on existing noisy superconducting [21, 25, 26] and ion quantum computers [28].”
Quantum Simulators: Architectures and Opportunities (2019, methodology): “Variational quantum eigensolver algorithms for quantum chemistry have also been demonstrated with this platform [91, 92].”
Laser-annealing Josephson junctions for yielding scaled-up superconducting quantum processors (2020, background): “Many practical applications have been proposed for robust quantum computers, including estimating the ground state energy of chemical compounds and implementing machine learning algorithms [1–5].”
Barren plateaus in quantum neural network training landscapes (2018, background): “Many of the most promising application-oriented approaches are hybrid quantum-classical algorithms that rely on optimization of a parameterized quantum circuit [2–8].”
Quantum Chemistry in the Age of Quantum Computing. (2018, background): “ution times obtainable from the covariance matrix. Each parallel cycle of the LDCA circuit interleaves gates between even and odd neighboring qubits, U(k) MG = Y i2odd G(k) i;i+1 Y i2even G(k) i;i+1: (59) There are N 2 cycles in total, i.e. UNN MG = dM Y2 e k=1 U(k) MG; (60) such that the transforma”
Potential of quantum computing for drug discovery (2018, background): “In recent years, there have been a number of VQE experiments [97, 107–111] performed on a variety of physical platforms, some of which have already delivered results of chemical accuracy within a given one-particle basis set for small systems, such as berylium hydride.”
Error mitigation extends the computational reach of a noisy quantum processor (2018, methodology): “We now demonstrate its general applicability by considering problems in quantum chemistry [7, 9, 12, 22, 23, 26]. in Figure 4.”
Self-verifying variational quantum simulation of lattice models (2018, background): “Small-scale, pioneering experiments with hybrid algorithms running on quantum computers of two to six qubits have been shown to successfully tackle problems in quantum chemistry 18,20–22 and, more recently, in condensed matter and high-energy physics 23,24 .”
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity (2018, background): “More recently, experimental demonstrations [11 – 15] have focused on the development of variational algorithms [16,17], which are often [18,19], but not always [20,21], inspired by time-evolution primitives.”