Scalable Quantum Simulation of Molecular Energies

By P. O’Malley, Ryan Babbush, I. Kivlichan, Jonathan Romero, Jarrod R. McClean, R. Barends, J. Kelly, P. Roushan, A. Tranter, Nan Ding and 20 more. 2015

1,013 citations recorded by the source corpus.

We report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation. We use a programmable array of superconducting qubits to compute the energy surface of molecular hydrogen using two distinct quantum algorithms. First, we experimentally execute the unitary coupled cluster method using the variational quantum eigensolver. Our efficient implementation predicts the correct dissociation energy to within chemical accuracy of the numerically exact result. Second, we experimentally demonstrate the canonical quantum algorithm for chemistry, which consists of Trotterization and quantum phase estimation. We compare the experimental performance of these approaches to show clear evidence that the variational quantum eigensolver is robust to certain errors. This error tolerance inspires hope that variational quantum simulations of classically intractable molecules may be viable in the near future.

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

View Paper: https://www.semanticscholar.org/paper/e45f73f2f82efd833b65e1c29789a16e035b33e6

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Citations in the corpus

Self-verifying variational quantum simulation of lattice models (2018, methodology): “We now address questions related to the complexity and scalability 10,18,29,51,56,57 of the VQS algorithm we employ, applied to the specific example of Schwinger model ground states simulated with trapped ions.”
Theory of variational quantum simulation (2018, methodology): “For solving static problems, the Rayleigh-Ritz variational method can be naturally generalised to the quantum regime, such as the variational quantum eigensolver (VQE) [29–40] and the quantum approximate optimisation algorithm (QAOA) [28].”
Low-Depth Quantum Simulation of Materials (2017, methodology): “[40,41] using superconducting qubit platforms from industrial quantum computing groups, which are expected to reach the quantum supremacy threshold in the near future [35].”
Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers (2019, methodology): “[4] P J J O’Malley, R Babbush, I D Kivlichan, J Romero, J R McClean, R Barends, J Kelly, P Roushan, A Tranter, N Ding, B Campbell, Y Chen, Z Chen, B Chiaro,”
Potential of quantum computing for drug discovery (2018, background): “On the experimental side, simple demonstrations of quantum chemistry calculations for a diverse set of physical systems have been realized [93–97].”
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.”
Computation of Molecular Spectra on a Quantum Processor with an Error-Resilient Algorithm (2017, methodology): “[17] or through alternative symmetry enforcing transformations as introduced by Bravyi et al [30].”
Quantum Chemistry in the Age of Quantum Computing. (2018, methodology): “hat species the FGSs as the corresponding quadratic fermionic Hamiltonian for which the target FGSs are ground states. The Hamiltonian is H= XN j;k=1 jkc y jck+ 1 2 XN j;k=1 jkc y jc y k+ h:c: ; (55) where = yand = yare complex matrices. This Hamiltonian is brought into its 92 diagonal form using ”