Computation of Molecular Spectra on a Quantum Processor with an Error-Resilient Algorithm

By J. Colless, Vinay Venkatesh Ramasesh, D. Dahlen, M. Blok, Jarrod R. McClean, Jonathan Carter, Wibe de Jong, Irfan Siddiqi. 2017

449 citations recorded by the source corpus.

© 2018 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Harnessing the full power of nascent quantum processors requires the efficient management of a limited number of quantum bits with finite coherent lifetimes. Hybrid algorithms, such as the variational quantum eigensolver (VQE), leverage classical resources to reduce the required number of quantum gates. Experimental demonstrations of VQE have resulted in calculation of Hamiltonian ground states, and a new theoretical approach based on a quantum subspace expansion (QSE) has outlined a procedure for determining excited states that are central to dynamical processes. We use a superconducting-qubit-based processor to apply the QSE approach to the H2 molecule, extracting both ground and excited states without the need for auxiliary qubits or additional minimization. Further, we show that this extended protocol can mitigate the effects of incoherent errors, potentially enabling larger-scale quantum simulations without the need for complex error-correction techniques.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/e90822722cc0255ae47e0039eb82252086308b96

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Citations in the corpus

Superconducting quantum computing: a review (2020, background): “[184] Colless, J.”
Self-verifying variational quantum simulation of lattice models (2018, methodology): “This is equivalent to an excitation ( E ( θ opt ) − E (0) ) of 11% ± 18% of the energy gap Δ exp , where Δ exp = 2.11 ± 0.24 itself is experimentally evaluated via a quantum subspace expansion strategy (see Supplementary Information section VI) 42 .”
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.”
qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor (2019, background): “Compared to PEA, VQE has much more modest requirements on the coherence times of the quantum processor, so it has already been realized on NISQ devices, such as superconducting qubits [11, 12], photons [8] and trapped ions [13, 14].”
Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution (2019, methodology): “Given the QITE subroutine, we now consider how to formulate the QLanczos algorithm, which is an especially economical realization of a quantum subspace method 31,32 .”
Laser-annealing Josephson junctions for yielding scaled-up superconducting quantum processors (2020, background): “[4] Colless, J.”