A general quantum algorithm for open quantum dynamics demonstrated with the Fenna-Matthews-Olson complex

Hu, Zixuan; Head-Marsden, Kade; Mazziotti, David A.; Narang, Prineha; Kais, Sabre

doi:10.22331/q-2022-05-30-726

Cited by 41 publications

(37 citation statements)

References 46 publications

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“…Quantum simulation offers some of the strongest prospects for practical quantum advantages. Example applications include finding ground [146,147] and excited states [163,310] of electronic degrees of freedom, vibrational degrees of freedom [273,274] and more complex degrees of freedom [82,[311][312][313], or dispersion interaction between drug molecules and proteins [314]. VQEs in particular have the strong possibility of near-term advantages as they scale.…”

Section: Prospects For Quantum Simulationmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

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Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

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Section: Prospects For Quantum Simulationmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

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show abstract

“…A Hamiltonian simulation, as described in Section but executed for an open quantum system with a non-Hermitian operator, cannot directly be translated into a unitary transformation due to a change of norm and thus cannot be expressed by a series of quantum gates. Nevertheless, a propagation with such an operator is possible, e.g., with dilation methods, − time-dependent variational methods, or the here implemented QITE algorithm. ,, …”

Section: Theorymentioning

confidence: 99%

Quantum-Compute Algorithm for Exact Laser-Driven Electron Dynamics in Molecules

Langkabel

Bande

2022

J. Chem. Theory Comput.

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In this work, we investigate the capability of known quantum computing algorithms for fault-tolerant quantum computing to simulate the laser-driven electron dynamics of excitation and ionization processes in small molecules such as lithium hydride, which can be benchmarked against the most accurate time-dependent full configuration interaction (TD-FCI) calculations. The conventional TD-FCI wave packet propagation is reproduced using the Jordan−Wigner transformation for wave function and operators and the Trotter product formula for expressing the propagator. In addition, the time-dependent dipole moment, as an example of a time-dependent expectation value, is calculated using the Hadamard test. To include non-Hermitian operators in the ionization dynamics, a similar approach to the quantum imaginary time evolution (QITE) algorithm is employed to translate the propagator, including a complex absorption potential, into quantum gates. The computations are executed on a quantum computer simulator. By construction, all quantum computer algorithms, except for the QITE algorithm used only for ionization but not for excitation dynamics, would scale polynomially on a quantum computer with fully entangled qubits. In contrast, TD-FCI scales exponentially. Hence, quantum computation holds promises for substantial progress in the understanding of electron dynamics of excitation processes in increasingly large molecular systems, as has already been witnessed in electronic structure theory.

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“…In particular, the no-fast-forwarding theorem states that simulating the dynamics of a quantum system for time t typically requires O ( t ) gates; in other words, a generic Hamiltonian evolution cannot be achieved in sublinear time . Despite its relevance and importance, research on simulating time-dependent processes in chemical systems with noisy intermediate-scale quantum (NISQ) devices remains limited. − …”

Section: Introductionmentioning

confidence: 99%

Simulating Energy Transfer in Molecular Systems with Digital Quantum Computers

Lee

Lau

Shi

et al. 2022

J. Chem. Theory Comput.

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Quantum computers have the potential to simulate chemical systems beyond the capability of classical computers. Recent developments in hybrid quantum-classical approaches enable the determinations of the ground or low energy states of molecular systems. Here, we extend near-term quantum simulations of chemistry to time-dependent processes by simulating energy transfer in organic semiconducting molecules. We developed a multiscale modeling workflow that combines conventional molecular dynamics and quantum chemistry simulations with hybrid variational quantum algorithm to compute the exciton dynamics in both the single excitation subspace (i.e., Frenkel Hamiltonian) and the full-Hilbert space (i.e., multiexciton) regimes. Our numerical examples demonstrate the feasibility of our approach, and simulations on IBM Q devices capture the qualitative behaviors of exciton dynamics, but with considerable errors. We present an error mitigation technique that combines experimental results from the variational and Trotter algorithms, and obtain significantly improved quantum dynamics. Our approach opens up new opportunities for modeling quantum dynamics in chemical, biological, and material systems with quantum computers.

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A general quantum algorithm for open quantum dynamics demonstrated with the Fenna-Matthews-Olson complex

Cited by 41 publications

References 46 publications

Biology and medicine in the landscape of quantum advantages

Biology and medicine in the landscape of quantum advantages

Quantum-Compute Algorithm for Exact Laser-Driven Electron Dynamics in Molecules

Simulating Energy Transfer in Molecular Systems with Digital Quantum Computers

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