Elucidating reaction mechanisms on quantum computers

By Markus Reiher, Nathan Wiebe, Krysta Marie Svore, Dave Wecker, Matthias Troyer. Proceedings of the National Academy of Sciences of the United States of America, 2016

777 citations recorded by the source corpus.

Significance Our work addresses the question of compelling killer applications for quantum computers. Although quantum chemistry is a strong candidate, the lack of details of how quantum computers can be used for specific applications makes it difficult to assess whether they will be able to deliver on the promises. Here, we show how quantum computers can be used to elucidate the reaction mechanism for biological nitrogen fixation in nitrogenase, by augmenting classical calculation of reaction mechanisms with reliable estimates for relative and activation energies that are beyond the reach of traditional methods. We also show that, taking into account overheads of quantum error correction and gate synthesis, a modular architecture for parallel quantum computers can perform such calculations with components of reasonable complexity. With rapid recent advances in quantum technology, we are close to the threshold of quantum devices whose computational powers can exceed those of classical supercomputers. Here, we show that a quantum computer can be used to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical computer simulations used to probe these reaction mechanisms, to significantly increase their accuracy and enable hitherto intractable simulations. Our resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. Our results demonstrate that quantum computers will be able to tackle important problems in chemistry without requiring exorbitant resources.

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

View Paper: https://www.semanticscholar.org/paper/3e5f3024502c2d4a843eb470dcb43c49dbc54a9f

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Citations in the corpus

Quantum technology for military applications (2021, background): “The most dominant approaches are two: quantum-phase estimation [31] and quantum variational techniques (VQE) [32, 33].”
Toward the first quantum simulation with quantum speedup (2017, methodology): “For quantum chemistry, a natural target for a problem just beyond the reach of classical computing is a simulation of FeMoco, the primary cofactor of nitrogenase, an enzyme that catalyzes the nitrogen fixation process.”
Practical quantum advantage in quantum simulation (2022, background): “This will open the possibility to simulate processes in quantum chemistry 13,92 and materials science 93,94 , where the effective models are often complex and involve long-range couplings.”
A Game of Surface Codes: Large-Scale Quantum Computing with Lattice Surgery (2018, methodology): “In particular, the simulation of FeMoco [1], a structure that plays an important role in nitrogen ﬁxation, can have a T count of up to 10 15 .”
Quantum Simulators: Architectures and Opportunities (2019, background): “Quantum Chemistry: Chemistry problems such as calculating reaction rates and modeling catalysis are tantalizing topics with societal importance, including more efficient nitrogen fixation [23] and making synthetic versions of light-harvesting photosynthetic complexes.”
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity (2018, methodology): “[43] sought empirical estimates of the T complexity rather than rigorous upper bounds as we did, they estimated that approximately 10 15 – 10 16 T gates would be required for a 108-qubit simulation of the FeMoco molecule active space.”
Suppressing quantum errors by scaling a surface code logical qubit (2022, neutral): “These applications often require billions of quantum operations [9–11] while state-of-the-art quantum processors typically have error rates around 10 − 3 per gate [12–17], far too high to execute such large circuits.”
Machine learning for molecular and materials science (2018, background): “In problems such as optimising synthetic routes 96 or improving a given metric (e.g. optical absorption for solar energy materials) where multiple acceptable solutions exist, loss of qubit fidelity is less serious than when certainty is required.”
Materials challenges and opportunities for quantum computing hardware (2021, background): “…sophisticated algorithms on current hardware ( 8 , 30 ), and future error-correcting quantum computers will likely require fidelities far beyond 99.9% across a large-scale device ( 31 – 33 ); higher-fidelity operations translate to reduced overheads associated with quantum error correction (Fig.”
Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator (2018, background): “[48] Markus Reiher, Nathan Wiebe, Krysta M Svore, Dave Wecker, and Matthias Troyer, “Elucidating reaction mechanisms on quantum computers.”
High-threshold and low-overhead fault-tolerant quantum memory (2023, background): “It is believed that a scalable functioning quantum computer may help solve computational problems in such areas as scientific discovery, materials research, chemistry, and drug design, to name a few [2, 3, 4, 5].”
Q#: Enabling Scalable Quantum Computing and Development with a High-level DSL (2018, background): “For instance, Q# makes it easy to express phase estimation [10, 21, 23] and quantum chemistry [16] algorithms, both of which require rich quantum–classical interactions.”