Quantum Chemistry on Quantum Computers

Speaker: Dr Nicole Holzmann
Institute: Riverlane
Country: UK
Speaker Link: https://www.riverlane.com/
Time: 14:00 CET 22-Feb-22

Dr Nicole Holzmann

Riverlane, Cambridge, United Kingdom 

Quantum computers have the potential to impact a wide range of industries [1] through their application to problems such as computational fluid dynamics, combinatorial optimisation and computational chemistry. Due to the quantum nature of fermions, the last of these looks to be a particularly promising application area – as Feynman put it in the early 80s, nature is quantum mechanical by default and thus its simulation requires a quantum computer. 

Such calculations have relevance to the pharmaceutical and materials industries, presenting opportunities to revolutionise the Computer-Aided Drug Design process [2,3], and the development of battery materials and catalysts [4,5]. Initial studies on using quantum computers in such situations have been published by global industrial and quantum players [6-9].

Quantum computing capabilities are currently limited and they are unable to perform useful computational chemistry calculations. However, quantum hardware is advancing, with milestones being reached [10-12] and roadmaps being published [13,14]. At the same time, algorithm development had reduced the estimated quantum computational resources needed to run computational chemistry calculations [e.g. 15].

From the viewpoint of a computational chemist, in this talk we want to look at where the hype about quantum computing ends and where reality starts. How does a quantum computer work, what is a qubit and what are the problems and challenges of the technology? What can a quantum computer do for a chemist, when will we be able to actually do a useful quantum chemical calculation and what resources would we need? How can method and algorithm development help us to make quantum computing useful, sooner?



[1] M Langione, C Tillemann-Dick, A Kumar, V Taneja, Where will quantum computers create value – and when?, Boston Consulting Group 2019,  https://www.bcg.com/publications/2019/quantum-computers-create-value-when

[2] M Evers, A Heid, I Ostojic, Pharma’s digital Rx: Quantum computing in drug research and development, McKinsey & Company 2021 https://www.mckinsey.com/industries/life-sciences/our-insights/pharmas-digital-rx-quantum-computing-in-drug-research-and-development

[3] M Langione, JF Bobier, C Meier, S Hasenfuss, U Schulze, Will Quantum Computing Transform Biopharma R&D?, Boston Consulting Group 2019, https://www.bcg.com/fr-fr/publications/2019/quantum-computing-transform-biopharma-research-development

[4] F Budde, D Volz, The next big thing? Quantum computing’s potential impact on chemicals, McKinsey & Company 2019, https://www.mckinsey.com/industries/chemicals/our-insights/the-next-big-thing-quantum-computings-potential-impact-on-chemicals

[5] O Burkacky, N Mohr, L Pautasso, Will quantum computing drive the automotive future?, McKinsey & Company 2020, https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/will-quantum-computing-drive-the-automotive-future

[6] M Reiher, N Wiebe, KM Svore, D Wecker, M Troyer, Elucidating reaction mechanisms on quantum computers, PNAS 2017, 114(29), 7555-7560.  https://doi.org/10.1073/pnas.1619152114

[7] V von Burg, G H Low, T Haner, DS Steiger, M Reiher, M Roetteler, M Troyer, Quantum computing enhanced computational catalysis, Phys. Rev. Research 2021, 3, 033055. https://doi.org/10.1103/PhysRevResearch.3.033055

[8] JE Rice, TP Gujarati, M Motta, TY Takeshita, E Lee, JA Latone, JM Garcia, Quantum computation of dominant products in lithium–sulfur batteries, J. Chem. Phys. 2021, 154, 134115. https://doi.org/10.1063/5.0044068

[9] IH Kim, E Lee, YH Liu, S Pallister, W Pol, S Roberts, Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules, arXiv:2104.10653 [quant-ph]

[10] F Arute, K Arya, R Babbush, D Bacon, JC Bardin, R Barends, R Biswas, S Boixo, FGSL Brandao, DA Buell, B Burkett, Y Chen, ZJ Chen, B Chiaro, R Collins, W Courtney, A Dunsworth, E Farhi, B Foxen, A Fowler, C Gidney, M Giustina, R Graff, K Guerin, S Habegger, MP Harrigan, MJ Hartmann, A Ho, M Hoffmann, T Huang, TS Humble, SV Isakov, E Jeffrey, Z Jiang, D Kafri, K Kechedzhi, J Kelly, PV Klimov, S Knysh, A Korotkov, F Kostritsa, D Landhuis, M Lindmark, E Lucero, D Lyakh, S Mandrà, JR McClean, M McEwen, A Megrant, X Mi, K Michielsen, M Mohseni, J Mutus, O Naaman, M Neeley, C Neill, MYZ Niu, E Ostby, A Petukhov, JC Platt, C Quintana, EG Rieffel, P Roushan, NC Rubin, D Sank, KJ Satzinger, V Smelyanskiy, KJ Sung, MD Trevithick, A Vainsencher, B Villalonga, T White, ZJ Yao, P Yeh, A Zalcman, H Neven, JM Martinis, Quantum supremacy using a programmable superconducting processor, Nature 2019, 574, 505. https://doi.org/10.1038/s41586-019-1666-5

[11] HS Zhong, H Wang, YH Deng, MC Chen, LC Peng, YH Luo, J Qin, D Wu, X Ding, Y Hu, P Hu, XY Yang, WJ Zhang, H Li, YX Li, X Jiang, L Gan, GW Yang, LX You, Z Wang, L Li, NL Liu, CY Lu, JW Pan, Quantum computational advantage using photons, Science 2020, 370 (6523), 1460. DOI: 10.1126/science.abe8770

[12] M Deutscher, IBM debuts new quantum processor with 127 qubits, SiliconANGLE Media Inc. 2021 https://siliconangle.com/2021/11/15/ibm-debuts-new-eagle-quantum-processor-127-qubits/

[13] J Gambetta, IBM’s roadmap for scaling quantum technology, IBM 2020, https://research.ibm.com/blog/ibm-quantum-roadmap

[14] P Chapman, Scaling IonQ’s quantum computers: The roadmap, IonQ 2020, https://ionq.com/posts/december-09-2020-scaling-quantum-computer-roadmap

[15]  J Lee, DW Berry, C Gidney, WJ Huggins, JR McClean, N Wiebe, R Babbush, Even more efficient quantum computations of chemistry through tensor hypercontraction, PRX Quantum 2021, 2, 030305, https://doi.org/10.1103/PRXQuantum.2.030305

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