Dr Nicole Holzmann
Riverlane, Cambridge, United Kingdom
Quantum computers have the potential to impact a wide range of industries  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?
 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
 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
 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
 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
 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
 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
 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
 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
 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]
 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
 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
 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/
 J Gambetta, IBM’s roadmap for scaling quantum technology, IBM 2020, https://research.ibm.com/blog/ibm-quantum-roadmap
 P Chapman, Scaling IonQ’s quantum computers: The roadmap, IonQ 2020, https://ionq.com/posts/december-09-2020-scaling-quantum-computer-roadmap
 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