The Quantum Landscape

Transcription

1 The Quantum Landscape

2 Computational drug discovery employing machine learning and quantum computing Contact us! Or visit our blog to learn 2

3 Applications What is a quantum computer good for?

4 Simulating nature

5 Quantum chemistry High-Accuracy Quantum Calc. Calculating the electronic structure of molecules ranks among the most computationally intensive tasks in all scientific calculations Utilized for calculating: Quantum chemistry simulations can help understand the fundamental properties of small molecules 1 IBM Conference 2017 Suppl. Reference: Reiher et al. (2017). Proceed. of the Nat. Acad. of Sciences. Likely to require full quantum error correction and millions of qubits (5-10 years)1 Drug properties Target sites of reactivity Model refinement Discovery of chemical catalysts Effects of water

6 ProteinQure: protein folding/design Model energy minimization processes (such as protein folding) on quantum computers to predict structure Effects such as quantum tunneling speed up calculations Quantum Annealing, D-Wave (2012) Tunneling helps find optimal solutions Protein design requires predicting the 3D shape of proteins which can be solved faster on QCs Hybrid (classical + quantum) approaches viable in the next 2 years Suppl. Reference: Internal PQ Whitepaper I and II

7 Quantum machine learning Machine learning and quantum theory are both deeply rooted in linear algebra and, thus, QCs promise huge speedups in AI and Big Data processing Quantum machine learning most valuable for applications with sparse data and complicated probability distributions Big data approaches are far away (5+ years), but clever short term applications may exist Suppl Reference: Biamonte et al. (2017). Nature, 549(7671), 195.

8 Alternative possibilities Virtual high throughput screens and lead optimization using molecular similarity search Suppl. References: Graph-based methods for molecular similarity search. Hernandez et et al. arxiv (2016) Quantum annealing for a binding prediction Li, Richard et al. npj Quantum Info (2018) Predict active binding sites and active parts of molecules in docking interactions We are still in early stages of exploring what biological problems can map to quantum computers Hybrid (classical + quantum) approaches viable in 2-3 years

9 Quantum strategy How does the present and future look like?

10 Quantum hardware: the big players Universal Superconducting Quantum Computers Quantum Annealers Other Universal QC Hardware Digital Annealers Xanadu The hardware architectures are very different, need to experiment for your applications It is difficult to do apples to apples comparisons (e.g. determining which is the best)

11 Quantum computing metrics Key measurements of quantum horsepower # of qubits: How big a calculation can we do? Connectivity: How connected are the qubits? Circuit depth: How many operations can you perform on each qubit? Error rates: How likely are the calculations to be correct? There are tradeoffs between attributes, but hardware providers are trying to demonstrate quantum computational supremacy E.g. find a problem where the quantum computer is better than classical computers

12 Quantum volume IBM has tried to come up with a catch all term to represent computational power: Quantum Volume Combines many metrics into a single number representing computational capability Incorporates the ultimate goal of lots of qubits with low errors Source: IBM Research

13 Current State (Apr 2018) Metric Current Best Requirements for Quantum Computational Supremacy Connectivity Pairwise Pairwise connectivity likely to be standard for medium term1 Circuit depth % 0.5% # of qubits Error Rates Fig. State of quantum computers for gate-based architectures (IBM, Google etc.) 1 2 Google 2018 Estimates for commercial problems by PQ team

14 Quantum computing - what s next? What do the experts say (IBM Q conference - Dec 2017)? Quantum chemistry large-scale application: 5-10 years until fruition Fully error-corrected quantum computer: years until fruition BUT! Near-term quantum computing (2-3 years) can be powerful if you know what you re doing. > clever hybrid algorithms > treating it as a subcomponent for heavy lifting

15 Quantum software: the players General quantum software: Strangeworks Dedicated life science applications:

16 Quantum machine learning incubator

17 Advice Quantum developers are needed but almost non existent > hard to train classical programmers to program quantum > super low-level programming (machine instructions) > Combines expertise in multiple extremely technical fields Within the next 2-3 years you should be more focused on proof-of-concept and ensuring you are on the frontier of high performance computing > advantage since no one knows when it will take off (advances are happening faster than people expected)

18 Technical Appendix: Classical vs. quantum A short overview of computation

19 Resources to learn more Quantum computing explained Quantum computational supremacy explained Quantum computing Q&A A gentle introduction to quantum mechanics sskind/dp/ /ref=sr_1_3?ie=utf8&qid= &sr=8-3&key words=the+theoretical+minimum