This document summarizes Total's exploration of quantum computing technology. It discusses how classical computing is reaching its limits and how quantum computing may provide new opportunities. Total's objectives are to understand quantum computer evolution, build competencies through research partnerships, and develop algorithms for business uses. Total has collaborated with several academic and industrial partners, purchased a 30-qubit quantum emulator, and has ongoing work in areas like quantum machine learning and optimization. The conclusion states that quantum computing is a major shift that could open new research frontiers, and Total aims to build skills to develop algorithms for its applications.

1. ORGANIZED BY
JUNE 20TH
2019
French industrial quantum use cases
Henri CALANDRA
Expert in Numerical algorithms and High Performance Computing for Geosciences at Total E&P
Scientific Advisor for Total Corporate Research, France

2. Quantum Computing @ TOTAL
Henri Calandra, TOTAL E&P

3. 3
Why explore Quantum computing
technology?
EXPLORE QUANTUM COMPUTING TECHNOLOGY
WHAT HAVE WE DONE SO FAR…
CONCLUSION

4. 4
Why explore Quantum computing technology?

5. Toward more and more complex classical HPC
systems
5
✓ Classical computers have fundamental limits:
▪ Transistor scaling
▪ Energy consumption
✓ HPC systems are likely to become much more heterogeneous and
massively-parallel systems
▪ Parallelism limitations: Adhams’law
✓ End of Moore’s law is expected by around 2025 !!

6. Compute requirements continue to grow
6
✓ Seismic depth imaging:
✓ compute better, faster
✓ More physics
✓ More data integration
✓ Uncertainty quantification
✓ Reservoir simulation:
✓ Compute faster
✓ Better predictability
✓ Multi real time simulations
✓ Inversion of subsurface models
✓ operations optimization,
✓ cost and risk reduction
✓ More complex targets:
✓ strong geological heterogeneity – several reservoirs
✓ Massive simulations:
✓ history matches; uncertainty Mgt on huge models
✓ New physics for EOR & integration of different processes incl. geomechanics

7. An there’s more we want to do
7
Machine learning, HPDA
✓ Development of new training set
and algorithms
✓ Classification and sampling of large
dataset
✓ Physics-constrained neural nets…
A
B
Liquid
Vapor
Combinatorial optimization
✓ MINLP (Mixed Integer Non Linear
programming) problems in general
including:
✓ Refinery blending,
✓ Scheduling, production, shipping.
✓ Oil field/reservoir optimization
✓ ….
Computational material science
✓ The ability to accurately model
ground states of fermionic systems
would have significant implications
for many areas of chemistry and
materials science:
✓ Catalysis, Solvents, Lubricants,
batteries…

8. An there’s more we want to do
8
Machine learning, HPDA
✓ Development of new training set
and algorithms
✓ Classification and sampling of large
dataset
✓ Physics-constrained neural nets…
A
B
Liquid
Vapor
Combinatorial optimization
✓ MNILP problems in general including:
✓ Refinery blending,
✓ Scheduling, production, shipping.
✓ Oil field/reservoir optimization
✓ ….
Computational material science
✓ The ability to accurately model
ground states of fermionic systems
would have significant implications
for many areas of chemistry and
materials science:
✓ Catalysis, Solvents, Lubricants,
batteries…
✓ Compute better, compute faster
✓ Solve intractable problems on classical High Performance Computing
Explore Quantum computing as a disruptive technology ?

9. 9
explore Quantum computing technology

10. 1
0
QUANTUM HARDWARE DEVELOPMENT IS ACCELERATING
✓ A groundbreaking new approach to pave the way for the future of scientific computing
beyond exascale ?

11. A very challenging technology
✓Quantum Hardware comes in many forms (supra-conductors, ion trap, photonics…)
✓ Still limited to few ten’s of qubits NISQ (Noisy Intermediate scale Quantum device)
From Denis Vion, CEA, 41th Orap Forum
IBM
Google
Rigetti
IONQ
XANADU

12. OBJECTIVEs
12
✓ Understand Quantum Computers technology evolution
D’WAVE computer
IBM Q
Google: bristlecone Rigetti : 16Q Aspen
✓ Accelerate and build in-house competencies skill set with research
partners and hardware providers ecosystem to develop algorithms for
Total business use cases
✓ Be ready when industrial quantum computers become available and quantum supremacy is
demonstrated.
✓ Quantum computing is a huge paradigm shift and Quantum algorithmics is a brand new science

13. 13
Anticipated Impact ( what we expect)
✓ Compute better, compute faster
✓Open new frontiers in R&D for Chemistry, material science, optimization, machine learning,….
Quantum linear algebra
(qublas), solving ODEs. PDEs,
inverse problems…
Quantum
combinatorial
optimization
Quantum machine
learning
Quantum Chemistry

14. 14
What have we done so Far…

15. Define an appropriate R&D roadmap and potential
Use CASES
Quantum combinatorial
optimization
Quantum machine learningQuantum Chemistry
NISQ device ~ (102 qubits) 3-5 years (pre)-QEC device ~ (103(-6) qubits) 5+ (++?) years
Quantum linear algebra
(qublas), solving ODEs. PDEs,
inverse problems…
15

16. Quantum Computing at Total
Q Hardware Q Software Applications
Math libraries
ATOS QLM - emulator
Verification: simulation, benchmarking, testing
Chemistry, Material
Science
Optimization, Machine
Learning
Hybrid (QC-HPC)
Gate-based (IBM, Rigetti,
google…)
Annealer ( D-Wave,….)
Programming models
Hybrid computing ODEs, PDEs, linear algebra,
inverse problem…
Quantum computing global program overview
16

17. 17
academic and industrial collaborations network
Collaboration agreement, purchase of a 30 and 35 qubits QLM
ATOS QLM-30 system, installed September 2018,
ATOS QLM 35 Qubits system upgrade in progress
Quantum Machine learning
for industrial applications.
Ph.D. 07/01/2019-
06/30/2024
Quantum Derivative free
gradient optimization for
inverse problems
Ph.D. 10/01/2019-09/30/2024
Jülich Unified Infrastructure for Quantum computing (JUNIQ)
2 months internship on quantum micro benchmark
Quantum Gibbs sampling on
NISQ devices. 2 years PostDoc,
09/01/2019-08/31/2021
EU-Project (FETFLAG-QUANTUM): PasQuans
Quantum Seismic Imaging, 2 years postdoc,
07/01/2019-06/30/2021
Qualitative Computing for Quantum Computing,
2 years postdoc, 07/01/2019-06/30/2019
Construction of a QBLAS library, Ph.D. 10/01/2019-09/30/2022

18. 18
Get one’s hands dirty in Programming models and
algorithm design✓ Examples of on going dev:
✓ NISQ oriented applications development on ATOS QLM : VQE and QAOA
VQE
QAOA
✓ Understand existing implementations: QISKIT (IBM), Pyquil (Rigetti)….
✓ Understand actual limitations (HW and algorithm) and possible test cases.
✓ « hybrid » programming , mixing quantum speedup procedures and classical ones

19. conclusion
✓ Quantum computing is a huge paradigm shift and Quantum
algorithmics is a brand new science
19
GROVER ALGORITHM
✓ Quantum computing can provide new opportunities,
opening new frontiers in R&D in many fields of application.
✓ Set up a good academic, partners and industrial network
based on an appropriate research program
✓ Build internal skills to develop algorithms for Total business use case
✓ Quantum Computing for Industrial Applications Project:
✓ 2018: project design, collaboration setup , purchase of a QLM30 qubits
✓ 2019:
✓ 2(3) Total researchers,
✓ 3 Postdocs and 3 PhDs,
✓ Upgrade to a QLM 35 qubits
✓ Develop internal skills on NISQ devices