François Chung, Ph.D.

Tag: digital circuit

Cybersecurity specialization

Cybersecurity specialization

Coursera training, MOOC (2022). This specialization from The University of Maryland (US) covers the fundamental concepts underlying the construction of secure systems, including the hardware, the software and the human-computer interface, with the use of cryptography to secure interactions. These concepts are illustrated with examples drawn from modern practice, and augmented with hands-on exercises involving relevant tools and techniques.

Course 1: Usable security

Main topics:

  • Human-Computer Interaction (HCI);
  • Design methodology and prototyping;
  • A/B testing, quantitative and qualitative evaluation;
  • Secure interaction design;
  • Biometrics, two-factor authentication (2FA);
  • Privacy settings, data inference.

Course 2: Software security

Main topics:

  • Low-level security: attacks and exploits;
  • Defending against low-level exploits:
  • Web security: attacks and defenses;
  • Designing and building secure software;
  • Static program analysis;
  • Penetration and fuzz testing.

Course 3: Cryptography

Main topics:

  • Computational secrecy and modern cryptography;
  • Private-key encryption;
  • Message authentication codes;
  • Number theory;
  • Key exchange and public-key encryption;
  • Digital signatures.

Course 4: Hardware security

Main topics:

  • Digital system design: basics and vulnerabilities;
  • Designing intellectual property protection;
  • Physical attacks and modular exponentiation;
  • Side-channel attacks and countermeasures;
  • Hardware trojan detection;
  • Trusted integrated circuit;
  • Good practice and emerging technologies.

References

Training

Usable security (course certificate)
Software security (course certificate)
Cryptography (course certificate)
Hardware security (course certificate)

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Quantum computing and physics

Quantum computing and physics

Udemy training, MOOC (2020). This online training presents quantum computing as the next wave of the software industry. Quantum computers are exponentially faster than classical computers of today. Problems that were considered too difficult for computers to solve, such as simulation of protein folding in biological systems and cracking RSA encryption, are now possible through quantum computers. The training is primarily about analyzing the behavior of quantum circuits using math and quantum physics.

Section 1: Introduction

Main topics:

  • Why learn about quantum computing?
  • How is quantum computing different?

Section 2: Quantum cryptography

Main topics:

  • Experiments with photon polarization;
  • No-cloning theorem;
  • Encoding with XOR;
  • Encryption with single-use shared-secrets;
  • Encoding data in photon polarization.

Section 3: Foundation

Main topics:

  • Probability;
  • Complex numbers;
  • Matrix algebra;
  • Matrix multiplication;
  • Logic circuits.

Section 4: Math model for quantum physics

Main topics:

  • Modeling physics with math;
  • Substractive probabilities through complex numbers;
  • Modeling superposition through matrices.

Section 5: Quantum physics of spin states

Main topics:

  • Matrix representation of quantum state;
  • State vector;
  • Experiments with spin.

Section 6: Modeling quantum spin states with math

Main topics:

  • Analysis of experiments;
  • Dirac bra-ket notation;
  • Random behavior.

Section 7: Reversible and irreversible state transformations

Main topics:

  • Irreversible transformations measurement;
  • Reversible state transformations.

Section 8: Multi-qubit systems

Main topic:

  • Multi-qubit systems.

Section 9: Quantum entanglement

Main topic:

  • Quantum entanglement.

Section 10: Quantum computing model

Main topics:

  • Quantum circuits;
  • Reversible gates;
  • CNOT and CCNOT gates;
  • Universal and Fredkin gates;
  • Superposition and entanglement on quantum gates.

Section 11: Quantum programming with Microsoft Q#

Main topics:

  • Q# simulator hardware architecture;
  • Measuring superposition states;
  • Effect of superposition on quantum gates;
  • Toffoli gate;
  • Programming quantum computers.

Section 12: IBM quantum experience

Main topic:

  • IBM quantum experience.

Section 13: Conclusion

Main topic:

  • Speedup revisited.

References

Training

Related articles

Digital Annealer (Fujitsu project)
DataNews 2020 (FR) (magazine article, French version)
DataNews 2020 (NL) (magazine article, Dutch version)

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DataNews 2020 – Magazine article

DataNews 2020 (FR) – Magazine article

Publication

François Chung; Combler le fossé quantique, aujourd’hui; DataNews, 2, p. 5, 2020.

Abstract

L’informatique quantique permettra de solutionner des problèmes complexes, impossibles à résoudre avec les ordinateurs d’aujourd’hui. Le Digital Annealer de Fujitsu offre une alternative à l’informatique quantique encore trop coûteuse et difficile à exécuter.

References

Publication

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Quantum computing and physics (Udemy training)
DataNews 2020 (NL) (magazine article, Dutch version)
Digital Annealer (Fujitsu project)

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DataNews 2020 – Magazine article

DataNews 2020 (NL) – Magazine article

Publication

François Chung; De kwantumkloof dichten, vandaag de dag; DataNews, 2, p. 5, 2020.

Abstract

Kwantuminformatica biedt een oplossing voor complexe problemen, die niet kunnen opgelost worden met de huidige computersystemen. De Digital Annealer van Fujitsu biedt een alternatief voor de kwantuminformatica, die momenteel nog te duur en te moeilijk uit te voeren is.

References

Publication

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Quantum computing and physics (Udemy training)
DataNews 2020 (FR) (magazine article, French version)
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Digital Annealer

Digital Annealer

Fujitsu project @Brussels, Belgium (2019). Using a digital circuit design inspired by quantum phenomena, Fujitsu’s Digital Annealer (DA) computational architecture bridges the gap to the quantum world and paves the way for much faster, more efficient solving of today’s business problems. The solution is designed to solve large-scale combinatorial optimization problems, which are unsolvable using today’s classical computers.

Among the various quantum computing methods that exist in the market today, DA is categorized as an example of the annealing method, which focuses on solving combinatorial optimization problems and the achievement of successful results with rapid operational capabilities. Unlike classical computers, Digital Annealing does not require programming, simply setting parameters allows calculations to be performed.

DA solution is applicable for a wide range of use cases, across various sectors, such as:

  • Finance: investment portfolio optimization through risk diversification;
  • Pharmaceutical: molecular similarity search for drug discovery;
  • Marketing: clustering for big data utilization;
  • Logistics: route optimization for reducing traffic congestion;
  • Manufacturing: manpower management, production control scheduling.

Within the Digital Business Solutions (DBS) team, my role consists in supporting DA activities in Belgium and across EMEIA region, and includes tasks related to presales (e.g. DA solution presentation), business analysis (e.g. analyzing client’s business needs), data science (e.g.converting optimization problems into mathematical formulation) and project management (e.g. project coordination during the implementation phase).

References

Related articles

Quantum computing and physics (Udemy training)
DataNews 2020 (FR) (magazine article, French version)
DataNews 2020 (NL) (magazine article, Dutch version)

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