Mediaboss Marketing

Mon 11 Mar 2024 4 min of reading by Eddy S.

Developers of Ethereum, the $200 billion crypto ecosystem, have sounded the call to arms. Their mission? To protect the millions of digital assets held on the network from the clutches of a new kind of enemy quantum computers. The first round of this unprecedented battle for survival is fast approaching.

A threat full of power and mystery looms on the horizon. These quantum computing monsters, still in development, could one day crack the crypto codes that secure Ethereum wallets.

In the blink of a digital eye, billions of dollars in ETH and other assets could be stolen. A digital apocalypse hovers, threatening to obliterate Ethereum as we know it. Time is of the essence to counter this technological plague before it becomes a reality.

Developers have no choice but to act, and to do so quickly. To undertake a massive preemptive counter-attack. A decisive action to save the flagship of the crypto ecosystem before its engulfed by the raging waters of the next quantum revolution.

It was Vitalik Buterin, the visionary behind Ethereum, who lit the fuse. An emergency plan will be put in place to secure the network a hard fork of a magnitude equal to the threat it faces.

The first crucial step: to completely disable transactions from the classic wallets. Too vulnerable to quantum attacks. Instead, new smart wallets will take over. Built upon the very structure of the Ethereum blockchain, they will benefit from crypto armor resistant to the capabilities of these future quantum monsters.

But the cornerstone of this renaissance operation lies in the integration of STARK proofs (Scalable Transparent Arguments of Knowledge). A mechanism that will enable users to reliably verify their ownership of assets without having to expose their private keys, even to the verification system itself. A cutting-edge cryptographic breakthrough.

A transitional mechanism will also be deployed to allow holders to safely migrate their funds to this new fortified system. A renaissance that will not come without pain for users, forced to undergo a lengthy software update process. But it is a necessary sacrifice on the altar of resilience against the existential threat posed by quantum computers.

Today, Ethereum once again defies the doubts of skeptics to lead the way to a new post-quantum world. Where digital ownership will withstand the onslaught of mass destruction weapons that will be quantum computers. A new era that remains science fiction for now, but towards which the leading network of the blockchain is advancing, prepared for battle. Ready to secure a decisive victory for free crypto!

Maximize your Cointribune experience with our 'Read to Earn' program! Earn points for each article you read and gain access to exclusive rewards. Sign up now and start accruing benefits.

Le monde volue et l'adaptation est la meilleure arme pour survivre dans cet univers ondoyant. Community manager crypto la base, je m'intresse tout ce qui touche de prs ou de loin la blockchain et ses drivs. Dans l'optique de partager mon exprience et de faire connatre un domaine qui me passionne, rien de mieux que de rdiger des articles informatifs et dcontracts la fois.

DISCLAIMER

The views, thoughts, and opinions expressed in this article belong solely to the author, and should not be taken as investment advice. Do your own research before taking any investment decisions.

See more here:
Ethereum: Vitalik is preparing for the war against quantum computers! - Cointribune EN

The solution, Quantum Noise Injection for Adversarial Defence (QNAD), counteracts the impact of adversarial attacks designed to disrupt the interference of quantum computers. This is AIs ability to make decisions or solve tasks.

Adversarial attacks designed to disrupt AI inference have the potential for serious consequences, said Dr Kanad Basu, assistant professor of electrical and computer engineering at the Erik Jonsson School of Engineering and Computer Science.

The work will be presented at the IEEE International Symposium on Hardware Oriented Security and Trust on 6-9 May in Washington, DC.

Quantum computers can solve several complex problems exponentially faster than classical computers. The emerging technology uses quantum mechanics and is expected to improve AI applications and solve complex computational problems.

Qubits represent the fundamental unit of information in quantum computers, like bits in traditional computers.

In classical computers, bits represent 1 or 0. However, qubits take advantage of the principle of superposition and can, therefore, be in a state of 0 and 1. By representing two states, quantum computers have greater speed compared to traditional computers.

For example, quantum computers have the potential to break highly secure encryption systems due to their computer power.

Despite their advantages, quantum computers are vulnerable to adversarial attacks.

Due to factors such as temperature fluctuations, magnetic fields, and imperfections in hardware components, quantum computers are susceptible to noise or interference.

Quantum computers are also prone to unintended interactions between qubits.

These challenges can cause computing errors.

The researchers leveraged intrinsic quantum noise and crosstalk to counteract adversarial attacks.

The method introduced crosstalk into the quantum neural network. This is a form of Machine Learning where datasets train computers to perform tasks. This includes detecting objects like stop signs or other computer vision responsibilities.

The noisy behaviour of quantum computers actually reduces the impact of attacks, said Basu, who is senior author of the study. We believe this is a first-of-its-kind approach that can supplement other defences against adversarial attacks.

The researchers revealed that during an adversarial attack, the AI application was 268% more accurate with QNAD than without it.

The approach is designed to supplement other techniques to protect quantum computer security.

In case of a crash, if we do not wear the seat belt, the impact of the accident is much greater, Shamik Kundu, a computer engineering doctoral student and a first co-author, said.

On the other hand, if we wear the seat belt, even if there is an accident, the impact of the crash is lessened. The QNAD framework operates akin to a seat belt, diminishing the impact of adversarial attacks, which symbolise the accident, for a QNN model.

The research was funded by the National Science Foundation.

Original post:
Protecting quantum computers from adversarial attacks - Innovation News Network

SemiQon, a Finland-based start-up specializing in silicon-based quantum processors, has announced it has successfully manufactured and pre-tested a 4-qubit quantum dot array from the first production run at its manufacturing facility in Espoo, Finland. The new chips are now shipping to strategic partners around the world as a toolkit for further research and development. The aim is to help make building stable logical qubits easier and faster to accelerate the use of quantum computing for complex problems.

First-generation quantum computers have already achieved impressive computational feats. However, solving highly specific problems related to pharmaceuticals, logistics, space, and material design will require increased computational power. As researchers, ecosystems, and companies around the globe lay out their ambitious visions for quantum computing, the computing power must still be scaled efficiently to address these challenges. Current methods do not make this possible.

Source: SemiQon

We are gradually moving towards the million qubit era and the contribution of hardware is becoming more and more essential,saysDr. Himadri Majumdar, CEO and Co-founder of SemiQon.Our solution builds on the technological development and know-how of semiconductors and benefits from existing infrastructure and industry. Utilizing such infrastructure effectively and efficiently has allowed us to accomplish one of our first goals within a short period of time. The challenge is getting to quantum supremacy in a sustainable, scalable, and affordable manner. These new chips are our first step in a long journey to making quantum dreams a reality.

SemiQons strategic path of combining classical and quantum elements at cryogenic temperatures also took a big leap forward through the demonstration of very low noise and better control over the channel using record low sub-threshold swing in the manufactured fully-depleted silicon-on-insulator metal-on-semiconductor (FDSOI-MOS) transistors. These transistors will be the backbone of realizing a cryogenic integrated circuit (IC), ultimately leading to quantum IC for scalable, efficient, and affordable quantum computers.

The results will be communicated through a peer-reviewed international scientific article, which is currently under review.

Source: SemiQon

Read the original post:
SemiQon tests and ships its silicon-based 4-qubit quantum chip - Electronic Products & Technology

Hadfield, S. et al. From the quantum approximate optimization algorithm to a quantum alternating operator Ansatz. Algorithms 12, 34 (2019).

Article MathSciNet Google Scholar

Cook, J., Eidenbenz, S. & Brtschi, A. The quantum alternating operator Ansatz on maximum k-Vertex cover. In IEEE International Conference on Quantum Computing and Engineering QCE20, 8392 (2020). https://doi.org/10.1109/QCE49297.2020.00021.

Wang, Z., Rubin, N. C., Dominy, J. M. & Rieffel, E. G. XY mixers: Analytical and numerical results for the quantum alternating operator ansatz. Phys. Rev. A 101, 012320 (2020).

Article ADS MathSciNet CAS Google Scholar

Farhi, E., Goldstone, J. & Gutmann, S. A Quantum Approximate Optimization Algorithm. arXiv preprint (2014). https://doi.org/10.48550/arXiv.1411.4028.

Farhi, E., Goldstone, J. & Gutmann, S. A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem. arXiv preprint (2015). https://doi.org/10.48550/arXiv.1412.6062.

Farhi, E., Goldstone, J., Gutmann, S. & Sipser, M. Quantum computation by adiabatic evolution. arXiv preprint (2000). https://doi.org/10.48550/arXiv.quant-ph/0001106.

Kadowaki, T. & Nishimori, H. Quantum annealing in the transverse ising model. Phys. Rev. E 58, 53555363 (1998).

Article ADS CAS Google Scholar

Das, A. & Chakrabarti, B. K. Quantum annealing and analog quantum computation. Rev. Mod. Phys. 80, 1061 (2008).

Article ADS MathSciNet Google Scholar

Hauke, P., Katzgraber, H. G., Lechner, W., Nishimori, H. & Oliver, W. D. Perspectives of quantum annealing: methods and implementations. Rep. Prog. Phys. 83, 054401 (2020).

Article ADS CAS PubMed Google Scholar

Yarkoni, S., Raponi, E., Bck, T. & Schmitt, S. Quantum annealing for industry applications: Introduction and review. Rep. Prog. Phys. 85, 104001 (2022).

Article ADS MathSciNet Google Scholar

Morita, S. & Nishimori, H. Mathematical foundation of quantum annealing. J. Math. Phys. 49, 125210 (2008).

Article ADS MathSciNet Google Scholar

Santoro, G. E. & Tosatti, E. Optimization using quantum mechanics: Quantum annealing through adiabatic evolution. J. Phys. A: Math. Gen. 39, R393 (2006).

Article ADS MathSciNet CAS Google Scholar

Finnila, A. B., Gomez, M., Sebenik, C., Stenson, C. & Doll, J. D. Quantum annealing: A new method for minimizing multidimensional functions. Chem. Phys. Lett. 219, 343348 (1994).

Article ADS CAS Google Scholar

Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473, 194198 (2011).

Article ADS CAS PubMed Google Scholar

Lanting, T. et al. Entanglement in a quantum annealing processor. Phys. Rev. X 4, 021041 (2014).

Google Scholar

Boixo, S., Albash, T., Spedalieri, F. M., Chancellor, N. & Lidar, D. A. Experimental signature of programmable quantum annealing. Nat. Commun. 4, 2067 (2013).

Article ADS PubMed Google Scholar

King, A. D. et al. Coherent quantum annealing in a programmable 2000-qubit Ising chain. Nat. Phys. 18, 13241328 (2022).

Article CAS Google Scholar

Chow, J. M. et al. Simple all-microwave entangling gate for fixed-frequency superconducting qubits. Phys. Rev. Lett. 107, 080502 (2011).

Article ADS PubMed Google Scholar

Chamberland, C., Zhu, G., Yoder, T. J., Hertzberg, J. B. & Cross, A. W. Topological and subsystem codes on low-degree graphs with flag qubits. Phys. Rev. X 10, 011022 (2020).

CAS Google Scholar

Tasseff, B. et al. On the emerging potential of quantum annealing hardware for combinatorial optimization. arXiv preprint (2022). https://doi.org/10.48550/arXiv.2210.04291.

Sanders, Y. R. et al. Compilation of fault-tolerant quantum heuristics for combinatorial optimization. PRX Quantum 1, 020312 (2020).

Article Google Scholar

Lotshaw, P. C. et al. Scaling quantum approximate optimization on near-term hardware. Sci. Rep. 12, 12388 (2022).

Article ADS CAS PubMed PubMed Central Google Scholar

Albash, T. & Lidar, D. A. Demonstration of a scaling advantage for a quantum annealer over simulated annealing. Phys. Rev. X 8, 031016 (2018).

CAS Google Scholar

King, A. D. et al. Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets. Nat. Commun. 12, 1113 (2021).

Article ADS CAS PubMed PubMed Central Google Scholar

Farhi, E. & Harrow, A. W. Quantum supremacy through the quantum approximate optimization algorithm. arXiv preprint (2019). https://doi.org/10.48550/arXiv.1602.07674.

Brady, L. T., Baldwin, C. L., Bapat, A., Kharkov, Y. & Gorshkov, A. V. Optimal protocols in quantum annealing and quantum approximate optimization algorithm problems. Phys. Rev. Lett. 126, 070505 (2021).

Article ADS MathSciNet CAS PubMed Google Scholar

Willsch, M., Willsch, D., Jin, F., De Raedt, H. & Michielsen, K. Benchmarking the quantum approximate optimization algorithm. Quantum Inf. Process. 19, 197 (2020).

Article ADS MathSciNet Google Scholar

Sack, S. H. & Serbyn, M. Quantum annealing initialization of the quantum approximate optimization algorithm. Quantum 5, 491 (2021).

Article Google Scholar

Golden, J., Brtschi, A., Eidenbenz, S. & OMalley, D. Numerical Evidence for Exponential Speed-up of QAOA over Unstructured Search for Approximate Constrained Optimization. In IEEE International Conference on Quantum Computing and Engineering QCE23, 496505 (2023). https://doi.org/10.1109/QCE57702.2023.00063.

Golden, J., Brtschi, A., OMalley, D. & Eidenbenz, S. The Quantum Alternating Operator Ansatz for Satisfiability Problems. In IEEE International Conference on Quantum Computing and Engineering QCE23, 307312 (2023). https://doi.org/10.1109/QCE57702.2023.00042.

Binkowski, L., Komann, G., Ziegler, T. & Schwonnek, R. Elementary Proof of QAOA Convergence. arXiv preprint (2023). https://doi.org/10.48550/arXiv.2302.04968.

Lubinski, T. et al. Optimization Applications as Quantum Performance Benchmarks. arXiv preprint (2024). https://doi.org/10.48550/arXiv.2302.02278.

Pelofske, E., Golden, J., Brtschi, A., OMalley, D. & Eidenbenz, S. Sampling on NISQ Devices: Whos the Fairest One of All?. In IEEE International Conference on Quantum Computing and Engineering QCE21, 207217 (2021). https://doi.org/10.1109/qce52317.2021.00038.

Ushijima-Mwesigwa, H. et al. Multilevel combinatorial optimization across quantum architectures. ACM Trans. Quantum Comput. 2, 1:11:29 (2021).

Article MathSciNet Google Scholar

Streif, M. & Leib, M. Comparison of QAOA with quantum and simulated annealing. arXiv preprint (2019). https://doi.org/10.48550/arXiv.1901.01903.

Pelofske, E., Brtschi, A. & Eidenbenz, S. Quantum Annealing vs. QAOA: 127 Qubit Higher-Order Ising Problems on NISQ Computers. In International Conference on High Performance Computing ISC HPC23, 240258 (2023). https://doi.org/10.1007/978-3-031-32041-5_13.

Suau, A. et al. Single-Qubit Cross Platform Comparison of Quantum Computing Hardware. In IEEE International Conference on Quantum Computing and Engineering QCE23, 13691377 (2023). https://doi.org/10.1109/QCE57702.2023.00155.

Pagano, G. et al. Quantum approximate optimization of the long-range ising model with a trapped-ion quantum simulator. Proc. Natl. Acad. Sci. 117, 2539625401 (2020).

Article ADS MathSciNet CAS PubMed PubMed Central Google Scholar

Weidenfeller, J. et al. Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware. Quantum 6, 870 (2022).

Article Google Scholar

Harrigan, M. P. et al. Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. Nat. Phys. 17, 332336 (2021).

Article CAS Google Scholar

Herman, D. et al. Constrained optimization via quantum Zeno dynamics. Commun. Phys. 6, 219 (2023).

Article Google Scholar

Niroula, P. et al. Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer. Sci. Rep. 12, 17171 (2022).

Article ADS CAS PubMed PubMed Central Google Scholar

Zhou, L., Wang, S.-T., Choi, S., Pichler, H. & Lukin, M. D. Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices. Phys. Rev. X 10, 021067 (2020).

CAS Google Scholar

Basso, J., Farhi, E., Marwaha, K., Villalonga, B. & Zhou, L. The quantum approximate optimization algorithm at high depth for maxcut on large-girth regular graphs and the Sherrington-Kirkpatrick Model. In 17th Conference on the Theory of Quantum Computation, Communication and Cryptography TQC22 (2022). https://doi.org/10.4230/LIPICS.TQC.2022.7.

Wang, Z., Hadfield, S., Jiang, Z. & Rieffel, E. G. Quantum approximate optimization algorithm for MaxCut: A fermionic view. Phys. Rev. A 97, 022304 (2018).

Article ADS CAS Google Scholar

Crooks, G. E. Performance of the quantum approximate optimization algorithm on the maximum cut problem. arXiv preprint (2018). https://doi.org/10.48550/arXiv.1811.08419.

Guerreschi, G. G. & Matsuura, A. Y. QAOA for Max-Cut requires hundreds of qubits for quantum speed-up. Sci. Rep. 9, 6903 (2019).

Article ADS CAS PubMed PubMed Central Google Scholar

Marwaha, K. Local classical MAX-CUT algorithm outperforms p=2 QAOA on high-girth regular graphs. Quantum 5, 437 (2021).

Article Google Scholar

Hastings, M. B. Classical and quantum bounded depth approximation algorithms. Quantum Inf. Comput. 19, 11161140 (2019).

MathSciNet Google Scholar

Saleem, Z. H. Max independent set and quantum alternating operator Ansatz. Int. J. Quantum Inf. 18, 2050011 (2020).

Article MathSciNet Google Scholar

de la Grandrive, P. D. & Hullo, J.-F. Knapsack Problem variants of QAOA for battery revenue optimisation. arXiv preprint (2019). https://doi.org/10.48550/arXiv.1908.02210.

Farhi, E., Goldstone, J., Gutmann, S. & Zhou, L. The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick model at infinite size. Quantum 6, 759 (2022).

Article Google Scholar

Jiang, S., Britt, K. A., McCaskey, A. J., Humble, T. S. & Kais, S. Quantum annealing for prime factorization. Sci. Rep. 8, 17667 (2018).

Article ADS PubMed PubMed Central Google Scholar

Ji, X., Wang, B., Hu, F., Wang, C. & Zhang, H. New advanced computing architecture for cryptography design and analysis by D-Wave quantum annealer. Tsinghua Sci. Technol. 27, 751759 (2022).

Article Google Scholar

Dridi, R. & Alghassi, H. Prime factorization using quantum annealing and computational algebraic geometry. Sci. Rep. 7, 43048 (2017).

Article ADS CAS PubMed PubMed Central Google Scholar

Peng, W. et al. Factoring larger integers with fewer qubits via quantum annealing with optimized parameters. Sci. China Phys., Mech. Astron. 62, 60311 (2019).

Article ADS Google Scholar

Warren, R. H. Factoring on a quantum annealing computer. Quantum Inf. Comput. 19, 252261 (2019).

MathSciNet Google Scholar

Titiloye, O. & Crispin, A. Quantum annealing of the graph coloring problem. Discret. Optim. 8, 376384 (2011).

Article MathSciNet Google Scholar

Kwok, J. & Pudenz, K. Graph coloring with quantum annealing. arXiv preprint (2020). https://doi.org/10.48550/arXiv.2012.04470.

See the article here:
Short-depth QAOA circuits and quantum annealing on higher-order ising models | npj Quantum Information - Nature.com

Companies such as Google and IBM have been working on quantum computers (QCs) for well over a decade. They assert QCs will be an order of magnitude faster when pitted against traditional computers. And, according to experts, one aspect of computing theyll have a profound impact on is cybersecurity.

While breakthroughs about QCs hit newswires occasionally, they still remain firmly in the domain of research. In fact, Google and XPRIZE have just announced a $5-million-dollar competition to help find a real-world use of the technology.

But if QCs arent here yet, why should businesses invest in defending against them?

David McNeely, CTO at Delinea, which provides privileged access management (PAM) solutions, believes while the immediate risk appears low, its about time companies start assessing their options.

Read |Investopia 2023: A look at quantum computing and AI

For one, he says, quantum computing is advancing rapidly. According to some theories, it is growing much faster than traditional computing, which follows Moores Law, says McNeely. If we take into account the increased financial investments made into quantum technology, the prospect of an exponentially faster, usable quantum computer by 2030 becomes more of a possible reality.

Commenting on whats at stake, McNeely says encryption secures our online interactions, but the rise of (QCs) poses a significant threat as these powerful machines have the potential to break current encryption algorithms.

Greg Welch, CEO, CyberProtonics, which develops quantum-proof cybersecurity solutions, agrees. Its not a question of if Q-day is coming, its a matter of when. Q-Day is the hypothetical day when QCs will be able to crack our public encryption systems.

According to Welch, Q-day threats arent just for corporate networks. He argues that with the prevalence of remote work, the new edge of the corporate network is the remote office.

Read: UAE identifies 10 future trends for the next decade

Any internet-connected device at home can be impacted by Q-day attacks, from streaming services to connected IoT devices and your home WiFi connected devices, says Welch. Organizations encrypting all their data at the source of creation can help those [remote] users secure their entire connected home.

From a security perspective, McNeely says it is always a good idea to stay one step ahead of cyber criminals.

He agrees that while QCs cant yet break current encryption, its reasonable to assume that hackers with resources, such as nation-state actors, are already thinking about how they will exploit this new technology.

He backs his argument pointing to a 2022 Deloitte poll, which highlighted the risk of criminals adopting the hack/harvest now, decrypt later (HNDL) technique. HNDL involves hackers stealing the encrypted data now, and then waiting until they can run it through a QC when available.

David Boast, general manager, MENA at Endava which among other things helps businesses secure their software agrees with this view. He says HNDL attacks warrant that organizations take a critical look at their present-day infrastructures and security measures.

Read |Investopia 2023: Quantum computing investment is the new economy next 2 years

Welch quantifies the threat pointing to estimates that note that theres a cyberattack every 11 seconds. He says the average financial impact of an attack for an affected organization is expected to be upwards of $10 million, adding up to a staggering $10.5 trillion.

Organizations should therefore invest now to protect against breaches and recognize encryption as just a part of a multi-layered security environment that must account for people, processes, and technologies, suggests Boast.

In that aspect, McNeely shares that the industry already has the technology to secure data with new types of encryption. He says the US National Institute of Standards and Technology (NIST) is currently working on standardizing post-quantum cryptography algorithms.

In fact, he says there are several promising candidates. He particularly points to IBMs CRYSTALS-Kyber algorithm, which is based on a mathematical problem that is much harder for QCs to solve. This makes it more resistant to both conventional and quantum attacks.

As per Boast, the fear of quantum threats will breathe life into a healthy market of quantum protection solutions. However, he believes, the actual appetite, pace and need for adoption of quantum-resistant security depends on several factors.

The challenge here will be to identify the right point at which to invest, says Boast. As with any rapidly maturing technology, the cost to adoption can be expected to rapidly decrease, thus almost burdening first movers.

Read: A quantum supremacy breakthrough could transform our world

Furthermore, he says organizations will have a tough time developing the skills necessary to deliver and maintain impactful deployments. Given the severity of the threat, however, he expects providers of critical services, such as BFSI, government entities, and such, to be forced into action.

Vitaliy Trifonov, head of services group at cybersecurity company Group-IB, suggests organizations transition to quantum-secure cryptography gradually. He says they either deploy parallel quantum solutions, do a phased migration, or a complete overhaul.

Regardless of the approach, he insists, its time for organizations to take action. Waiting for quantum-resilient cryptographic standards and regulations may leave organizations vulnerable, says Trifonov. Embracing the quantum era now is essential to safeguard sensitive data and reap its benefits confidently.

For more stories on tech, click here.

Read more here:
Is it time for companies to quantum-proof their data? - Economy Middle East