Archive for the ‘Quantum Computing’ Category

A ‘simple’ hard fork could subvert a quantum attack on Ethereum: Vitalik Buterin – Cointelegraph

Ethereum is already well-positioned to mitigate the impact of a massive quantum computing attack on the network, according to Ethereum co-founder Vitalik Buterin.

In a March 9 post to Ethereum Research, Buterin discussed what would happen if a quantum emergency happened as early as tomorrow.

Suppose that it is announced tomorrow that quantum computers are available, and bad actors already have access to them and are able to use them to steal users funds, Buterin postulated.

The blockchain would have to hard fork and users would have to download new wallet software, but few users would lose their funds, he added.

Buterin explained that the process of such a hard fork would involve rolling back the Ethereum network to the point where it is clear that large-scale theft was occurring and disabling all traditional transactions from that point.

Ethereum developers would then add a new transaction type which forms part of the Ethereum Improvement Proposal (EIP) 7560 to allow transactions from smart contract wallets.

When a user makes a transaction from their Ethereum wallet, the signature of that transaction reveals the public key, and in a post-quantum world, this would see the users private key revealed as well.

The new transaction type that forms the core part of the quantum-resist EIP would leverage Winternitz signatures and zero-knowledge proof technologies known as STARKs to ensure that existing wallets are switched to new validation code, he added.

This validation code leverages ERC-4337 account abstraction the underlying technology of smart contract wallets to prevent private keys from being displayed while signing transactions in the future, rendering these accounts immune from a quantum attack.

Related:Ethereum leans into rollup-centric future as Dencun hard fork looms

According to Buterin, users who have never approved a transaction from an Ethereum wallet are already safe from any potential quantum-related exploit, as only the wallet address has ever been made publicly available.

He also added that the infrastructure needed to implement such as hard fork could in principle start to be built tomorrow.

The advent of quantum computing has been a long-feared inflection point for the crypto industry, as a computer capable of breaking blockchain encryption could see once-untouchable user funds stolen in large volumes and at rapid rates.

However, most computer scientists and developers believe that quantum computing is still a ways off, with Google and IBM engineers estimating that quantum computing wont be sufficiently developed until 2029 at the earliest.

Magazine: Google to fix diversity-borked Gemini AI, ChatGPT goes insane:AI Eye

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A 'simple' hard fork could subvert a quantum attack on Ethereum: Vitalik Buterin - Cointelegraph

Ethereum: Vitalik is preparing for the war against quantum computers! – Cointribune EN

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!

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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.

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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.

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

Protecting quantum computers from adversarial attacks – Innovation News Network

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.

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Protecting quantum computers from adversarial attacks - Innovation News Network

SemiQon tests and ships its silicon-based 4-qubit quantum chip – Electronic Products & Technology

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

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SemiQon tests and ships its silicon-based 4-qubit quantum chip - Electronic Products & Technology

Short-depth QAOA circuits and quantum annealing on higher-order ising models | npj Quantum Information – Nature.com

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.

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