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Following the launch in 2017 of a global challenge to set future post-quantum cryptography standards in digital signatures and public key encryption, which attracted 82 candidates from 25 countries, the NIST has selected Falcon algorithm co-developed for its extremely strong security and high bandwidth efficiency.

Falcon was co-developed by Thales together with academic and industrial partners from France (University of Rennes 1, PQShield SAS), Switzerland (IBM), Canada (NCC Group) and the US (Brown University, Qualcomm). It was selected by NIST alongside two other algorithms as standard for digital signatures, while a fourth algorithm was deemed standard for public key encryption/KEM. Thales was the only technology group serving the defence, aerospace and digital identity markets, to take part in the NIST competition.

Post-quantum cryptography enables conventional computers to withstand attacks by large-scale quantum computers, which many specialists believe could appear in the next few years. Quantum machines are expected to increase todays computer processing power to such a degree that they could break current cryptographic algorithms in a matter of seconds.

This quantum leap in computing power could usher in a crypto-apocalyse by posing very real and serious threats to the security of digital systems private citizens and organisations worldwide rely on day-to-day, such as critical information systems, on-line banking, payment cards, e-commerce, electronic signatures or on-line voting.A hacker with a quantum computer, for example, could easily gain access to confidential data, steal someone else's identity or falsify transactions and legal contracts. In the same way, a nations security could be threatened if its critical communications systems were the target of a quantum attack.

New algorithms such as Falcon, are quantum-resistant because they are based on mathematical problems that are among the most difficult to solve, even for a quantum computer.

Organisations who are willing to protect their data in a Zero Trust world must adopt a strong quantum crypto agility strategy. Thales Cyber Solutions consulting teams have developed a post-quantum cyber architecture offer to help their customers prepare for the threat of cyber-attacks by quantum-computers. Thales also provides quantum resistant network encryption and hardware security modules that are capable of protecting customer data against future quantum attacks, by already allowing customers to implement a number of Quantum Resistant algorithms.

Thales has been at the forefront of post-quantum cryptography research since 2013, and the selection of the Falcon algorithm by NIST is great recognition of the excellent co-development work and expertise of our crypto teams. We will pursue our on-going research in France and Europe to develop innovative, trusted solutions that will be quantic-resistant, without compromising performance, and are already helping our customers in their transition to a new generation of security solutions, to avert a future crypto-apocalypse.said Pierre-Yves Jolivet, Vice-President, Cyber Defence Solutions at Thales.

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Falcon algorithm co-developed with Thales selected by the NIST as a new standard in post-quantum cryptography - Thales

With the crypto market continuing to trade sideways with mounting bearish pressure, niche categories for crypto tokens remain highly popular as traders and investors prowl for underrated and undervalued projects for long-term investments. Some popular crypto token types include Metaverse tokens, Web3 coins, and dApp tokens on various ecosystems like Tron, Elrond, Ethereum, Polkadot, etc.

Cryptocurrencies have the power to revolutionize finance by cutting out intermediaries. By bringing their exceptional capability to the design process, quantum computers and supercomputers have the potential to revolutionize the way medicines and materials are created.

But, Heres the issue: If quantum computing develops more quickly than efforts to future-proof digital money, the blockchain accounting system that underpins cryptocurrencies may be susceptible to sophisticated hacks and fake transactions.

On the other hand, some new cryptocurrencies claim to be quantum secure and quantum-resistant, which means they can withstand known quantum computer assaults. We will look at some cryptocurrency tokens at the top of their game.

Note: This list is ordered by market capitalization, from lowest to highest.

Mochimo (MCM), a brand-new cryptocurrency developed by an international team and released on June 25th, 2018, is resistant to threats from quantum computers.

Mochimo uses WOTS+ Quantum Resistant Security approved by the EU-funded PQCrypto research organization and a one-time addressing feature to secure privacy when you want it.

According to the website, the Mochimo blockchain remains small while substantially increasing TX speed using ChainCrunch, a proprietary algorithm. Using a compressed portion of the historical blockchain available on every node in the decentralized network, anyone can set up a full working node in minutes.

Industry experts in computer networking, artificial intelligence, telecommunications, cryptography, and software engineering make up the critical contributors of Mochimo.

Some top cryptocurrency exchanges for trading Mochimo $MCM are currently CITEX, FINEXBOX, and VinDAX.

The goal of HyperCash (HC), originally known as Hcash, is to make value transfers possible between various blockchains. It supports DAO governance, quantum resistance, and zero-hash proofs.

Its a decentralized and open-source cross-platform cryptocurrency designed to facilitate the exchange of information between blockchains and non-blockchain networks.

Its also a highly secure network featuring quantum-resistant signature technology.

The HCASH network has two chains running laterally, each serving different functions within the ecosystem.

Hcashs governance is based on a hybrid PoW/PoS consensus methodology and blockchain/DAG network.

If you want to know where to buy HyperCash at the current rate, check out these exchanges OKX, MEXC, KuCoin, Huobi Global, Gate.io, and Hoo. HyperCash is up 3.87% in the last 24 hours.

Nexus (NXS) is a community-driven initiative with the shared goal of creating a society characterized by progressive and ethical principles, advanced technology, and universal access to connection on a free and open basis.

Since September 23rd, 2014, Nexus has been created through mining alone, without an ICO or premine. Nexus uses post-quantum signature schemes (FALCON) and automated key management functions through the Signature Chains technology.

This technology eliminates key management issues (wallet.dats) by allowing users to access their accounts with the familiarity of a username, password, and PIN.

Another technology being developed by Nexus includes;

All the tech mentioned above is connected through a multi-dimensional chaining structure. Nexus is bringing this possibility to life with an end-to-end decentralized platform designed to empower every human being with technology to reclaim their digital identity.

Some top cryptocurrency exchanges for trading $NXS are Binance, Pionex, Bittrex, and CoinDCX.

The Quantum Resistant Ledger (QRL) is a fully quantum-resistant blockchain network using PQ-CRYPTO recommended/IETF standardized cryptography.

The QRL utilizes a hash-based eXtended Merkle Tree Signature Scheme (XMSS) instead of ECDSA, which is reportedly vulnerable to quantum attacks and found in many other blockchain projects.

According to the project, a set of applications and a development environment that enable users to simply build blockchain applications on its provably quantum-resistant network enhance the security of its platform.

Combining on-chain lattice key storage with their robust ephemeral messaging layer to internode communication provides a first-of-its-kind post-quantum secure message layer for ultra-secure digital communications.

The platform has a full suite of end-user products designed with the end-user in mind: from integrations with hardware wallets to mobile applications.

If you want to know where to buy $QRL, check out the CoinTiger exchange.

Launched in 2016, IOTA (MIOTA) is a distributed ledger. However, it differs significantly from a blockchain in that it isnt one. Instead, it uses a system of nodes called Tangle, its patented technology, to confirm transactions.

There are no fees since there is no blockchain, no mining, i.e., no miners. When congestion worsens, costs soar on many conventional networks, but IOTA seeks to offer limitless capacity at a low price.

The platforms foundation claims it provides much faster speeds than traditional blockchains and has the perfect footprint for the ever-expanding Internet of Things ecosystem.

The objective of IOTA is to establish itself as the default platform for carrying out IoT device transactions.

In summary:

According to the team behind the project, their distributed ledger may provide everyone access to digital identities, lead to auto insurance policies based on actual usage, open the door to cutting-edge smart cities, facilitate frictionless international trade, and establish the legitimacy of goods.

Some top cryptocurrency exchanges for trading $MIOTA are Binance, OKX, Bybit, Bitget, and BingX.

Disclosure: This is not trading or investment advice. Always do your research before buying any Quantum Computing token or investing in any cryptocurrency.

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Originally posted here:
Top 5 Quantum Computing Crypto Tokens to Watch in 2022 - The VR Soldier

NEW YORK, July 10, 2022 (GLOBE NEWSWIRE) -- Bragar Eagel & Squire, P.C., a nationally recognized shareholder rights law firm, reminds investors that class actions have been commenced on behalf of stockholders of IonQ, Inc. (NYSE: IONQ), Energy Transfer LP (NYSE: ET), Digital Turbine, Inc. (NASDAQ: APPS), and Teladoc Health, Inc. (NYSE: TDOC). Stockholders have until the deadlines below to petition the court to serve as lead plaintiff. Additional information about each case can be found at the link provided.

IonQ, Inc. (NYSE: IONQ)

Class Period: March 20, 2021 May 2, 2022

Lead Plaintiff Deadline: August 1, 2022

On May 3, 2022, Scorpion Capital released a research report alleging, among other things, that IonQ is a scam built on phony statements about nearly all key aspects of the technology and business. It further claimed that the Company reported [f]ictitious revenue via sham transactions and related-party round-tripping.

On this news, the Companys stock fell $0.71, or 9%, to close at $7.15 per share on May 3, 2022, on unusually heavy trading volume.

The complaint filed in this class action alleges that throughout the Class Period, Defendants made materially false and/or misleading statements, as well as failed to disclose material adverse facts about the Companys business, operations, and prospects. Specifically, Defendants failed to disclose to investors: (1) that IonQ had not yet developed a 32-qubit quantum computer; (2) that the Companys 11-qubit quantum computer suffered from significant error rates, rendering it useless; (3) that IonQs quantum computer is not sufficiently reliable, so it is not accessible despite being available through major cloud providers; (4) that a significant portion of IonQs revenue was derived from improper round-tripping transactions with related parties; and (5) that, as a result of the foregoing, Defendants positive statements about the Companys business, operations, and prospects were materially misleading and/or lacked a reasonable basis at all relevant times.

For more information on the IonQ class action go to: https://bespc.com/cases/IONQ

Energy Transfer LP (NYSE: ET)

Class Period: April 13, 2017 December 20, 2021

Lead Plaintiff Deadline: August 2, 2022

Energy Transfer is a Delaware company headquartered in Dallas, Texas. Energy Transfer is a company engaged in natural gas and propane pipeline transport. It was founded in 1996 and became a publicly traded partnership in 2006. The Partnership through its subsidiaries provides transportation, storage, and terminalling services for products like natural gas, crude oil, NGL, and refined products. The Partnership also constructs natural gas pipelines through its various subsidiaries.

On April 13, 2017, the horizontal directional drilling activities ("HDD") for the Rover Pipeline Project, one of the Partnership's natural gas pipeline construction projects, caused a large inadvertent release of drilling mud near the Tuscarawas River in Ohio. On August 8, 2019, Energy Transfer filed its quarterly report on Form 10-Q with the SEC, reporting the Partnership's financial and operating results for the second quarter ended June 30, 2019. This quarterly report disclosed that two years earlier, in mid-2017 the Federal Energy Regulatory Commission ("FERC")'s Enforcement Staff began a formal investigation "regarding allegations that diesel fuel may have been included in the drilling mud at the Tuscarawas River HDD." On this news, the price of Energy Transfer stock declined $0.65, or 4.6% over two trading days, to close at $13.38 on August 12, 2019.

Then, on December 16, 2021, FERC publicly issued to Energy Transfer the Order To Show Cause and Notice of Proposed Penalty, which directed the Partnership to show cause why it should not be assessed a civil penalty in the amount of $40,000,000. The order presented the allegation by the Enforcement Staff that the HDD crews intentionally included diesel fuel and other toxic substances and unapproved additives in the drilling mud during its HDDs under the Tuscarawas River. On this news, the price of Energy Transfer shares declined $0.24, or 2.8% over the course of two trading days, to close at $8.25, on December 20, 2021.

The Complaint alleges Energy Transfer concealed and misrepresented that: (a) Energy Transfer had inadequate internal controls and procedures to prevent contractors from engaging in illegal conduct with regards to drilling activities, and/or failed to properly mitigate known issues related to such controls and procedures; (b) Energy Transfer through its subsidiary hired third-party contractors to conduct HDDs for the Rover Pipeline Project, whose conduct of adding illegal additives in the drilling mud caused severe pollution near the Tuscarawas River when the April 13 Release took place; and (c) Energy Transfer continually downplayed its potential civil liabilities when FERC was actively investigating the Partnership's wrongdoing related to the April 13 Release and consistently provided it with updated information about FERC's findings on this matter.

For more information on the Energy Transfer class action go to: https://bespc.com/cases/ET

Digital Turbine, Inc. (NASDAQ: APPS)

Class Period: August 9, 2021 May 17, 2022

Lead Plaintiff Deadline: August 5, 2022

Digital Turbine is a software company that delivers products to assist third parties in monetizing through the utilization of mobile advertising. The Company completed the acquisitions of AdColony Holdings AS (AdColony) and Fyber N.V. (Fyber) on April 29 and May 25, 2021, respectively.

On May 17, 2022, Digital Turbine issued a press release revealing that it will restate its financial statements for the interim periods ended June 30, 2021, September 30, 2021, and December 31, 2021, following a review of the presentation of revenue net of license fees and revenue share for the Companys recently acquired businesses."

On this news, the Companys shares fell $1.93, or 7.1%, to close at $25.28 per share on May 18, 2022, on unusually heavy trading volume.

The complaint filed in this class action alleges that throughout the Class Period, Defendants made materially false and/or misleading statements, as well as failed to disclose material adverse facts about the Companys business, operations, and prospects. Specifically, Defendants failed to disclose to investors: (1) that the Companys recent acquisitions, AdColony and Fyber, act as agents in certain of their respective product lines; (2) that, as a result, revenues for those product lines must be reported net of license fees and revenue share, rather than on a gross basis; (3) that the Companys internal control over financial reporting as to revenue recognition was deficient; and (4) that, as a result of the foregoing, the Companys net revenues was overstated throughout fiscal 2022; and (5) that, as a result of the foregoing, Defendants positive statements about the Companys business, operations, and prospects were materially misleading and/or lacked a reasonable basis.

For more information on the Digital Turbine class action go to: https://bespc.com/cases/APPS

Teladoc Health, Inc. (NYSE: TDOC)

Class Period: October 28, 2021 April 27, 2022

Lead Plaintiff Deadline: August 5, 2022

Teladoc provides virtual healthcare services in the U.S. and internationally through Business-to-Business (B2B) and Direct-to-Consumer (D2C) distribution channels. The Company offers its customers various virtual products and services addressing, among other medical issues, mental health through its BetterHelp D2C product, and chronic conditions.

Teladoc touts itself as the first and only company to provide a comprehensive and integrated whole person virtual healthcare solution that both provides and enables care for a full spectrum of clinical conditions[.] Despite recent market concerns over new entrants to the telehealth field, such Amazon.com, Inc. (Amazon) and Walmart Inc. (Walmart), the Company has continued to assure investors of the Companys dominant market position in the industry.

In fact, as recently as February 2022, Teladoc forecasted full year (FY) 2022 revenue of $2.55 - $2.65 billion, as well as adjusted earnings before interest, taxes, depreciation, and amortization (EBITDA) of $330 - $355 million, on anticipated continued growth through its competitive advantages.

Throughout the Class Period, Defendants made materially false and misleading statements regarding the Companys business, operations, and prospects. Specifically, Defendants made false and/or misleading statements and/or failed to disclose that: (i) increased competition, among other factors, was negatively impacting Teladocs BetterHelp and chronic care businesses; (ii) accordingly, the growth of those businesses was less sustainable than Defendants had led investors to believe; (iii) as a result, Teladocs revenue and adjusted EBITDA projections for FY 2022 were unrealistic; (iv) as a result of all the foregoing, Teladoc would be forced to recognize asignificant non-cash goodwill impairment charge; and (v) as a result, the Companys public statements were materially false and misleading at all relevant times.

On April 27, 2022, Teladoc announced its first quarter (Q1) 2022 financial results, including revenue of $565.4 million, which missed consensus estimates by $3.23 million, and [n]et loss per share of $41.58, primarily driven by [a] non-cash goodwill impairment charge of $6.6 billion or $41.11 per share[.] Additionally, the Company revised its FY 2022 revenue guidance to $2.4 - $2.5 billion and adjusted EBITDA guidance to $240 - $265 million to reflect dynamics we are currently experiencing in the [D2C] mental health and chronic condition markets. On a conference call with investors and analysts that day to discuss Teladocs Q1 2022 results, Defendants largely attributed the Companys poor performance, revised FY 2022 guidance, and $6.6 billion non-cash goodwill impairment charge to increased competition in its BetterHelp and chronic care businesses.

On this news, Teladocs stock price fell $22.48 per share, or 40.15%, to close at $33.51 per share on April 28, 2022.

For more information on the Teladoc class action go to: https://bespc.com/cases/TDOC

About Bragar Eagel & Squire, P.C.:

Bragar Eagel & Squire, P.C. is a nationally recognized law firm with offices in New York, California, and South Carolina. The firm represents individual and institutional investors in commercial, securities, derivative, and other complex litigation in state and federal courts across the country. For more information about the firm, please visit http://www.bespc.com. Attorney advertising. Prior results do not guarantee similar outcomes.

Contact Information:

Bragar Eagel & Squire, P.C.

Brandon Walker, Esq.

Melissa Fortunato, Esq.

(212) 355-4648

investigations@bespc.com

http://www.bespc.com

Excerpt from:
Bragar Eagel & Squire, P.C. Reminds Investors That Class Action Lawsuits Have Been Filed ... - The Bakersfield Californian

In this co-published article, Haseltine Lake Kempners Laura Compton takes a practical look at how to formulate claims and draft applications for quantum machine learning inventions in view of the EPOs patent eligibility requirements

In quantum machine learning (QML), classical machine learning algorithms, or expensive subroutines of them, are typically adapted to run on a quantum computing device. QML utilises quantum resources to improve the execution time and/or the performance of classical machine learning algorithms.

Aspects of QML that may be patentable include the utilisation of a quantum computing device to execute more efficiently all or part of a classical machine learning algorithm (for example, using a quantum computer to calculate classical distances more efficiently for nearest neighbour, kernel and clustering methods), or to execute a model itself (for example, reformulating a stochastic model as a quantum system). Other related aspects include the reformulation of an optimisation problem such that it may be solved using a quantum computing device.

Another aspect of QML that may be patentable includes improvements to existing QML algorithms or models (for example, an improvement that reduces the depth of the quantum circuit required to execute the algorithm or model, and/or uses gates that are less complex, and/or avoids repetition of certain subroutines of the algorithm). Some improvements may be specific to the problem being solved itself (for example, modifying the operations applied to a quantum computing device such that a more limited space of potential solutions to an optimisation problem is then searched over by the device).

Inventions relating to these aspects will be considered patentable subject matter at the EPO when the quantum computing device is an integral part of the invention.

For such inventions, the independent claims are likely to make some reference to the quantum computing device and the manner in which the algorithm has been adapted to be implemented on it. The dependent claims, if not the independent claim itself, should:

In view of the EPOs technicality requirements, having a dependent claim that specifies how the output of the quantum computing device and/or the output of the machine learning model, is then used in some technical process, is recommended.

Where the invention relates to more general QML methods, or improvements to such methods (which could be applied to a wide range of problems across a wide range of fields), it is also recommended to provide a number of different use cases that demonstrate how the invention can be applied to different practical problems in the dependent claims, or the description,.

Quantum computing generally, as well as QML, is a rapidly evolving and complex field. As such, drafting applications which meet the EPOs sufficiency and clarity requirements can be a challenge. Therefore, when drafting patent specifications, it is best practice to include a full mathematical description of the quantum implementation of the algorithm or model, alongside how each operation being applied to the qubits relates to the algorithm or model being implemented (for example, describing how a series of operations applied to the qubits are representative of an objective function that is to be minimised).

For inventions which relate to improving existing QML algorithms or models, detailed description on how the changes to the quantum circuit enable the improvement to be realised should be included. As with any rapidly evolving field where there is a lack of universally accepted terminologies, for applications relating to quantum computing generally, the terms used in the claims of the application should be defined in the description.

Finally, experimental data can be particularly useful in terms of demonstrating an improvement in speed or accuracy over the prior art and can be useful for supporting inventive step arguments in later prosecution. It is also worth considering setting up the technical problem the invention solves in terms of why classical processes suffer from disadvantages that make them commercially or technically non-viable (for example, too slow for real time deployment).

To summarise, the points above can be used to assist in drafting QML inventions suitable for submission to the EPO and can be used to provide the applicant with the best possible chance of obtaining a commercially useful patent.

Laura Compton

Haseltine Lake Kempner LLP

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The keys to QML patent success - Lexology

The quantum internet is coming. Image Credit: Yurchanka Siarhei/Shutterstock.com

In recent years, quantum computing has taken quantum leaps in practicality, scalability, and raw computing power. However, replacing the worlds internet infrastructure with an entirely new system would likely take the best part of a century after all, many parts of the world dont even have access to the current internet.

One of the best ways scientists could make the quantum internet scalable would be to utilize the current infrastructure to transmit information.

Now, a research laboratory in Illinois has demonstrated long-distance transmission of quantum information using just existing fiber optic cables, pushing forward the dream of a scalable quantum internet.

To have two national labs that are 50 kilometers apart, working on quantum networks with this shared range of technical capability and expertise, is not a trivial thing, said Panagiotis Spentzouris, head of the Quantum Science Program at Fermilab, in a statement.

You need a diverse team to attack this very difficult and complex problem.

The experiment involved transporting quantum-encoded photons across a large distance while maintaining a high level of synchronization between them in human words, particles that have been modified to carry information are transported through a network while both ends of the line are working in harmony.

Synchronization is the real difficulty here. Computers must be synchronized across a network for a number of security and functional reasons, but this cannot rely on a standard clock. If you checked your watch and your friend checked theirs, even if you intentionally set them to almost identical times, they would still differ slightly by fractions of a second. For classical computing this simply wont do, so synchronization relies on Network Time Protocol (NTP), which synchronizes all participating computers within milliseconds of one another.

However, quantum computers are even pickier and require even smaller margins of error, so researchers must get creative to achieve synchronicity. The researchers sent a clock down the same optical fibres they were sending the quantum-encoded photons, just on different wavelengths to avoid interference, though this is no easy feat.

Choosing appropriate wavelengths for the quantum and classical synchronization signals is very important for minimizing interference that will affect the quantum information, said Rajkumar Kettimuthu, an Argonne computer scientist and project team member.

One analogy could be that the fiber is a road, and wavelengths are lanes. The photon is a cyclist, and the clock is a truck. If we are not careful, the truck can cross into the bike lane. So, we performed a large number of experiments to make sure the truck stayed in its lane.

They succeeded, with just a 5-picosecond difference between the clocks at each location. The researchers had managed to send quantum information across a long-distance network, using just current infrastructure, with incredible precision.

This is the first demonstration in real conditions to use real optical fiber to achieve this type of superior synchronization accuracy and the ability to coexist with quantum information, Spentzouris said.

This record performance is an essential step on the path to building practical multinode quantum networks.

Continue reading here:
Quantum Information Transmitted Over A Long Distance Using Current Infrastructure - IFLScience