Archive for the ‘Quantum Computing’ Category

Nuclear submarines, non-nuclear weapons and the search for strategic stability – The Strategist

The decision to deploy nuclear-powered ballistic missile submarines (SSBNs) in the years to come will be a product of the major paradigms and concepts used to manage nuclear dangers more broadly. Recently, an emerging literature has pointed to a change in the way that at least the major powers plan to mitigate nuclear threats to their interests. This shift in thinking can be summarised as involving a greater reliance on strategic non-nuclear weaponsweapons and enabling systems that can be used to compromise an adversarys nuclear forces using both kinetic and non-kinetic means that dont involve nuclear weaponsand a decreased commitment to mutual vulnerability as the basis of strategic stability between nuclear-armed adversaries.

Strategic non-nuclear weapons include ballistic missile defence, conventional precision-strike missiles, anti-satellite weapons and anti-submarine weapons. When combined with advances in enabling platforms and systems such as elements of cyber, artificial intelligence and quantum technology, they can, in principle, be used to compromise an adversarys nuclear capabilities, with serious implications for issues of deterrence and stability.

Traditional approaches to deterrence based on the threat of punishment now compete with policies based instead on deterrence by denial. Stability based on rational calculations under conditions of mutual vulnerability appears set to be even harder to maintain.

The potential for conventional counterforce strikes makes future scenarios involving use them or lose them logic more likely for states that face adversaries armed with more sophisticated capabilities.

The current challenge to traditional nuclear deterrence relationships has a dual but paradoxical effect on the incentives to deploy sea-based nuclear weapons. In general, as missile silos (and even, over time, mobile land-based missiles), air fields, satellites, and command, control and communications stations become more vulnerable to counterforce attacks, the incentives to diversify a states nuclear force structure increase. In particular, SSBNs still remain the most secure form of second-strike capability, meaning that the further spread of strategic non-nuclear weapons is likely to result in ever more nuclear weapons being deployed at sea.

On the other hand, one of the key technologies that falls under the banner of strategic non-nuclear weapons is anti-submarine weapons themselves, and much analysis now is focusing on whether advances in this area may in fact undermine the perceived invulnerability of SSBNs. Its important to note that growing concerns over the effects of new anti-submarine capabilities on strategic stability are, at least in part, based on projections about the future. Little serious analysis or commentary predicts that the oceans are going to become effectively transparent overnight. However, advances in sensing and signal processing in particular mean that its a serious possibility that the oceans will become significantly more transparent than they are today. And when it comes to nuclear force structure planning, serious possibilities are enough to keep decision-makers up at night.

As the development of strategic non-nuclear weapons and the associated shift in thinking about stable deterrence based on mutual vulnerability continues, policymakers and analysts will need to give serious attention to what might become the new determinants of stability in the global nuclear order.

The development of countermeasures will play an important role in mitigating the destabilising effects of disruptive technological breakthroughs in anti-submarine weaponry. The role of countermeasures is already evident in other domains. For example, as a reaction to US missile defence, both China and Russia today are placing increasing emphasis on hypersonic missiles because their combination of speed and manoeuvrability makes them extraordinarily difficult to defend against.

Countermeasures for anti-submarine weapons need not rely on kinetic effects. The development both of ever quieter SSBNs with smaller acoustic signatures and of new techniques of deception (for example, unmanned underwater vehicles designed to produce tonals that match those of SSBNs that are thought to have been identified by an adversary) can increase a states confidence that at least some of its SSBNs can remain undetected and uncompromised in a crisis.

Developments in anti-submarine weapons aimed at compromising SSBNs and developments in countermeasures aimed at mitigating those breakthroughs will take on a tit-for-tat dynamic in the years to come. This is not a new phenomenon, but as rapid increases in things such as sensing techniques and data processing allow for technological leaps in anti-submarine capabilities, countermeasures should be expected to take on a new and much greater importance.

Defensive measures for SSBNs aimed at increasing their reliability in the face of technological breakthroughs in anti-submarine weaponry are unlikely to solely rely on new technologies themselves. For example, James Holmes has suggested that both bastion strategies for SSBNs (vessels constricted to a much smaller, actively defended area for patrols) and SSBNs being accompanied by convoys of skirmisher-type defensive units (adopting a similar principle to aircraft carrier battle groups) may be necessary to regain confidence in the survivability of SSBNs.

Stability needs to be seen as the most important goal and that will require a degree of what has been termed security dilemma sensibility among the nuclear-armed powers. Leaders that develop security dilemma sensibility display an openness to the idea that, as Nicholas Wheeler has put it, an adversary is acting out of fear and insecurity and not aggressive intent, as well as a recognition that ones own actions have contributed to that fear.

For example, future Chinese breakthroughs on quantum computing and their application to SSBN communication technology could be a positive development in the USChina strategic relationship. The more confidence Beijing has in the security of its second-strike capability, the less likely it is that a crisis between the US and China will inadvertently escalate.

Beyond unilateral measures, it may be possible, over the longer term, to negotiate, and design, limited multilateral efforts aimed at restoring stability between adversaries, including in relation to sea-based nuclear deployments. History suggests that confidence-building measures can play as important a role as formal arms control measures in reducing nuclear dangers, meaning that finding avenues for dialogue, even at a low level, should now be a top priority.

In the short term, the increasing salience of strategic non-nuclear weapons and the abandonment of deterrence strategies based on mutual vulnerability, is likely to continue to encourage states to deploy more SSBNs. Simultaneously, these forces will intensify the pressures to better protect SSBN fleets that are already deployed from technological breakthroughs in the anti-submarine weapons domain. Restraint in the deployment of anti-submarine capabilities may need to become a substitute for the more traditional tools used to instil stability in nuclear-armed relationshipsrestraint in defensive technology (such as missile defence) and negotiated limits on arms.

This piece was produced as part of the Indo-Pacific Strategy: Undersea Deterrence Project, undertaken by the ANU National Security College. This article is a shortened version of chapter 20, Strategic non-nuclear weapons, SSBNs, and the new search for strategic stability, as published in the 2020 edited volume The future of the undersea deterrent: a global survey. Support for this project was provided by a grant from Carnegie Corporation of New York.

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Nuclear submarines, non-nuclear weapons and the search for strategic stability - The Strategist

Menten AIs combination of buzzword bingo brings AI and quantum computing to drug discovery – TechCrunch

Menten AI has an impressive founding team and a pitch that combines some of the hottest trends in tech to pursue one of the biggest problems in healthcare new drug discovery. The company is also $4 million richer with a seed investment from firms including Uncork Capital and Khosla Ventures to build out its business.

Menten AIs pitch to investors was the combination of quantum computing and machine learning to discover new drugs that sit between small molecules and large biologics, according to the companys co-founder Hans Melo.

A graduate of the Y Combinator accelerator, which also participated in the round alongside Social Impact Capital*, Menten AI looks to design proteins from scratch. Its a heavier lift than some might expect, because, as Melo said in an interview, it takes a lot of work to make an actual drug.

Menten AI is working with peptides, which are strings of amino acid chains similar to proteins that have the potential to slow aging, reduce inflammation and get rid of pathogens in the body.

As a drug modality [peptides] are quite new, says Melo. Until recently it was really hard to design them computationally and people tried to focus on genetically modifying them.

Peptides have the benefit of getting through membranes and into cells where they can combine with targets that are too large for small molecules, according to Melo.

Most drug targets are not addressable with either small molecules or biologics, according to Melo, which means theres a huge untapped potential market for peptide therapies.

Menten AI is already working on a COVID-19 therapeutic, although the companys young chief executive declined to disclose too many details about it. Another area of interest is in neurological disorders, where the founding team members have some expertise.

Image of peptide molecules. Image Courtesy: D-Wave

While Menten AIs targets are interesting, the approach that the company is taking, using quantum computing to potentially drive down the cost and accelerate the time to market, is equally compelling for investors.

Its also unproven. Right now, there isnt a quantum advantage to using the novel computing technology versus traditional computing. Something that Melo freely admits.

Were not claiming a quantum advantage, but were not claiming a quantum disadvantage, is the way the young entrepreneur puts it. We have come up with a different way of solving the problem that may scale better. We havent proven an advantage.

Still, the company is an early indicator of the kinds of services quantum computing could offer, and its with that in mind that Menten AI partnered with some of the leading independent quantum computing companies, D-Wave and Rigetti Computing, to work on applications of their technology.

The emphasis on quantum computing also differentiates it from larger publicly traded competitors like Schrdinger and Codexis.

So does the pedigree of its founding team, according to Uncork Capital investor, Jeff Clavier. Its really the unique team that they formed, Clavier said of his decision to invest in the early-stage company. Theres Hans the CEO who is more on the quantum side; theres Tamas [Gorbe] on the bio side and theres Vikram [Mulligan] who developed the research. Its kind of a unique fantastic team that came together to work on the opportunity.

Clavier has also acknowledged the possibility that it might not work.

Can they really produce anything interesting at the end? he asked. Its still an early-stage company and we may fall flat on our face or they may come up with really new ways to make new peptides.

Its probably not a bad idea to take a bet on Melo, who worked with Mulligan, a researcher from the Flatiron Institute focused on computational biology, to produce some of the early research into the creation of new peptides using D-Waves quantum computing.

Novel peptide structures created using D-Waves quantum computers. Image Courtesy: D-Wave

While Melo and Mulligan were the initial researchers working on the technology that would become Menten AI, Gorbe was added to the founding team to get the company some exposure into the world of chemistry and enzymatic applications for its new virtual protein manufacturing technology.

The gamble paid off in the form of pilot projects (also undisclosed) that focus on the development of enzymes for agricultural applications and pharmaceuticals.

At the end of the day what theyre doing is theyre using advanced computing to figure out what is the optimal placement of those clinical compounds in a way that is less based on those sensitive tests and more bound on those theories, said Clavier.

*This post was updated to add that Social Impact Capital invested in the round. Khosla, Social Impact, and Uncork each invested $1 million into Menten AI.

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Menten AIs combination of buzzword bingo brings AI and quantum computing to drug discovery - TechCrunch

Six things you need to learn about quantum computing in finance – eFinancialCareers

This willcome as bad news if you're only just getting to grips with Python, but you should probably be thinking of adding quantum computing to your repertoire if you want to maintain your long term employability in finance. Both Goldman Sachs and JPMorgan have been investigating the application of quantum computers to their businesses, and many say it's less a question of if than whenquantum computing is more widely applied.

Both Google and IBM are competing for quantum leadership. Google declared that it had achieved 'quantum supremacy' last October,a claimpromptly disputed by IBM, which said that Google's assertion was misleading. IBM itself now has 18 quantum computersthatcan be accessed via the cloud and that are already used by JPMorgan to set derivatives prices. In a new report*, IBM researchers includingDaniel Egger, Claudio Gambella,Jakub Marecek,Scott McFaddin, and Martin Mevissenargue that this is just the start.

Over time, the researchers say banks will use quantum computers for everything from creating value at risk and liquidity coverage ratios to running simulations to enable more accurate calculations of net stable funding ratios and pricing financial instruments. In preparation for this future they suggest you familiarize yourself with the following six quantum algorithms.

1. The Variational Quantum Eigensolver

The Variational Quantum Eigensolver (VQE) is used for optimization applications. It harnesses energy states to calculate the function of the variables it needs to optimize and is good whenstandard computers struggle due to the intensity of the computing required. In financial services, IBM says the VQE can be used in portfolio optimization. The only problem is that the number of qubits you need increases signficantly withproblem size.

2. The Quantum Approximate Optimization

TheQuantum Approximate Optimization is used to optimize combined problems and tond solutions to problems with complex constraints. IBM says it can be combined with VQE forportfolio optimization.

3. TheQuantum Amplitude Estimator

TheQuantum Amplitude Estimator(QAE) is used in simulations, optimizations and machine learning. It allows users to create simulation scenarios by estimating an unknown property in the style of the Monte Carlo method. Instead of simple samplying random distributions, the QAE can handle them directly and this dramatically speeds up simulation time. In finance, it can be used for option pricing, portfolio risk calculations,issuance auctions, anti-money laundering operations and identifying fraud.

4.Quantum Support Vector Machines

Quantum support vector machines (QSVM) applysupervised machine learning to high dimensional problem sets. Used for financial forecasting, they map data into a 'quantum-enhanced feature space' that enables the separation of data points and improvedforecastaccuracy.

5. Harrow, Hassidim, and Lloyd

Harrow, Hassidim, and Lloyd (HHL) is used for optimization and machine learning and enables better measurement of large linear systems by exponentially speeding up calculations. It can be used for credit scoring.

6.Quantum Semidenite Programming

Quantum Semidefinite Programming (QDSP) is used to optimize a linear objective over a set of positive semi-denite matrices. It can be used for portfolio diversification and "exponentially" speeds-up calculations when there are particular constraints.

As the financial services industry is subject to the combined demands of, "sophisticated risk analysis, dynamic client management, constant updates to market volatility, and faster transaction speeds," IBM's researchers predict quantum algorithms are primed for take-off. Now might be a good time to start familiarizing yourself with how they work.

*Quantum computing for Finance: state of the art and future prospects

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Six things you need to learn about quantum computing in finance - eFinancialCareers

Quantum Computing Market: In-Depth Market Research and Trends Analysis till 2030 – Cole of Duty

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