Mediaboss Marketing

By Yuval Boger

Last year, a group of scientists published a paper in Nature that discussed qubit shuttling, or as it was called in the paper coherent transport of entangled atom arrays. The work of this Harvard-led group that included scientists from QuEra Computing, MIT, and the University of Innsbruck, could prove pivotal for the development of large-scale quantum computing using neutral atom arrays.

The video below demonstrates this shuttling. This is a real video (with added red ellipses for emphasis), not an animation.

It shows groups of physical qubits that are moved at the same time. The red circles show the potential for entanglement with nearby qubits at every step.

Coherent qubit shuttling the ability to move qubits around while preserving their quantum state could profoundly impact how next-generation quantum computers might be built. That potential impact can be appreciated in three areas: error correction, multi-zone architecture and scale-up.

A primary challenge in building large-scale quantum computers lies in error management. Unlike classical computing, where information from a single binary digit can be duplicated for error correction, quantum mechanics doesnt allow for such copying (the no-cloning theorem). Therefore, quantum error correction involves spreading information across multiple qubits through entanglement to create redundancy.

The ability to move qubits while preserving their state allows entangling nearby qubits but then spreading these entangled qubits over a larger area. One such error correction code that involves spreading qubits over an area is the toric code, where logical qubits are encoded in such a way that they span a two-dimensional lattice. Because the logical qubits are spread out over a large area, localized errors affect only a small portion of the logical qubit, which makes it possible to correct the error without damaging the overall quantum information. See this article in Nature from Harvard, University of Innsbruck, MIT and AWS, for illustrations of this toric code.

Once qubits can be moved around while preserving their state, one could envision the development of a quantum computing architecture that includes multiple zones. For instance, one could imagine an architecture with three zones:

Enabled by qubit shuttling, qubits can be moved in and out of these zones as required.

Beyond the fact that error-corrected qubits allow meaningful execution of longer circuits, there is the question of control signals. Control signals are required to alter the state of individual qubits as well as to perform multi-qubit operations. However, as one thinks about million-qubit machines, do we expect to have millions of control signals? Imagine opening up your 4K television and discovering that every pixel has a wire going to it. That would be ridiculous. Similarly, qubit shuttling allows increasing the number of qubits without a matching increase in control signals

Additionally, qubit shuttling essentially enables any-to-any qubit connectivity. This is in contrast to fixed-layout configurations where qubits are connected to just their nearest neighbors. Any-to-any connectivity allows compressing the circuit because information can propagate with fewer information.

In conclusion, the ability to shuttle qubits around while maintaining their quantum state can have far-reaching implications for the future of quantum computing and bring us one step closer to realizing its full potential. This advancement opens up innovative approaches to error correction, multi-zone architecture, and scaling. It allows for a much more flexible, robust, and efficient quantum computing architecture that can handle complex computations and larger circuits while managing errors more effectively.

Yuval Boger is the Chief Marketing Officer for QuEra, the leader in quantum computers based on neutral atoms. QuEras 256-qubit computer is available for public access on Amazon Braket.

May 17, 2023

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Qubit Shuttling and its Implication for Neutral Atom Computers - Quantum Computing Report

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Insilico Medicine, a clinical stage generative artificial intelligence (AI)-driven drug discovery company, today announced that it combined two rapidly developing technologies, quantum computing and generative AI, to explore lead candidate discovery in drug development and successfully demonstrated the potential advantages of quantum generative adversarial networks in generative chemistry.

The study, published in the Journal of Chemical Information and Modeling, was led by Insilico's Taiwan and UAE centers which focus on pioneering and constructing breakthrough methods and engines with rapidly developing technologiesincluding generative AI and quantum computingto accelerate drug discovery and development.

The research was supported by University of Toronto Acceleration Consortium director Aln Aspuru-Guzik, Ph.D., and scientists from the Hon Hai (Foxconn) Research Institute.

"This international collaboration was a very fun project," said Aln Aspuru-Guzik, director of the Acceleration Consortium and professor of computer science and chemistry at the University of Toronto. "It sets the stage for further developments in AI as it meets drug discovery. This is a global collaboration where Foxconn, Insilico, Zapata Computing, and University of Toronto are working together."

Generative Adversarial Networks (GANs) are one of the most successful generative models in drug discovery and design and have shown remarkable results for generating data that mimics a data distribution in different tasks. The classic GAN model consists of a generator and a discriminator. The generator takes random noises as input and tries to imitate the data distribution, and the discriminator tries to distinguish between the fake and real samples. A GAN is trained until the discriminator cannot distinguish the generated data from the real data.

In this paper, researchers explored the quantum advantage in small molecule drug discovery by substituting each part of MolGAN, an implicit GAN for small molecular graphs, with a variational quantum circuit (VQC), step by step, including as the noise generator, generator with the patch method, and quantum discriminator, comparing its performance with the classical counterpart.

The study not only demonstrated that the trained quantum GANs can generate training-set-like molecules by using the VQC as the noise generator, but that the quantum generator outperforms the classical GAN in the drug properties of generated compounds and the goal-directed benchmark.

In addition, the study showed that the quantum discriminator of GAN with only tens of learnable parameters can generate valid molecules and outperforms the classical counterpart with tens of thousands parameters in terms of generated molecule properties and KL-divergence score.

"Quantum computing is recognized as the next technology breakthrough which will make a great impact, and the pharmaceutical industry is believed to be among the first wave of industries benefiting from the advancement," said Jimmy Yen-Chu Lin, Ph.D., GM of Insilico Medicine Taiwan and corresponding author of the paper. "This paper demonstrates Insilico's first footprint in quantum computing with AI in molecular generation, underscoring our vision in the field."

Building on these findings, Insilico scientists plan to integrate the hybrid quantum GAN model into Chemistry42, the Company's proprietary small molecule generation engine, to further accelerate and improve its AI-driven drug discovery and development process.

Insilico was one of the first to use GANs in de novo molecular design, and published the first paper in this field in 2016. The Company has delivered 11 preclinical candidates by GAN-based generative AI models and its lead program has been validated in Phase I clinical trials.

"I am proud of the positive results our quantum computing team has achieved through their efforts and innovation," said Alex Zhavoronkov, Ph.D., founder and CEO of Insilico Medicine. "I believe this is the first small step in our journey. We are currently working on a breakthrough experiment with a real quantum computer for chemistry and look forward to sharing Insilico's best practices with industry and academia."

More information: Po-Yu Kao et al, Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry, Journal of Chemical Information and Modeling (2023). DOI: 10.1021/acs.jcim.3c00562

The data acquisition code and source codes associated with this study are publicly available at: github.com/pykao/QuantumMolGAN-PyTorch

Journal information: Journal of Chemical Information and Modeling

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Study combines quantum computing and generative AI for drug discovery - Phys.org

At its recent Think 2023 conference, IBM focused on two areas AI and quantum computing that it believes will be essential to its enterprise customers. Lets look at the details.

Why should organizations consider AI or quantum computing? The technologies are not mutually exclusive but deserve separate answers.

In the former case, AI offers companies new tools for analyzing and leveraging existing data resources with the aim of improving processes from IT management to supply chain operations. Consider that a company could use AI models to generate code for developers or to automate complex data center tasks. These and solutions based on other AI modalities are currently available to businesses.

While it is still in relatively early days, quantum computing is maturing quickly and, in fact, is an issue that many government agencies and businesses will need to understand and deploy relatively soon.

For example, last year the U.S. government released new requirements and guidelines for federal agencies to start transitioning to solutions to protect valuable and critically important data against quantum computing-based attacks. In part, that is due to the increasing prevalence of harvest now, decrypt later attacks aimed at enduringly valuable government information, like classified and strategic documents.

To achieve that, the National Institute of Standards and Technology (NIST) selected four quantum-resistant algorithms for standardization three of which were developed by IBM, alongside academic and industry collaborators.

In addition, the National Security Agency (NSA) announced that national security systems will be required to fully transition to quantum-safe algorithms by 2035. They further define that software and firmware signing should begin transitioning immediately.

Finally, the White House ordered federal agencies to submit inventories of systems that could be vulnerable to cryptographically relevant quantum computers.

Also see:Generative AI Companies: Top 12 Leaders

AI-enabled business has arrived, and the need for quantum computing is approaching quickly. IBMs central message at Think 2023 was that it is delivering artificial intelligence business solutions today and is rapidly developing robust quantum safe technology and services for near term deployment.

According to IBM chairman and CEO Arvind Krishna, the new WatsonX AI and data platform was, Built for the needs of enterprises, so that clients can be more than just users, they can become AI advantaged. Foundation models make deploying AI significantly more scalable, affordable and efficient. With IBM WatsonX, clients can quickly train and deploy custom AI capabilities across their entire business, all while retaining full control of their data.

What are foundation models? According to the company, they are built with large sets of IBM-curated enterprise data backed by a robust filtering and cleansing process and auditable data lineages. IBM is training the models on language, as well as other modalities, including code, time-series data, tabular data, geospatial data and IT events data.

Using WatsonX foundation models alone or in concert with their own proprietary data sets will enable IBM customers to speed and scale AI training processes while reducing potential problems caused by inaccurate or noisy data.

As a result, AI-enabled business processes should be more stable and successful out of the block. Plus, IBM believes that the adaptable AI models combined with enterprises data and domain expertise will enable customers to derive competitive differentiation and unique business value.

On a related topic:The AI Market: An Overview

As detailed at Think 2023, the WatsonX platform consists of three product sets:

Watsonx.ai and watsonx.data are expected to be generally available in July 2023. Watsonx.governance is expected to be generally available later this year. In addition, IBM plans to infuse WatsonX foundation models throughout its advanced software portfolio.

Finally, the company also announced other upcoming offerings designed to help drive AI adoption. Those include:

Also see:Top Generative AI Apps and Tools

IBMs new Quantum Safe technology is an end-to-end solution designed to help clients remain secure now and throughout their quantum-safe journey towards the post-quantum era. Sounds good, but what exactly does this mean?

While some quantum computing and quantum-like solutions are available, the market is still in very early days. However, potential dangers lie ahead as quantum technologies mature and become increasingly available to valid business and government agencies, as well as to bad actors, including rogue states and organized cybercriminals.

The question, then, is how organizations can best protect themselves against quantum-based cyberattacks during this transition. These attacks include harvest now, decrypt later schemes designed to steal highly encrypted data in the hopes that quantum-based tools can eventually be used to decode it.

In essence, IBM Quantum Safe is designed to thwart such efforts with various tools, including:

IBM also announced its Quantum Safe Roadmap, which is designed to help clients understand new threats and solutions and support them through this security transition.

Also see:What is Artificial Intelligence?

So, what are we to make of IBMs new WatsonX solutions and Quantum Safe offerings? In the former case, WatsonX is hardly IBMs first foray into AI. In fact, the company has been at the forefront of AI R&D since the 1950s with Arthur Samuels checkers-playing computer. IBM efforts continued through the Deep Blue system that beat chess grand master Gerry Kasparov in 1997 and the Watson system that triumphed over two Jeopardy grand champions in 2011.

While IBMs commercial Watson efforts havent all succeeded as the company hoped, the platform remains one of the worlds most advanced generative AI solutions. Moreover, IBMs focus on using Watson for simplifying operations and amplifying business benefits continues to find willing customers. In the weeks leading up to Think 2023, SAP announced that it will embed IBM Watson AI in SAP Start, the digital assistant that runs across all SAP instances.

With those points in mind, IBMs plans and goals for WatsonX appear eminently sensible and achievable, and the companys approach is spot-on. Leveraging its own substantial resources curated foundation models, machine learning expertise, hybrid cloud assets and data management and governance tools should make the WatsonX platform powerfully attractive to IBM clients.

Similar to its experience in AI, IBM was an early adopter and promoter of quantum computing and is actively working to bring quantum solutions to market. It also clearly recognizes the potential dangers that government agencies and enterprises face during the quantum computing transition. Along with its work on three of the four quantum-resistant algorithms adopted for standardization by the NIST, IBM also embedded quantum safe features in the z16 mainframesand LinuxONE 4 systems it launched last year.

In other words, just as it has done with WatsonX, the company is using its quantum computing investments and expertise to deliver Quantum Safe solutions to keep government and enterprise clients secure now and against future threats.

For moreinformation, also see:What is Data Governance

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IBM Think 2023: AI and Quantum Computing | eWEEK - eWeek

Energy companies have had a lot to worry about this past year. The economic disruption caused by Russias ongoing war in Ukraine has amplified calls for an accelerated energy transition in Europe a shift that could offer the combined benefits of alleviating the continents dependence on Russia and cutting down on its use of highly-polluting fossil fuels.

In order to keep global warming under 1.5C the stated goal of the 2015 Paris Climate Accords carbon emissions will need to reach net-zero by 2050, according to the UN. Added to this challenge? Global power consumption is expected to triple by 2050 as living standards grow worldwide, according to a 2022 report by McKinsey.

In their efforts to achieve these complex and apparently conflicting objectives, energy companies such as E.ON SE, Eni, and EDF are turning to quantum computing. Theyre hoping to harness the technologys immense potential processing power to deliver more energy more efficiently, thus maintaining a stable and sustainable supply of electricity for the years to come.

One way to help get to net zero is by decentralising energy supply networks, a trend thats happening across Europe. Every day, companies are adding more small-scale generative units, such as wind turbines and household solar panels, into their power grids. Theyre also connecting a host of EV charging points, which feed batteries that can store power until its needed.

These decentralised grids tend to be more efficient than traditional networks since the sources are often closer to the users, meaning less energy is lost during transmission. Theyre also better at linking small-scale renewable sources, rather than depending exclusively on large-scale plants, and offer greater control over the entire network, helping companies cope with fluctuating demand.

The perks are numerous. Managing decentralised grids, however, is incredibly complex since companies have to make real-time decisions that factor in a huge range of variables. This leaves the energy industry facing very difficult mathematical problems, explains Heike Riel, a researcher at IBM. Such problems might, indeed, turn out to be too tough for classical supercomputers to handle meaning energy suppliers are being forced to rely on approximations that, given their margins of error, dont offer maximum efficiency.

But its not just supply that needs to be optimised on decentralised grids. The tools we use to produce and transmit our energy all need to be maintained over their lifecycle for regular wear-and-tear, as well as any damage caused by freak weather or felled trees. This, too, requires complicated scheduling optimisation both for the maintenance work itself and the teams of workers that perform it. Youre balancing having lots of extra spare parts, which is basically reducing your efficiency, versus running out, which is going to bring problems in terms of operating capacity, explains Murray Thom, vice-president at quantum computing company D-Wave Systems.

Theres also the question of setting up new generators. When planning fresh developments, companies need to consider everything from local weather conditions to demand, grid constraints, supply chain challenges, transportation costs, and employee availability. Renewables, like wind turbines, are particularly vulnerable to the vagaries of their environment, so such calculations will only grow more prescient and more complicated as companies shift to sustainable resources.

Indeed, researchers from Microsoft announced, all the way back in 2018, that they had developed a new quantum-inspired algorithm for so-called unit commitment identifying the best power-producing resources to activate based on forecasted demand, efficiencies, and capacity limitations. This tool already outpaced more powerful classical systems in a demonstration and will likely show a far bigger advantage when scaled-up quantum computers become commercially available.

These arent new dilemmas. Such problem-solving has previously been handled albeit more slowly and less precisely by large, expensive data centres that burn through lots of energy. But quantum could, researchers believe, be faster, more accurate, and more cost-effective.

It could also be a research accelerator, explains Riel. Scientists have, for example, long struggled to improve batteries capacity to store energy. This is tough to investigate experimentally, explains Riel, because there are so many complex chemical reactions involved. But if we can precisely predict those reactions using the processing powers of a quantum computer, we might be able to boost batteries capacities and save precious energy.

Multiple energy companies have begun investing in quantum research lured by the pressures of sustainable development and the potential to win big economic gains.

German energy giant E.ON is trying to use IBMs quantum capabilities to optimise the output of its decentralised power infrastructure. The partnership, announced in 2021, gives E.ON access to IBMs quantum computing systems, via the IBM Cloud, as well as IBMs expertise and Qiskit quantum software developer tools. For E.ON, the innovative use of quantum computing offers an opportunity to solve complex and cross-system optimisation tasks in the energy transition in an innovative way, said Victoria Ossadnik, who was E.ONs chief digital officer at the time.

French energy supplier EDF, meanwhile, partnered with startup Quandela to study the use of photonic quantum computing in simulations of deformations in hydroelectric dams. This research aims to boost the speed and accuracy of these simulations and thus enable better design and maintenance of these flexible energy sources.

In November 2022, Italian energy company Eni teamed up with Paris-based quantum computing startup Pasqual in its mission to use quantum technologies to solve some of the most advanced computing problems, currently not approachable even by supercomputers, a spokesperson told Tech Monitor. Theyre investigating both quantum optimisation and quantum machine learning aiming to use these tools for everything from simulating reservoirs to studying magnetic fusion.

This kind of development, as evidenced by these partnerships, thrives on collaboration. The value of industry partners and larger businesses driving the quantum industry forward together should not be underestimated, says Daniel Goldsmith, senior quantum technologist at UK innovation agency Digital Catapult. For startups in the quantum space to succeed and effectively launch their solutions, industry partners must be involved in a collaborative process that enables the development of new technologies to solve broader industrial challenges.

So whats stopping everyone from getting onboard? It might still be too soon for many companies to see and believe the huge rewards that quantum researchers are promising. Right now, we are in what analysts call the Noisy Intermediate Scale Quantum (NISQ) era of quantum computing, says Goldsmith. This is where devices are small, qubits are prone to error, and proof of concepts dont yet have the success to achieve wide-scale business adoption.

Some people also just dont know what quantums all about, explains Thom. There isnt enough awareness because the technology is emerging really, really quickly, he says. A lot of folks that Im talking to are kind of like, what is a quantum computer?. Energy companies in particular might be more conservative in their technological experimentation, says Thom, because of the mission-critical nature of their work.

Capacity might not be ready yet, says Riel, but its not too soon to start looking into problems that could be solved by quantum computers. Otherwise, she warns, companies risk being left in the dust by their tech-savvy competitors. Quantum, with all its radical promises, isnt going away any time soon. We are on a steep trajectory, says Riel. Its time to get your feet wet.

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Can quantum computing make the grid sustainable? - Tech Monitor

While advancements in the field of Artificial Intelligence have been grabbing headline after headline, the world of quantum computing has quietly (and not so quietly in some cases) been going about the business of making the stuff once thought to be science fiction that of science fact.

The sphere hasnt seen much in the way of private investments (much, not to say none at all), but governments are keen to see the potential through with China announcing plans to commit at least $15 billion to quantum computing, and perhaps a first, with $7.2 billion committed vs. $1.3 the European Union sees itself in a leadership position ahead of the US market.

And these advancements arent just about the pure science of the matter, but massive commercial potential as well with McKinsey recently pinpointing that quantum technology could account for nearly $1.3 trillion in value by 2035.

On May 24, 2023, at the Tech.eu Summit Ill be sitting down with three experts, Sabrina Maniscalco, co-founder and CEO at Algorithmiq, Dr. Jan Goetz, CEO and co-founder at IQM Quantum Computers, and Markus Pflitsch, co-founder and CEO at Terra Quantum to discuss what the future of quantum computing holds, particularly here in Europe, and how Europe can stay ahead of, if not beat other geopolitical players to the punch.

Granted, 20 minutes is nowhere close to enough time to cover a topic as historied, deep, and technical as quantum computing, Im hoping that these three panelists can provide enough information to determine what state our European qubits are in and remain stable long enough for us to measure them. Or not.

I've got my questions for these experts, what are yours? Hit me up on @sensorpunk and I'll do my best to work them into the show.

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Quantum computing onstage at the Tech.eu Summit: The Year of the ... - Tech.eu