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If you thought all generative AI could do was produce text and images, think again. It can do far more about way more serious and complex problems all it needs is a quantum boost. This new field of learning called quantum generative AI is a perfect marriage between the very buzzy area of generative AI and quantum computing and brings together the benefits of both.

In an exclusive chat with Prateek Jain, Lead Researcher and Architect of Quantum Computing at Fractal, we discuss the applications of quantum generative AI in critical sectors like healthcare, the associated challenges and how to get around them.

Fractal: Quantum generative AI (QGAI) is a subfield of quantum computing that focuses on developing algorithms and models that can generate new data with the help of quantum computers.

The main difference between QGAI and classical generative AI is the underlying computational platform. While classical generative AI relies on classical computers, QGAI is a novel approach which relies on quantum computers to perform operations on quantum bits (qubits) to generate new data. QGAI algorithms are designed to exploit the quantum mechanical properties of qubits to generate data with unique and potentially useful features.

Some examples of QGAI applications include:

Fractal: Quantum generative AI can be used to optimize drug and material properties by generating new compounds with specific characteristics that are required for various applications. Here are some ways in which QGAI can be used to optimize drug and material properties:

Drug Optimization: QGAI can be used to optimize the properties of existing drugs to enhance their efficacy and reduce side effects. By simulating the behaviour of molecules on quantum computers, QGAI algorithms can predict the binding affinity of a molecule to a target protein, which is a key factor in determining drug efficacy. QGAI can also be used to optimize drug properties, such as solubility and bioavailability, to improve their pharmacokinetics.

You can find some of our research at Fractal.ai here https://arxiv.org/abs/2212.07826

Material Optimization: QGAI can be used to optimize the properties of materials for specific applications, such as energy storage or catalysis. By simulating the behaviour of atoms and molecules on quantum computers, QGAI algorithms can predict the properties of materials, such as their conductivity or reactivity, malleability, radioactivity etc.

Chemical Reaction Optimization: QGAI can also be used to optimize chemical reactions by predicting the optimal conditions and reactants required for a specific reaction. This can lead to more efficient and sustainable chemical processes, such as the production of pharmaceuticals or materials.

Fractal: Quantum generative AI has the potential to revolutionize drug and material design by helping researchers to generate new and innovative compounds that could not be discovered using traditional methods. QGAI algorithms use quantum computers to simulate the behaviour of molecules and atoms and help researchers to predict their properties and generate new structures that meet specific criteria.

Here are some examples of how QGAI can be applied to drug and material design:

Drug Discovery: QGAI can be used to generate new compounds that are more effective at targeting specific diseases than existing drugs.

Material Design: QGAI can be used to design new materials with unique properties, such as superconductors or catalysts. By simulating the behaviour of atoms and molecules on quantum computers, QGAI algorithms can predict the properties of new materials, such as their conductivity or reactivity. This can help researchers design materials with specific properties that are required for various applications, such as energy storage or catalysis.

The potential benefits of using QGAI for drug and material design are significant. QGAI can accelerate the drug discovery and material design process by reducing the time and cost required to synthesize and test new compounds. QGAI can also enable researchers to discover compounds that would be difficult or impossible to discover using classical methods, leading to new treatments for diseases and innovative materials for a variety of applications. Additionally, QGAI can enable researchers to design compounds with specific properties that are required for various applications and pave the way for more effective drugs and materials.

There have been several research projects that have already applied quantum generative AI (QGAI) to drug and material design.

For instance, in 2019, researchers from IBM used QGAI to design a new molecule that could potentially be used to create more efficient potential drug molecules. The designed molecule was then synthesized and tested and was found to have high drug-like properties.

These examples demonstrate the potential of QGAI to accelerate the drug and material design process by generating new compounds that have desirable properties. While these projects are still in the early stages of development, they provide a glimpse into the exciting possibilities of QGAI in the field of drug and material design.

Fractal: Even though the emerging fields of quantum machine learning & newer quantum generative modelling are in their infancy, fast-moving research will push these areas to the fore. For starters, organizations can ensure that their quantum generative AI models are accurate and reliable by following some established approaches:

Data quality control: High-quality data is crucial for models to produce accurate results, especially in the healthcare sector. Organizations can ensure data quality by validating and cleaning up the data before using it to train QGAI models. They can also use statistical methods to identify and remove any outliers or irrelevant data points.

Model validation and testing: Organizations should validate their models by testing them on independent data sets. This can help identify any errors or biases in the models and provide insights into how they can be improved.

Explainability and transparency: Organizations should ensure that their models are transparent and explainable, which means that the models should be able to provide a clear explanation of their decision-making process. This can help identify any potential biases ahead and reduce them before they pop up in the predictions.

Regular updates and maintenance: Models should be regularly updated and maintained to ensure their accuracy and reliability.

In addition to these measures, organizations can also take the following steps to mitigate any potential errors or biases in their models:

Diversity and inclusivity: Organizations should ensure that their QGAI models are trained on diverse and inclusive data sets to avoid any biases that may arise from underrepresented groups.

Robustness testing: Organizations can test the robustness of their models by intentionally introducing errors or biases into the data and observing how the models respond.

Ethical considerations: Organizations should consider the ethical implications of their QGAI models and ensure that they do not cause harm or discrimination to individuals or groups.

Fractal: Using quantum generative AI brings along several challenges:

Scalability: As the size of the molecule or material being designed increases, so does the complexity of the calculations required. This can limit the scalability of QGAI for designing larger and more complex materials and drugs especially when the quantum processors are very small in the NISQ era.

Noise and decoherence: The inherent noise and decoherence in quantum computing is a major problem & can affect the accuracy of the models by a wide margin. This can lead to several errors in the predictions made.

Data quality and quantity: QGAI models require high-quality and diverse data to accurately predict properties and generate new compounds. However, acquiring such data can be challenging and costly, especially for rare or newly discovered compounds.

Interpreting results: QGAI models may generate novel compounds that exhibit desired properties, but it can be challenging to interpret why these compounds have these properties, making it difficult to optimize their performance further.

To address these challenges, researchers are exploring various approaches, such as:

Developing more efficient algorithms: Researchers are developing new algorithms and techniques to improve the efficiency of QGAI calculations and reduce the computing resources required for large-scale designs.

Quantum error correction: Researchers are developing quantum error correction techniques to mitigate the impact of noise and decoherence in quantum computing.

Integration with classical computing: Hybrid quantum-classical computing approaches are being developed to address the current limitations by utilizing the strengths of classical computing to supplement quantum computing.

Enhanced data collection and processing: Researchers are exploring ways to improve data quality and quantity by leveraging advances in data collection and processing technologies, such as machine learning and high-throughput screening techniques.

By addressing these challenges, researchers can further advance the use of QGAI for drug and material design and unlock its full potential for revolutionizing these industries.

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Reaping the Synergies Between Quantum Computing and ... - Analytics India Magazine

The clamour around quantum just got another reason to grow louder. India is getting its first quantum computing-based telecom network that will be operational in the national capital. Although still far away from public use, the link opens the doors for testing quantum-based networks in the real world. Developed by C-DoT (Centre for Development of Telematics), the link serves as a proof of concept, validating the systems ability to function in real-world environments.

While research yields accurate results when conducted under controlled lab conditions (regulating factors like temperature, humidity, and other environmental variables), the deployment of the same research in the actual world presents a distinct set of challenges. The proof-of-concept aims to identify and address these challenges to ensure that the system performs optimally in real-world settings.

According to an official from C-DoT, deploying a system in the field exposes it to air pollutants, causing it to accumulate dirt and demand regular maintenance to prevent overheating. These issues provide valuable insights for designing better systems in the future.

In quantum communication, information is encoded in the properties of individual photons. Here, when someone tries to tap into the system, the property of the photon changes in an irreversible manner. This ensures that you can detect any type of tapping or any attempt to intercept, so the system remains secure. This is in contrast to what happens in present day communication. Today, if two parties are communicating and someone taps into the system to intercept data, it goes undetected most of the time in the current digital systems.

The quantum communication link has 2-3 verticals that researchers are working on, which will ultimately lead to the quantum internet, says the official from C-DoT.

The quantum internet will look similar to the Internet of Things (IoT) we know today. Similar to the sensors we use today to communicate between devices using the internet, quantum computers and quantum sensors will also be able to simulate reality with much wider applications. The highly-sensitive sensors will be unlocked in meteorology and other areas because of their ability to detect microgravity or, for that matter, any other parameter in a very accurate way.

Of all the verticals that will lead to such a realisation, Quantum Key Distribution (QKD) is the most practical application right now, since others are yet to be implemented due to challenges in technology and components. QKD will create a secure channel by utilising the principles of quantum mechanics to create an unbreakable key between two points. This is to negate the risks posed by quantum computing attacks, which have the potential to be able to break through all the cryptographic methods we currently employ based on public encryption keys.

Read: This Could Bring Our Digital World Crashing Down

There are three different types of QKD. The first is based on high-fidelity optical fibre technology, which allows long-distance communication with a stable 24/7 operation, unaffected by environmental factors. The second type utilises free-space communication and can cover short to moderate distances, typically within the same city, but may encounter turbulence, like say, solar radiations during the day. The third type employs satellite communication, enabling long-distance quantum key distribution over free space, using satellites as relays.

The communication link being established is based on optical fibre technology. Placed at the Sanchar Bhavan and NIC building, two nodes create a link and generate encryption keys utilising quantum mechanical properties. These keys are then employed to encrypt all traffic between the two nodes, ensuring secure communication.

Interestingly, prize money of Rs 10 lakh has been announced for ethical hackers. The C-DoT official said, If anyone tries to tap into our system and extract the 256-bit key, it will be considered as a successful break. Through this, they are trying to identify the loopholes in the implementation of the systems be it in the code, or the associated hardware.

The official said that C-DoT is trying to identify people who have the expertise so they can give access to some channels. For this, people need to bring their own hardware or software and tap into the channel with help from C-DoT. Based on a mutually agreed timeline, which can range from one to two months, they will be asked to sit in their lab and experiment with their system to extract any keys. If successful, they will be rewarded for it.

Furthermore, speaking of what C-DoT plans to do ahead, the official said they are eyeing a mass deployment in QKD and are forcing various government agencies to adopt it. C-DoT is also working on more secure QKD protocols. Currently, they are using the security protocol called DPS (differential-phase-shift) and COW (coherent one-way). But they are working on developing device-independent protocols because of claims of potential attacks on the current protocols.

Although this is certainly a positive step, it is important to acknowledge that India is still lagging in quantum by years. China, for instance, already launched the worlds first quantum network using satellite communication back in 2016, followed by the completion of a 2,000-kilometre-long optical fibre network for QKD in 2017. Moreover, other countries such as the United States, Japan, Australia, and several European nations have also made significant progress in this area. Therefore, India needs to accelerate its efforts and invest more resources to catch up.

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Inside India's First Quantum Computing-based Telecom Network - Analytics India Magazine

Boston, March 23, 2023 Quantum computing company QuEra Computing today announced a partnership with the National Energy Research Scientific Computing Center giving NERSC access to QuEra neutral-atom technology. The partnership is intended to advance the centers development of quantum computers and address problems in quantum dynamics, chemistry, high-energy physics and other fields.

NERSC, at Lawrence Berkeley National Laboratory, has about 9,000 scientific users accessing the centers HPC and data resources. QuEra said the partnership will begin this spring enabling NERSCs users to evaluate the companys Aquila quantum computer.

It is a privilege to provide NERSC with access to QuEras quantum computing resources and tocontribute to its ongoing efforts to increase its users computing capability. We believe the vast pool ofscientists who turn to NERSC for computing resources stand to benefit greatly from our technology. It isour hope that through this partnership, we can help accelerate scientific breakthroughs in an array ofdisciplines, said Alexander Keesling, CEO at QuEra Computing.

NERSC is acknowledged in more than 2,000 scientific publications annually, making it a world leader inenabling scientific discovery though large-scale computation and data analysis.

Adding quantum computing and quantum simulation capabilities of this kind has the potential toimpact a significant portion of NERSCs user base, whose work is in sectors where there are directapplications for this technology, said Richard Gerber, Senior Science Advisor and Head of the HPCDepartment at NERSC. Technology is evolving quickly in the HPC space. This project will increase ourcapacity to better serve our users in the future and reflects NERSCs commitment to providing thescientific community with new and advanced computational tools as they become available.

For QuEra, this new partnership signals the opportunity to partner with NERSC in support of its missionto accelerate scientific discovery at the DOE Office of Science through high performance computing anddata analysis. This not only allows QuEra to deliver value for users in the near-term, but also provides anopportunity to gain valuable insights into how users deploy the technology and enables the company tomake adjustments that would potentially unlock even greater value for users.

Excerpt from:
QuEra Partnering with NERSC on Quantum Evaluation - High ... - insideHPC

March 21 (Reuters) - Nvidia Corp (NVDA.O), the computing company powering the bulk of artificial intelligence, is positioning itself as a key player in quantum computing with the launch of new software and hardware.

On Tuesday at its developer conference GTC, Nvidia unveiled CUDA Quantum, a platform for building quantum algorithms using popular classical computer coding languages C++ and python. The program would help run the algorithm across quantum and classical computers depending on which system is most efficient in solving the problem.

The new platform is named after CUDA, the software most AI developers use to access Nvidia's graphics processing unit (GPU) and which has given Nvidia chips a huge competitive edge.

"CUDA Quantum will do the same for quantum computing, enabling domain scientists to seamlessly integrate quantum into their applications and gain access to a new disruptive computing technology," said Tim Costa, Nvidia's director of HPC and quantum.

One difference, Costa said, is while CUDA is proprietary, CUDA Quantum is open source and was developed with input from many quantum computing companies.

Nvidia also launched a new hardware system called DGX Quantum to connect the quantum computer with classical computers. It was designed in partnership with Israeli-based startup Quantum Machines whose hardware communicates with quantum processors.

"We see more and more demand to integrate these quantum computers with standard computers," said Itamar Sivan, co-founder and CEO of Quantum Machines.

While quantum computers could potentially speed up some calculations millions of times faster than the fastest supercomputer, it is still uncertain when that would happen. And even when they become good enough to be useful, they would have to be paired with powerful digital computers to operate, said Sivan.

"All quantum today is research, not production, and that isn't going to change next week," said Costa. With DGX Quantum, researchers will be able to develop hybrid applications and critical methods for quantum computing's future, he added.

Reporting by Jane Lanhee Lee; Editing by Richard Chang

Our Standards: The Thomson Reuters Trust Principles.

Thomson Reuters

Reports on global trends in computing from covering semiconductors and tools to manufacture them to quantum computing. Has 27 years of experience reporting from South Korea, China, and the U.S. and previously worked at the Asian Wall Street Journal, Dow Jones Newswires and Reuters TV. In her free time, she studies math and physics with the goal of grasping quantum physics.

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Nvidia positions for quantum computing with new products - Reuters

More than a year into Russias war of aggression against Ukraine, there are few signs the conflict will end anytime soon. Ukraines success on the battlefield has been powered by the innovative use of new technologies, from aerial drones to open-source artificial intelligence (AI) systems. Yet ultimately, the war in Ukrainelike any other warwill end with negotiations. And although the conflict has spurred new approaches to warfare, diplomatic methods remain stuck in the 19th century.

More than a year into Russias war of aggression against Ukraine, there are few signs the conflict will end anytime soon. Ukraines success on the battlefield has been powered by the innovative use of new technologies, from aerial drones to open-source artificial intelligence (AI) systems. Yet ultimately, the war in Ukrainelike any other warwill end with negotiations. And although the conflict has spurred new approaches to warfare, diplomatic methods remain stuck in the 19th century.

Yet not even diplomacyone of the worlds oldest professionscan resist the tide of innovation. New approaches could come from global movements, such as the Peace Treaty Initiative, to reimagine incentives to peacemaking. But much of the change will come from adopting and adapting new technologies.

With advances in areas such as artificial intelligence, quantum computing, the internet of things, and distributed ledger technology, todays emerging technologies will offer new tools and techniques for peacemaking that could impact every step of the processfrom the earliest days of negotiations all the way to monitoring and enforcing agreements.

Although the well-appointed interiors of Viennas Palais Coburg and Genevas Hotel President Wilson will likely remain the backdrop for many high-level diplomatic discussions, the way parties conduct these negotiations will undoubtedly change in the years ahead. One simple example is the need for live language interpreters. The use of automated language processingas exemplified by Googles language-translating glassescould smooth negotiations, reducing the time spent on consecutive interpretation.

While some tools will speed negotiations, others will better inform diplomats ahead of talks. As Nathaniel Fick, the inaugural U.S. ambassador at large for cyberspace and digital policy, recently quipped, briefings generated by the AI-powered ChatGPT are now qualitatively close enough to those prepared by his staff. As large language models improve, AI will be able to search and summarize information more quickly than a team of humans, better preparing diplomats to enter negotiations.

Although these systems will need some degree of human oversight, allied parties can also compare notes, leveraging their respective AI systems. As more and more parties develop their own AI, we could see AI hagglebotscomputers that identify optimal agreements given a set of trade-offs and intereststake on a key role in negotiations. Ever more sophisticated AI systems may even one day reach a level of artificial general intelligence. Such systems could upend our understanding of technology, allowing AI to become an independent agent in international engagements rather than a mere tool.

As negotiations begin, parties may augment their delegations with AI, providing real-time, data-informed counsel throughout discussions. IBMs Cognitive Trade Advisor has already assisted negotiators by responding to questions about trade treaties that might otherwise require days or weeks to answer.

New technologies also allow countries to solicit citizen input more easily in real time. More than a decade ago, Indonesia pioneered a platform called UKP4, allowing everyday citizens to submit complaints about anything from damaged infrastructure to absent teachers. Although technology can be misused for manipulation and misinformation, artificial intelligence can also serve as a powerful tool to identify these misbehaviors, creating an ongoing struggle in the arms race between AI that will help and AI that will harm.

Intelligent systems can also help negotiators test various positions and scenarios in a matter of minutes. During the first round of Iran nuclear negotiations, a team at the U.S. Energy Department built a replica of an Iranian nuclear site to test every permutation of Iranian nuclear enrichment and development. In the future, an AI system will be able to run similar scenarios and virtual experiments faster and at a much lower cost.

When I worked on then-U.S. Secretary of State John Kerrys team negotiating the Joint Comprehensive Plan of Action (JCPOA) in 2014 and 2015, diplomats would meet in a variety of configurationsfrom large plenaries to one-on-one sessionstrying to discover the intentions behind the positions each side took and discern even minor differences among individual negotiators. While traditionally the privy of the espionage community, computer vision can now aid in this effort, identifying micro-expressions and other emotions by analyzing videos of negotiations. Even if diplomacy remains an art, it will increasingly rely on hard science.

When negotiators reach an agreement, they need to secure the support of their capitals and leadership, creating the need for secure communication. Negotiators have long faced the risk of spies and leaks and are now more exposed than before to the threat of intercepted calls and cybersecurity breaches.

New technology can both secure communication and put it at risk. Most strikingly, powerful quantum computers are likely to one day crack present-day encryption. The furor caused by the WikiLeaks revelations would pale in comparison to the bedlam that could unfold as foreign intelligence agencies decrypt thousands of confidential diplomatic cables.

As of today, many intelligence agencies are likely already intercepting and storing cables with the hope of decrypting them once they develop the requisite technological capabilities. In response, countries have developed new techniques to ensure the integrity of diplomatic communication through post-quantum encryption. In a December 2022 demonstration, French President Emmanuel Macron sent the French diplomatic services first quantum-secure telegram.

After parties announce a deal, technology can still play a role in ensuring their agreement enters into force. When the JCPOA went into effect in January 2016, the United States had difficulty releasing Iranian assets frozen after the revolutionbanks were still afraid to transfer money for fear of running afoul of the sanctions regime. In the end, the U.S. government delivered $1.7 billion in cash to Iran, flying $400 million on pallets to Tehran through Switzerland.

Distributed ledger technology has the potential to transparently ensure parties receive compensation and could be used to openly transfer funds while keeping in place sanctions for other purposes. Already, blockchain is showing its promise across a variety of use cases, including transferring information securely out of Ukraine. Working together with social enterprise company Hala Systems, a lab at Stanford University has used blockchain to document Russian war crimes, ensuring that original evidence of war crimes cannot be manipulated.

After agreement and implementation, monitoring is key to ensuring an agreement holds. In 2015, Iran agreed to a monitoring regime of unprecedented rigor. As Kerry explained at the time, Irans nuclear program will remain subject to regular inspections forever. In the future, the internet of thingsor the ability for items of daily use to be connected to the internetmay make such inspections far more effective by creating many new data points. Teams at Los Alamos National Laboratory, for example, have already used AI to detect signs of nuclear explosive tests by relying on data from international sensor networks.

Remote sensing can also play a role in ensuring parties follow through on their commitments. For example, once the exclusive domain of intelligence agencies, a team at Stanford has now used open-source geospatial imagery to monitor activity at Irans nuclear facility in Natanz. Once quantum sensing matures, it will become even more difficult for malicious actors to disguise their activities. Quantum sensors have already proven successful at mapping underground tunnels and identifying seismic activity. Granted, some of these applications are still far in the future; in any upcoming negotiations, monitoring will have to rely on more traditional methods. But the promise of these new technologies is vast.

Although our ways of waging war have evolved, our ways of waging peace have not yet made similar strides. Ukraines defense has laid bare the importance of bringing innovation to the battlefield. Its success at the negotiating table will be in no small part a result of technological innovation too.

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From ChatGPT to Quantum Computing, New Tech Could Reshape ... - Foreign Policy