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Stakeholders in the biopharmaceutical industry are currently exploring the implementation of quantum computing in order to expedite the drug discovery process and cut down the overall R&D capital investment

LONDON, Sept. 6, 2023 /PRNewswire/ --Roots Analysishas announced the addition of "Quantum Computing Marketin Drug Discovery, 2023-2035" report to its list of offerings.

Owing to the various benefits of quantum computing, such as big data processing and complex molecular modeling for minimizing cost and time investment, the adoption rate of quantum computing in pharmaceutical industry is expected to increase rapidly during the forecast period. Additionally, various partnerships have been inked for application of quantum computing in drug discovery. Majority of these partnerships are research and development agreements, followed by platform utilization agreements. Drug developers require support from both quantum computing software and hardware developers. In July 2021, Riverlane and Astex Pharmaceuticals announced their collaboration with Rigetti Computing to utilize their quantum systems along with Riverlane's algorithm expertise to develop molecular models of chemical compounds and study their interaction with proteins in the human body.

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The financial opportunity within the quantum computing in drug discovery services market has been analysed across the following segments:

The research also includes detailed profiles of the key players (listed below) engaged in the quantum computing in drug discovery services market; each profile features an overview of the company, its financial information (if available), details related to its service portfolio, and recent developments and an informed future outlook.

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The quantum computing in drug discovery services market is ... - PR Newswire

The effect of quantum computing on Artificial Intelligence could be as understated as it is profound.

Some say quantum computing is necessary to achieve General Artificial Intelligence. Certain expressions of this paradigm, such as quantum annealing, are inherently probabilistic and optimal for machine learning. The most pervasive quantum annealing use cases center on optimization and constraints, problems that have traditionally involved non-statistical AI approaches like rules, symbols, and reasoning.

When one considers the fact that there are now cloud options for accessing this form of quantum computing (replete with resources for making it enterprise-applicable for any number of deployments) sans expensive hardware, one fact becomes unmistakably clear.

With quantum computing, a lot of times were talking about what will it be able to do in the future, observed Mark Johnson,D-WaveSVP of Quantum Technologies and Systems Products. But no, you can do things with it today.

Granted, not all those things involve data science intricacies. Supply chain management and logistics are just as easily handled by quantum annealing technologies. But, when these applications are considered in tandem with some of the more progressive approaches to AI-enabled by quantum annealing, their esteem to organizations across verticals becomes apparent.

Quantum annealing involves the variety of quantum computing in which, when the quantum computer reaches its lowest energy state, it solves a specific problem even NP-hard problems. Thus, whether users are trying to select features for a machine learning model or the optimum route to send a fleet of grocery store delivery drivers, quantum annealing approaches provide these solutions when the lowest energy state is achieved. Annealing quantum computing is a heuristic probabilistic solver, Johnson remarked. So, you might end up with the very best answer possible or, if you dont, you will end up with a very good answer.

Quantum annealings merit lies in its ability to supply these answers at an enormous scale such as that required for a defense agencys need to analyze all possible threats and responses for a specific location at a given time. It excels in cases in which you need to consider many, many possibilities and its hard to wade through them, Johnson mentioned. Classical computational models consider each possibility one at a time for such a combinatorial optimization problem.

Quantum annealing considers those possibilities simultaneously.

The data science implications for this computational approach are almost limitless. One developer resource D-Wave has made available via the cloud is a plug-in for the SDK for Ocean a suite of open source Python tools that integrates with scikit-learn to improve feature selection. It supports recognizing in a large pattern of data, can I pick out features that correlate with certain things and being able to navigate that, Johnson remarked. I understand it ends up mapping into an optimization problem. The statistical aspects of quantum annealing are suitable for other facets of advanced machine learning, too.

According to Johnson, because of its probabilistic nature, one of the interesting things that quantum annealing does is not just picking the best answer or a good answer, but coming up with a distribution, a diversity of answers, and understanding the collection of answers and a little about how they relate to each other. This quality of quantum annealing is useful for numerous dimensions of machine learning includingbackpropagation, which is used to adjust a neural networks parameters while going from the output to the input. It can also reinforce what Johnson termed Boltzmann sampling, which involves randomly sampling combinatorial structures.

There are considerable advantages to making quantum annealing available through the cloud. The cloud architecture for accessing this form of computing is just as viable for accessing what Johnson called the gate model type of quantum computing, which is primed for factoring numbers and used in RSA encryption schema, Johnson confirmed. Organizations can avail themselves of quantum annealing in D-Waves cloud platform. Moreover, they can also utilize hybrid quantum and classical computinginfrastructure as well, which is becoming ever more relevant in modern quantum computing conversations. You would just basically be using both of them together for the part of the problem thats most efficient, Johnson explained.

In addition to the ready availability of each of these computational models, D-Waves cloud platform furnishes documentation for a range of example use cases for common business problems across industries. Theres also an integrated developer environment you can pull up that already has in it Ocean, our open source suite of tools, which help the developer interface with the quantum computer, Johnson added. Examples include the ability to write code in Python. When organizations find documentation in the cloud about a previous use case thats similar to theirs, You can pull up sample code that will use the quantum computer to solve that problem in your integrated developer environment, Johnson noted.

That sample code provides an excellent starting point for developers to build applications for applying quantum computing and hybrid quantum and classical computing methods to an array of business problems pertaining to financial services, manufacturing, life sciences, manufacturing, and more. Its just one of the many benefits of quantum computing through the cloud. The appeal of quantum annealing, of course, lies in its ability to expedite the time required to solve combinatorial optimization problems.

As the ready examples of quantum solutions the vast majority of which entail quantum annealing across the aforesaid verticals indicate, such issues are, the harder we look, ubiquitous throughout business, Johnson indicated. The data science utility of quantum annealing for feature selection, Boltzmann sampling, and backpropagation is equally horizontal and may prove influential to the adoption rates of this computational approach.

See more here:
D-Wave Suggests Quantum Annealing Could Help AI - The New Stack

The Gordon and Betty Moore Foundation awarded $1.25 million to 21 experimental physicists at the end of August, including associate physics professor Keji Lai.

The Experimental Physics Investigators Initiative grant, awarded through the foundation, aims to support researchers with innovative ideas who lack the funding to pursue research, Lai said. He plans to use the funding to study the properties of sound and how it can be imaged to further researchers understanding of advanced technology.

The (grant) funds faculty in the middle of their careers to explore new and creative research ideas that might be too speculative to be funded through more traditional mechanisms, said Andreas Matouschek, associate dean for research and facilities at the College of Natural Sciences, in a written response.

Lais proposal details a device that can image acoustic waves being transmitted between acoustic devices, like speakers and microphones in phones, under ultra-low temperatures, contributing to the field of quantum computing, which is a major branch of quantum information science.

Sound doesnt have to propagate in air, Lai said. It can propagate in liquid. It can propagate in solids. Its this particular property of sound that propagates in solid where we usually dont call it sound, we call it acoustic waves. That kind of property has already been very well explored by electrical engineers In fact, the cell phones that you and I are using these days have a lot of (acoustic) devices that are based on acoustic waves.

The research has implications for the transportation of large amounts of data. Computers used in daily life have classical bits, which are semiconductor chips that use zeroes and ones to store data, said Lai.

In quantum computation, those quantum bits have a much larger space to store information, Lai said. In fact, people are considering using acoustic waves to communicate using the acoustic wave to transport those quantities from one side to another.

The proposed device would help researchers better understand the process of quantum, and as a result, improve its efficiency, Lai said.

This tool potentially will provide some kind of way to go down to the inner workings of these chips and image or visualize how these cards are actually going inside the computer, Lai said. This will provide a way to help us: number one, understand the operation of quantum computation, (and) number two, help to debug and (know) when something does not go right.

The Strategic Research Initiatives at CNS assisted Lai with his proposal to the Moore Foundation. The initiative is now working with Lai to provide him with the necessary facilities to complete his project, said Emily Cole, director of strategic research initiatives for CNS.

This award allows Dr. Lai to pursue his most creative, curiosity-driven ideas, which could lead to the next breakthrough in quantum computing, Cole said in a written response. We want to see our researchers win similar awards and always encourage our faculty to pursue these opportunities.

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Research grant awarded to UT physics professor The Daily Texan - The Daily Texan

Theres a joke, playing on the quantum worlds unique properties, that goes, There are three types of people in this world: Those who understand quantum computing, those who dont understand quantum computing, and those who simultaneously do and do not understand quantum computing. All kidding aside, Weiwen Jiang sees a world in which quantum computing is in widespread use; with new funding from the National Science Foundation (NSF), he is taking steps toward that goal.

Jiang, an assistant professor in George Mason Universitys Department of Electrical and Computer Engineering, is leading two recently awarded NSF projectsworth a total $900,000for work on the development of these complex devices and on building the quantum workforce of tomorrow.

Quantum computers differ from classical computers in that they use elements of quantum mechanics to perform calculations, allowing them to operate much faster and crunch more data. While there are several operational quantum computers in useIBM and Google are among the top manufacturersthey currently are far from their promised potential and simply cannot yet make the large-scale calculations predicted of them.

Jiang said one key problem is, They are not stable. We can use them for computations, but you might get one answer today and then get an entirely different answer tomorrow.

Quantum devices are notoriously susceptible to noisespecifically, things like cosmic rays, changes in the Earth's magnetic field, radiation, and even mobile wi-fi signals. The noise contributes to the devices instability.

The $600,000 collaborative grant will fund the work of Jiang and his collaborators from Kent State University in developing an adaptor that will adjust to fluctuating noise, improving the performance of applications on quantum devices. Jiang is well versed on the topic, having recently won the Best Poster Award for System-level optimizations in improving the robustness of quantum applications on unstable quantum devices at an event at Oak Ridge National Lab.

According to Jiangs preliminary works, the deployment of the quantum applications faces several challenges, including: sustainabilityon one quantum processor, most quantum applications are sensitive to the temporal changes of quantum noise; portabilitydifferent quantum processors (even from the same vendor) with specific properties will lead to variation of model uncertainty; and transparencya lack of visualization tools can block users from tailoring their quantum applications to quantum computers for higher reliability. The NSF project will systematically provide solutions in response to these challenges.

Jiang is optimistic about the future of quantum computing: Every year, we see a lot of breakthroughs. Just a couple of months ago IBM published a paper on noise reduction. And every year, we see that the number of qubits in quantum computers increases from five in the year 2000 to over 400 on a new computer from IBM. (A qubit is the basic unit of information used in quantum computing, much like a 1 and 0 for traditional computing.)

Another grant, which Jiang shares with collaborators MingzhenTian and JessicaRosenberg in the College of Science, provides $300,000 from NSF to bolster the quantum workforce pipeline. The grant is for an end-to-end quantum system integration training program. The faculty members are developing a new course at Mason, organizing workshops at the IEEE International Conference on Quantum Computing in September (where Jiang is the quantum system track co-chair), and conducting tutorials at international conferences. Recently the team, led by Rosenberg, coordinated a summer immersion program at Mason for high school students. In addition, in the coming months, Jiang will be conducting seminars at a variety of minority-serving institutions in the DC region.

Jiang said the opportunities for quantum-trained engineers are robust and growing. I have collaborations locally with Leidos and MITRE, for example, and they have needs in this field. Further, we know that quantum will make a difference in everything from finance to drug discovery to machine learning and beyond.

He is encouraged about the quantum futureboth in the world and here at Mason. He stressed that as student demand grows for this technology, we need to provide the appropriate materials for our students, because were seeing a lot of strong interest in this field.

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Quantum Conundrums: Navigating Noise and Enhancing Expertise - George Mason University

August 28, 2023August 28, 2023

A digital signature developed by researchers from Clemson University and three universities in Europe could become part of the national standard for encryption tools designed to protect the privacy of digital information against quantum computers in the future.

The U.S. National Institute of Standards and Technology (NIST) is holding a competition to select standard post-quantum digital signature algorithms that would securely authenticate email, credit card and bank transactions, and digital documents from unwanted third parties tampering.

The researchers CROSS (Codes and Restricted Objects Signature Scheme) proposal was named a candidate for standardization.

Now, researchers from around the world will try to break it.

If you think about it, this is the best way to choose the standards, said Felice Manganiello, an associate professor in the Clemson School of Mathematical and Statistical Sciences and one of the developers of CROSS. Once they decide which proposals are the candidates, the rest of the world can try to attack them to find vulnerabilities. These systems are secure until they are not anymore. So, these competitions are actually a healthy way to decide the standard by having a lot of people working on proving the security.

Clemson graduate student Freeman Slaughter and researchers from Polytechnic University of Marche,Polytechnic University of Milanand Technical University of Munich also worked on the proposal.

Quantum computers could revolutionize the future of fields such as medicine, finance, energy and transportation by solving complex problems that are beyond the reach of even the best of todays classic supercomputers.

Unlike conventional computers that perform computation and store information in binary form (1s and 0s), quantum computers exploit the strange properties of quantum physics to operate on information in multiple forms known as qubits. By leveraging two key phenomena quantum superposition and entanglement quantum computers can explore multiple solution pathways simultaneously, allowing them to solve problems that would take a classic computer too long to calculate.

With that power would come the ability to crack todays standards for encryption and digital signatures, which rely on math problems that even a combination of the fastest conventional computers find intractable.

The standards we have today would not be sufficient, Manganiello said.

The NIST announced the first group of three digital signatures in July 2022 after a multi-year vetting process. It called for additional digital signature proposals in 2022. About 50 proposals were received and 40 were named candidates.

A digital signature is a mathematical algorithm used to validate the authenticity and integrity of an email, credit card transaction or digital document. Digital signatures create a virtual fingerprint that is unique to a person or entity and are used to identify users and protect information in digital messages or documents.Digital signaturesare significantly more secure than other forms of electronic signatures, according to the Cybersecurity and Infrastructure Security Agency.

Six of the digital signature candidates are code-based signatures, including CROSS.

Manganiello said that after the NISTs first call for proposals several years ago, researchers realized that code-based cryptography was not competitive because it led to large signatures.

The code-based problems were the oldest and safest problems, but they were leading to very large signatures. That made the whole community start working on what could be done to decrease these signature sizes, he said.

While CROSS is code-based, it uses Merkle trees and zero-knowledge protocols to make the signatures shorter.

Our digital signature algorithm is competitive because the signatures are quite small and the speed of computing them is faster with respect to the other candidates, he said. The only issue is that the system is based on a more recent problem than others and theres not as much literature attacking it, he said.

Manganiello said it could take several years for the NIST to decide whether the researchers algorithm will be selected as a standard.

Or email us at news@clemson.edu

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Clemson mathematicians' collaborative digital signature is a ... - Clemson News