Nearly three years ago, the first season of We Are Lady Parts on Peacock was a blast of giddy energy, with six episodes of punk rock, friendship and solidarity. After a far-too-long wait, the British comedy series is back tomorrow (May 30) with six new episodes, a brief but joyous return to the world of the all-female Muslim punk band Lady Parts. My only disappointment about the season is that its taken so long to arrive, and is over far too quickly.

Where to stream 'We Are Lady Parts'

"We Are Lady Parts" is streaming on Peacock

Lady Parts were just getting started as a band by the end of the first season, but as the second season opens, theyve already completed a U.K. tour and have amassed a decent-size fan base. Creator Nida Manzoor, who writes and directs every episode, smartly moves the story forward while retaining the character-focused approach that made the first season so appealing. Lady Parts may be getting famous, but that doesnt solve any of the individual band members personal problems.

Although lead guitarist Amina (Anjana Vasan) is still clearly the main character, anchoring every episode with her Sex and the City-style narration, season 2 offers extended arcs for all four band members, plus manager Momtaz (Lucie Shorthouse). Its a bit more balanced than the first season, which spent much of its time on Aminas internal conflict over whether to join the band.

Everyone in Lady Parts is now firmly committed to the band, and one of the seasons greatest strengths is that its always a celebration of their personal and artistic connection, even when faced with outside challenges.

Newly confident, Amina declares that shes in her villain era and goes after what she wants, even though shes not always sure what that is. Shes still pining for Ahsan (Zaqi Ismail), the brother of Lady Parts drummer Ayesha (Juliette Motamed), although she ends up dating his white co-worker instead. Romantic indecision aside, shes more assertive and ambitious this season, and that gives the show space for the other characters to deal with their own issues.

Ayesha is happy in a new relationship with an outgoing, supportive woman, but she still hasnt been able to come out to her parents. Bassist Bisma (Faith Omole) struggles with her self-image as a responsible wife and mother but also a rebellious punk rocker, and shes showcased in some of the seasons most eye-catching set pieces. Band frontwoman Saira (Sarah Kameela Impey) continues to fight for Lady Parts integrity, even as shes tempted by the prospect of a high-powered manager and a deal with a major record label.

Even Momtaz, who was more of a background presence in the first season, gets her own empowering arc, as she examines her place in Lady Parts career and the music industry as a whole. Aspects of the industry storyline feel rushed, given the limited number of episodes and their short running times, but Manzoor captures the entire life cycle of an up-and-coming band thrilled by the prospect of stardom, then disillusioned by corporate interference.

During the long break between seasons of We Are Lady Parts, Manzoor wrote and directed the action-comedy feature film Polite Society, and she brings some of that grand, stylized approach to the musical sequences in the new season. The first season had its share of catchy songs, but season 2 features even more original music, including an improbably rousing number about responding to work emails at a reasonable hour.

Lady Parts is a punk band, but their original songs in season 2 feature touches of country and rockabilly, too, and theyre catchy and fun while getting across the adversity that the characters often face as Muslim women in the U.K.

Manzoor, who writes the original songs with her siblings, also makes brilliant use of some unlikely covers: Amina brings poignancy to her solo rendition of Extremes More Than Words, Lady Parts rocks out to a hard-hitting version of Britney Spears Oops! I Did It Again while playing a for-hire gig at a wedding, and Bisma delivers an anguished, slowed-down take on Nina Simones Dont Let Me Be Misunderstood while working through her family issues. The show even comes close to making Hoobastanks goopy power ballad The Reason tolerable during a climactic romantic declaration.

Each song is presented via elaborate musical sequences, with magical-realist touches that make their way into the non-musical scenes as well. Bismas tension with her daughter is represented by a remote control that allows her to literally pause family arguments to vent her frustrations, and Sairas potential censorship by the bands new label manifests itself in her voice being forcefully silenced.

Those heavy moments never drag We Are Lady Parts into sad-com territory, though. This is a comedy that always remembers to be funny, delivering consistently clever jokes while staying true to its characters lived experiences. Bingeing the new season may only offer a short window of time to spend with those characters, but its worth savoring every minute.

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'We Are Lady Parts' is back for season 2 and it's an irresistible binge - Tom's Guide

Gone are the days when self-help books for women were cheesy, impersonal, and boring. Now, if you know where to look, you'll find empowering, genuinely useful self-help books designed to make you feel goodlike you'rereceivingadvice from a trusted friend or an inspiring mentor. Self-help books are also handy for anyone interested in growing their creativity, career, emotional maturity, or spiritual life. No matter what you're going through in lifeor even if you're just looking for a life-changing book to readwe can all use a little self-help and learning from time to time. Ahead, check out some of the self-help books for women that made our required reading list.

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The 37 Best Self-Help Books for Women to Read in 2024 - MarieClaire.com

A research team led by Duan Luming at the Institute for Interdisciplinary Information Sciences at Tsinghua University in China has built the worlds most powerful ion-based quantum computing system, the South China Morning Post reported. The research achievement paves the way for scalable quantum computers in the future.

Considered the next frontier of computing, quantum computers promise faster computation that could help humanity solve challenges in medicine, astronomy, and climate change. This is achieved by using quantum bits or qubits to store information.

Unlike the classical bits in silicon-based computers, which can either be in an on state or off state, qubits can be both on and off simultaneously while occupying a range of states in between them, also known as superposition. This allows quantum algorithms to process information in a fraction of the time it takes for even the worlds fastest supercomputers.

Researchers are working with various quantum systems to determine the best way to work with qubits.

Ions or charged particles can be suspended using electromagnetic fields and used as qubits in a quantum system. However, previous work in this area has shown that although quantum information can be transferred using the collective motion of the ions, the system isnt suited for scaling up.

Just as scaling silicon-based computers helps achieve complex calculations, scalability is important in quantum computing as well. To overcome this challenge with ions, researchers have used trapped-ion systems instead.

In such a system, researchers use a one-dimensional ion crystal that binds the ions in a lattice structure within, hence the name trapped-ion system. The approach is quite popular among quantum physicists, who have achieved simulation with 61 ions so far.

The researchers in Duans team at Tsinghua University have created a record by achieving stable trapping and cooling of a two-dimensional crystal with 512 ions, a first in the field of quantum science.

The achievement was praised by reviewers as a milestone to be recognised at the journal where Duan and colleagues published their research findings, the SCMP report added.

The feat achieved by the Chinese researchers is important given that scalability with ions has been a problem in quantum computing before. The researchers demonstrated this ability in a stable quantum simulation system, which another reviewer of the paper dubbed the worlds largest simulation.

Quantum simulators are devices that help researchers find answers about quantum model systems by analyzing quantum effects. They are popular tools among researchers because they can help advance scientific knowledge about quantum systems.

The researchers also completed another simulation, using 300-ion qubits to successfully complete a quantum calculation. The SCMP report said that such a systems computational ability was already astronomical and far exceeded the capabilities of classical computers.

The research moves China closer to building large-scale quantum computers in the future, an area in which it is directly competing with the US. Interestingly, Duan, a doctoral student from the University of Science and Technology of China, spent 15 years teaching in the US before returning to China in 2018.

The research findings were published in the journal Nature this week.

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Ameya Paleja Ameya is a science writer based in Hyderabad, India. A Molecular Biologist at heart, he traded the micropipette to write about science during the pandemic and does not want to go back. He likes to write about genetics, microbes, technology, and public policy.

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China builds the world's most powerful ion-based computing machine - Interesting Engineering

Chinese scientists are one step closer to a future large-scale quantum computer after building the worlds largest quantum simulation machine based on the trapped-ion technique, praised by one academic journal reviewer as a milestone to be recognised.

The breakthrough was achieved under the leadership of Duan Luming, a quantum physicist renowned for his pioneering research, who returned to China in 2018 after 15 years of teaching in the United States.

Duan received his doctorate in 1998 from the University of Science and Technology of China, the countrys premier institute for quantum research, before joining the University of Michigan in the early 2000s.

Since his return, he has been a full-time professor at Tsinghua Universitys Institute for Interdisciplinary Information Sciences.

Duan and his colleagues, along with several research groups at universities and hi-tech companies around the world, have been chasing the trapped-ion approach to qubits.

Quantum bits, or qubits, are the building blocks of quantum computers, just as bits are in regular computers.

However, qubits are extremely difficult to harness in a controlled and repeatable way because of what is called their hazy nature.

Regular bits can be described as switches that are either on or off. But because uncertainty and probability hold sway in quantum physics, qubits can be both on and off at the same time, and also exist in a variety of in-between states.

Ions, or charged atomic particles, can be trapped and suspended in free space using electromagnetic fields. The qubits are stored in stable electronic states of each ion, and quantum information can be transferred through the collective motion of the ions in a shared trap.

But scalability remains a key challenge for this system.

This is where the trapped-ion approach comes in, as it offers one of the most promising architectures for a scalable, universal quantum computer.

Researchers earlier achieved quantum simulations with up to 61 ions in a one-dimensional crystal. Ion crystals are solids made up of ions bound together in a regular lattice the symmetrical three-dimensional structural arrangements of atoms, ions or molecules inside a solid.

But Duan and his teams quantum simulator was able to achieve the stable trapping and cooling of a two-dimensional crystal of up to 512 ions, in a first for science.

The feat holds great significance for the future of quantum computing, given that scalability is a major hurdle. The teams scaling up of the ions in a stable simulation system is seen as likely to pave the way to building more powerful quantum computers.

The findings of their study were published on Wednesday in the peer-reviewed journal Nature.

This is the largest quantum simulation or computation performed to date in a trapped-ion system, commented one reviewer.

Quantum simulators are devices that actively use quantum effects to answer questions about model systems and, through them, real systems. They are increasingly popular tools in the world of quantum computing for their role in advancing scientific knowledge and developing technologies.

Duan and his team also managed to perform a quantum simulation calculation using 300-ion qubits. They found the computational complexity of 300-ion quantum bits working simultaneously to be astronomical, far exceeding the direct simulation capability of classical computers.

Link:

US-returned Chinese physicist and team achieve world first in quantum computing - South China Morning Post

As technology continues to advance toward higher realms, a new mechanism has entered the crosshairs of scientists: quantum computing. This process uses the principles of fundamental physics to "solve extremely complex problems very quickly," according to McKinsey & Company.

Using logic-based computing to solve problems isn't a new phenomenon; it was (and remains) the basis for artificial intelligence and digital computers. However, quantum computers are "poised to take computing to a whole new level," McKinsey said, because the introduction of physics into computing has the "potential tosolvevery complex statistical problems that are beyond the limits of today's computers." Quantum computing alone "could account fornearly $1.3 trillion in valueby 2035."

However, while organizations like McKinsey are clearly high on the potential for quantum computing, others say that it could create a slew of new problems.

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Quantum computing is a huge leap forward because "complex problems that currently take the most powerful supercomputer several years could potentially be solved in seconds," said Charlie Campbell for Time. This could open "hitherto unfathomable frontiers in mathematics and science, helping to solve existential challenges like climate change and food security."

Quantum computing is already being used for more practical purposes. One company called D-Wave Systems has "used its quantum computer to help clients determine driver schedules for grocery-store deliveries, the routing of cross-country promotional tours and cargo-handling procedures at the port of Los Angeles," said Bob Henderson for The Wall Street Journal. It could even help optimize seemingly minute problems, such as the arranging of planes at airport gates. If trying to arrange just 50 planes at 100 gates, the number of possibilities would be "10 to the hundredth power far more than the number of atoms in the visible universe," said Henderson. No standard computer "could keep track of all these possibilities.But a quantum computer potentially could."

While ubiquitous usage of quantum computers is a long way away, there are some strides being made, as Google "has built a quantum computer that's about 158 million times faster than the world's fastest supercomputer," said Luke Lango, a senior investment analyst at InvestorPlace. And quantum theory in general "has led to huge advancements over the past century. That's especially true over the past decade," as scientists "have started to figure out how to harness the power of quantum mechanics to make a new generation of superquantum computers."

But with new advancements come new sets of problems. Case-in-point: Quantum computers have "become a national security migraine," said Campbell for Time, because its ability to solve problems "will soon render all existing cryptography obsolete, jeopardizing communications, financial transactions and even military defenses."

This would be "potentially a completely different kind of problem than one we've ever faced," Glenn S. Gerstell, a former general counsel for the National Security Agency, said to The New York Times. There may be "only a 1% chance of that happening, but a 1% chance of something catastrophic is something you need to worry about." This risk "extends not just to future breaches but to past ones: Troves of encrypted data harvested now and in coming years could ... be unlocked," said Zach Montague for the Times.

Even as the risks are documented, investors are working to ensure quantum computers can be used on a widespread scale. Curtis Priem, the co-founder of AI chip manufacturer Nvidia, is "looking to establish New York's Hudson Valley as an epicenter of quantum-computing research in the country," the Journal said. Priem has already donated more than $75 million to develop a quantum computing system at Rensselaer Polytechnic Institute, making it the first college campus in the world with such a device.

Others are looking at the future of the industry through a more financial lens; Illinois legislators will soon be "asked to consider a series of incentives" as part of the state's "intensifying push to become the nation's hub for quantum computing," said Crain's Chicago Business. One of these major proposals is the creation of an "'enterprise zone' that would allow the state to provide quantum companies exemptions from sales, payroll and utility taxes for up to 40 years." If lawmakers in Illinois pass these incentives, there is a high chance that other states could follow.

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Is quantum computing the next technological frontier? - The Week

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Researchers from Cleveland Clinic and IBM have recently published findings in the Journal of Chemical Theory and Computation that could lay the groundwork for applying quantum computing methods to protein structure prediction.

For decades, researchers have leveraged computational approaches to predict protein structures. A protein folds itself into a structure that determines how it functions and binds to other molecules in the body. These structures determine many aspects of human health and disease.

By accurately predicting the structure of a protein, researchers can better understand how diseases spread and thus how to develop effective therapies. Cleveland Clinic postdoctoral fellow Bryan Raubenolt, Ph.D. and IBM researcher Hakan Doga, Ph.D. spearheaded a team to discover how quantum computing can improve current methods.

In recent years, machine learning techniques have made significant progress in protein structure prediction. These methods are reliant on training data (a database of experimentally determined protein structures) to make predictions. This means that they are constrained by how many proteins they have been taught to recognize. This can lead to lower levels of accuracy when the programs/algorithms encounter a protein that is mutated or very different from those on which they were trained, which is common with genetic disorders.

The alternative method is to simulate the physics of protein folding. Simulations allow researchers to look at a given protein's various possible shapes and find the most stable one. The most stable shape is critical for drug design.

The challenge is that these simulations are nearly impossible on a classical computer, beyond a certain protein size. In a way, increasing the size of the target protein is comparable to increasing the dimensions of a Rubik's cube. For a small protein with 100 amino acids, a classical computer would need the time equal to the age of the universe to exhaustively search all the possible outcomes, says Dr. Raubenolt.

To help overcome these limitations, the research team applied a mix of quantum and classical computing methods. This framework could allow quantum algorithms to address the areas that are challenging for state-of-the-art classical computing, including protein size, intrinsic disorder, mutations and the physics involved in protein folding. The framework was validated by accurately predicting the folding of a small fragment of a Zika virus protein on a quantum computer, compared to state-of-the-art classical methods.

The quantum-classical hybrid framework's initial results outperformed both a classical physics-based method and AlphaFold2. Although the latter is designed to work best with larger proteins, it nonetheless demonstrates this framework's ability to create accurate models without directly relying on substantial training data.

The researchers used a quantum algorithm to first model the lowest energy conformation for the fragment's backbone, which is typically the most computationally demanding step of the calculation. Classical approaches were then used to convert the results obtained from the quantum computer, reconstruct the protein with its sidechains, and perform final refinement of the structure with classical molecular mechanics force fields.

The project shows one of the ways that problems can be deconstructed into parts, with quantum computing methods addressing some parts and classical computing others, for increased accuracy.

"One of the most unique things about this project is the number of disciplines involved," says Dr. Raubenolt. "Our team's expertise ranges from computational biology and chemistry, structural biology, software and automation engineering, to experimental atomic and nuclear physics, mathematics, and of course, quantum computing and algorithm design. It took the knowledge from each of these areas to create a computational framework that can mimic one of the most important processes for human life."

The team's combination of classical and quantum computing methods is an essential step for advancing our understanding of protein structures, and how they impact our ability to treat and prevent disease. The team plans to continue developing and optimizing quantum algorithms that can predict the structure of larger and more sophisticated proteins.

"This work is an important step forward in exploring where quantum computing capabilities could show strengths in protein structure prediction," says Dr. Doga. "Our goal is to design quantum algorithms that can find how to predict protein structures as realistically as possible."

More information: Hakan Doga et al, A Perspective on Protein Structure Prediction Using Quantum Computers, Journal of Chemical Theory and Computation (2024). DOI: 10.1021/acs.jctc.4c00067

Continue reading here:

Researchers apply quantum computing methods to protein structure prediction - Phys.org

NEW YORK, NY; BROOMFIELD, CO; LEMONT, IL; MAY 29, 2024 - In a new paper in Science Advances on May 29, researchers at JPMorgan Chase, the U.S. Department of Energys (DOE) Argonne National Laboratory and Quantinuum have demonstrated clear evidence of a quantum algorithmic speedup for the quantum approximate optimization algorithm (QAOA).

This algorithm has been studied extensively and has been implemented on many quantum computers. It has potential applications in fields such as logistics, telecommunications, financial modeling, and materials science.

This work is a significant step towards reaching quantum advantage, laying the foundation for future impact in production, says Marco Pistoia, Head of Global Technology Applied Research at JPMorgan Chase.

The team examined whether a quantum algorithm with low implementation costs could provide a quantum speedup over the best-known classical methods. QAOA was applied to the Low Autocorrelation Binary Sequences (LABS) problem, which has significance in understanding the behavior of physical systems, signal processing and cryptography. The study showed that if the algorithm was asked to tackle increasingly larger problems, the time it would take to solve them would grow at a slower rate than that of a classical solver.

To explore the quantum algorithms performance in an ideal noiseless setting, JPMorgan Chase and Argonne jointly developed a simulator to evaluate the algorithms performance at scale. It was built on the Polaris supercomputer, accessed through the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility.The ALCF is supported by DOEs Advanced Scientific Computing Research program.

The large-scale quantum circuit simulations efficiently utilized the DOE petascale supercomputer Polaris located at the ALCF. These results show how high-performance computing can complement and advance the field of quantum information science, says Yuri Alexeev, a computational scientist at Argonne.

To take the first step toward practical realization of the speedup in the algorithm, the researchers demonstrated a small-scale implementation on Quantinuums System Model H1 and H2 trapped-ion quantum computers. Using algorithm-specific error detection, the team reduced the impact of errors on algorithmic performance by up to 65%.

Our long-standing partnership with JPMorgan Chase led to this meaningful and noteworthy three-way research experiment that also brought in Argonne National Lab. The results could not have been achieved without the unprecedented and world leading quality of our H-Series Quantum Computer, which provides a flexible device for executing error-correcting and error-detecting experiments on top of gate fidelities that are years ahead of other quantum computers, says Ilyas Khan, Founder and Chief Product Officer of Quantinuum.

Read the full research paperhere.

About JPMorgan Chase JPMorgan Chase & Co. (NYSE: JPM) is a leading financial services firm based in the United States of America (U.S.), with operations worldwide. JPMorgan Chase had $4.1 trillion in assets and $337 billion in stockholders equity as of March 31, 2024. With over 63,000 technologists globally and an annual tech spend of $17 billion, JPMorgan Chase is dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Under the J.P.Morgan and Chase brands, the Firm serves millions of customers in the U.S., and many of the worlds most prominent corporate, institutional and government clients globally. Visit http://www.jpmorganchase.com/tech for more information.

About Argonne National Laboratory Argonne National Laboratory seeks solutions to pressing national problems in science and technology by conducting leading-edge basic and applied research in virtually every scientific discipline. Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energys Office of Science.

About Quantinuum Quantinuum,the worlds largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. Quantinuums technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With almost 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.

Quantinuum recently closed an equity fundraise anchored by JPMorgan Chase with additional participation from Mitsui & CO., Amgen and Honeywell, which remains the companys majority shareholder, bringing the total capital raised by Quantinuum since inception to approximately $625 million.

The Honeywell trademark is used under license from Honeywell International Inc. Honeywell makes no representations or warranties with respect to this service.

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JPMorgan Chase, Argonne National Laboratory and Quantinuum Show Theoretical Quantum Speedup with the ... - JP Morgan

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In a new paper in Science Advances, researchers at JPMorgan Chase, the U.S. Department of Energy's (DOE) Argonne National Laboratory and Quantinuum have demonstrated clear evidence of a quantum algorithmic speedup for the quantum approximate optimization algorithm (QAOA).

This algorithm has been studied extensively and has been implemented on many quantum computers. It has potential applications in fields such as logistics, telecommunications, financial modeling and materials science.

"This work is a significant step towards reaching quantum advantage, laying the foundation for future impact in production," said Marco Pistoia, head of Global Technology Applied Research at JPMorgan Chase.

The team examined whether a quantum algorithm with low implementation costs could provide a quantum speedup over the best-known classical methods. QAOA was applied to the Low Autocorrelation Binary Sequences problem, which has significance in understanding the behavior of physical systems, signal processing and cryptography. The study showed that if the algorithm was asked to tackle increasingly larger problems, the time it would take to solve them would grow at a slower rate than that of a classical solver.

To explore the quantum algorithm's performance in an ideal noiseless setting, JPMorgan Chase and Argonne jointly developed a simulator to evaluate the algorithm's performance at scale.

"The large-scale quantum circuit simulations efficiently utilized the DOE petascale supercomputer Polaris located at the ALCF. These results show how high performance computing can complement and advance the field of quantum information science," said Yuri Alexeev, a computational scientist at Argonne. Jeffrey Larson, a computational mathematician in Argonne's Mathematics and Computer Science Division, also contributed to this research.

To take the first step toward practical realization of the speedup in the algorithm, the researchers demonstrated a small-scale implementation on Quantinuum's System Model H1 and H2 trapped-ion quantum computers. Using algorithm-specific error detection, the team reduced the impact of errors on algorithmic performance by up to 65%.

"Our long-standing partnership with JPMorgan Chase led to this meaningful and noteworthy three-way research experiment that also brought in Argonne. The results could not have been achieved without the unprecedented and world-leading quality of our H-Series Quantum Computer, which provides a flexible device for executing error-correcting and error-detecting experiments on top of gate fidelities that are years ahead of other quantum computers," said Ilyas Khan, founder and chief product officer of Quantinuum.

More information: Ruslan Shaydulin et al, Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem, Science Advances (2024). DOI: 10.1126/sciadv.adm6761

Journal information: Science Advances

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Research team shows theoretical quantum speedup with the quantum approximate optimization algorithm - Phys.org

NXP Semiconductors N.V., eleQtron, and ParityQC, part of the QSea consortium of the DLR Quantum Computing Initiative (DLR QCI), have revealed the first full-stack, 10 qubit, ion-trap based quantum computer demonstrator made entirely in Germany. The quantum computer demonstrator is located in Hamburg, reinforcing the citys role as a significant technology and research hub in Germany. It will enable early access to real quantum computing resources, allowing companies and research teams to leverage quantum computing advantages in applications such as climate modeling, global logistics, and materials sciences.

The QSea I demonstrator combines eleQtrons MAGIC hardware, ParityQC architecture, and NXPs chip design and technology, complemented by a digital twin. The next phase of the QSea project will focus on making the quantum computer increasingly powerful and industry-ready. The demonstrator is set up at the DLR QCI Innovation Center in Hamburg and will be available to industry partners and DLR research teams. This collaboration aims to foster an advanced quantum computing ecosystem in Germany and support digital sovereignty efforts in critical technology areas.

A press release announcing the delivery of this computer has been posted on the parityQC website here. In addition, a blog has been posted here on the DLR QCI website that provides further details about this first project QSea I as well as a follow-on project called QSea II that will create a modular, scalable quantum computer based on multiple ion trap chips connected together.

June 1, 2024

See original here:

NXP, eleQtron, and ParityQC Deliver a 10 Qubit, Full-Stack Ion-Trap Based Quantum Computer Demonstrator to the DLR Quantum Computing Initiative -...

Researchers from Cleveland Clinic and IBM recently published findings in the Journal of Chemical Theory and Computation that could lay the groundwork for applying quantum computing methods to protein structure prediction. This publication is the first peer-reviewed quantum computing paper from the Cleveland Clinic-IBM Discovery Accelerator partnership.

For decades, researchers have leveraged computational approaches to predict protein structures. A protein folds itself into a structure that determines how it functions and binds to other molecules in the body. These structures determine many aspects of human health and disease.

By accurately predicting the structure of a protein, researchers can better understand how diseases spread and thus how to develop effective therapies. Cleveland Clinic postdoctoral fellow Bryan Raubenolt, Ph.D., and IBM researcher Hakan Doga, Ph.D., spearheaded a team to discover how quantum computing can improve current methods.

In recent years, machine learning techniques have made significant progress in protein structure prediction. These methods are reliant on training data (a database of experimentally determined protein structures) to make predictions. This means that they are constrained by how many proteins they have been taught to recognize. This can lead to lower levels of accuracy when the programs/algorithms encounter a protein that is mutated or very different from those on which they were trained, which is common with genetic disorders.

The alternative method is to simulate the physics of protein folding. Simulations allow researchers to look at a given proteins various possible shapes and find the most stable one. The most stable shape is critical for drug design.

The challenge is that these simulations are nearly impossible on a classical computer, beyond a certain protein size. In a way, increasing the size of the target protein is comparable to increasing the dimensions of a Rubik's cube. For a small protein with 100 amino acids, a classical computer would need the time equal to the age of the universe to exhaustively search all the possible outcomes, says Dr. Raubenolt.

To help overcome these limitations, the research team applied a mix of quantum and classical computing methods. This framework could allow quantum algorithms to address the areas that are challenging for state-of-the-art classical computing, including protein size, intrinsic disorder, mutations and the physics involved in proteins folding. The framework was validated by accurately predicting the folding of a small fragment of a Zika virus protein on a quantum computer, compared to state-of-the-art classical methods.

The quantum-classical hybrid framework's initial results outperformed both a classical physics-based method and AlphaFold2. Although the latter is designed to work best with larger proteins, it nonetheless demonstrates this framework's ability to create accurate models without directly relying on substantial training data.

The researchers used a quantum algorithm to first model the lowest energy conformation for the fragments backbone, which is typically the most computationally demanding step of the calculation. Classical approaches were then used to convert the results obtained from the quantum computer, reconstruct the protein with its sidechains, and perform final refinement of the structure with classical molecular mechanics force fields. The project shows one of the ways that problems can be deconstructed into parts, with quantum computing methods addressing some parts and classical computing others, for increased accuracy.

Multidisciplinary collaboration was essential to achieve this framework.

One of the most unique things about this project is the number of disciplines involved, says Dr. Raubenolt. Our teams expertise ranges from computational biology and chemistry, structural biology, software and automation engineering, to experimental atomic and nuclear physics, mathematics, and of course quantum computing and algorithm design. It took the knowledge from each of these areas to create a computational framework that can mimic one of the most important processes for human life.

The teams combination of classical and quantum computing methods is an essential step for advancing our understanding of protein structures, and how they impact our ability to treat and prevent disease. The team plans to continue developing and optimizing quantum algorithms that can predict the structure of larger and more sophisticated proteins.

This work is an important step forward in exploring where quantum computing capabilities could show strengths in protein structure prediction, says Dr. Doga. Our goal is to design quantum algorithms that can find how to predict protein structures as realistically as possible.

The rest is here:

Clinic, IBM apply quantum computing to protein research - Cleveland Clinic Newsroom