The results of applied and experimental quantum physics research are all around us, having made their way from the lab to the real world in the form of technology like sensors and lasers. But these areas are just two pieces of the larger quantum research puzzle theoretical research is needed to lay the foundation for advances in the field.

If youre wanting to build your fantastic device thats going to revolutionize the world, you have to understand how that device works, says Roger Moore, professor and department chair in the University of Albertas Department of Physics.

To make the most of applied quantum technology, you need to work backwards in a sense, explains Joseph Maciejko, associate professor in the Department of Physics and director of the Theoretical Physics Institute.

For example, before you make a computer chip, you need to decide on the material its made of. To do that, you need an understanding of what it is about the properties of certain materials that would make them best suited to the job at hand knowledge thats in the territory of theoretical quantum physics.

To understand these things you can touch, you need to understand things that you increasingly cannot touch. You have to think about them and you need mathematics to describe things that you cant really see with your naked eye. This is where the theory comes in, says Maciejko.

While experimental and applied quantum physicists can test their hypotheses and innovative ideas in the lab through experiments, theoretical quantum physicists work solely in the realm of thought. That makes collaboration a critical part of the process.

Thats how we (theorists) experiment. We experiment with thought, and so we need to bounce ideas around, says Maciejko.

Theorists need more theorists around them, adds Moore. That sharing, that interchange of ideas, can hugely advance science at a far more rapid pace than someone sitting in their office by themselves trying to solve everything.

Quantum Horizons Alberta, a new $25-million, provincewide network created through a partnership with the University of Alberta, the University of Calgary and the University of Lethbridge, will allow that kind of collaboration between theoretical researchers to flourish. Supported by a group of visionary donors, the network is dedicated to advancing fundamental, theoretical quantum science.

Our chances of achieving greatness, our chances of achieving a position on the world stage in quantum research, is much greater the more resources within the province we can gather. We wanted to make sure we have the benefit of the bench strength that already exists in all three universities, says Richard Bird, one of four donors behind the network along with Joanne Cuthbertson, Patrick Daniel and Guy Turcotte.

The focus of Quantum Horizons Alberta is on building capacity in Alberta, especially when it comes to scientific expertise capacity in the province, says Andr McDonald, associate vice-president of strategic research initiatives and performance at the U of A. The creation of these contiguous nodes of research expertise across the province is what is going to help crystallize and strengthen the pan-Alberta approach to developing research on fundamental quantum science in the province.

As McDonald explains, the U of A is well positioned as a node within the network, with over $100 million of infrastructure and equipment needed to support fundamental quantum research and training.

We have all of this physical infrastructure and now what were working to do is expand our social infrastructure by bringing on professors, postdoctoral researchers and other trainees.

Alberta universities have a long history of quantum science collaboration, and joint achievements to show for it, says Robert Thompson, associate vice-president (research) and professor in the Department of Physics and Astronomy at the University of Calgary. Quantum Horizons Alberta will increase quantum capacity across the province, while creating opportunities for each institution to apply their unique expertise to shared goals for research and impact.

As Moore explains, theoretical physics is kind of like the opening chapter of a story you may not know what the ending will be, but its a crucial part of the overall narrative.

Or, think of it this way. To construct even a simple structure out of LEGO, you need to first understand how to put the little bricks together. The same is true for the kind of quantum research that results in innovative advances that change our world youre trying to get to a deeper understanding of quantum physics, how matter behaves in this world, how electrons talk to each other and react in different scenarios. You need to understand the rules before you can put together a solid structure, explains Maciejko.

At least from my perspective, you cannot do applied science without fundamental science, because all of applied science at some point relies on fundamental science, he says. Like mechanics and Newtons laws: its applied science now but it was fundamental science in the 1700s.

If we want to have new technologies or things that affect society, we have to also invest in fundamental science. Theres this pipeline from very fundamental science and mathematics to building devices and selling them. Theres a lot of examples of things people thought were just fundamental research that have become very practical, explains Lindsay LeBlanc, associate professor in the Department of Physics and Canada Research Chair in Ultracold Gases for Quantum Simulation.

There are a few key areas in quantum theory. Research in superconductivity examines the properties of superconductors, special materials that conduct electricity perfectly. Quantum computing is another branch, with researchers inventing devices that store information in a quantum measurement called qubits rather than the standard bits found on a regular computer. Experts like Maciejko working in the field of topological materials study exotic new materials with distinct properties that could be the key to the next generation of devices. And then theres particle physics, which studies the fundamental building blocks of nature by looking at matter in its most minute form, at the subatomic level.

I would say quantum materials and quantum computing are two of the really big directions that we hope to push with (Quantum Horizons Alberta), says Maciejko, although researchers involved in the network are bringing expertise in many different areas of quantum science.

This is key because all quantum physicists, not just theorists, are trying to answer increasingly complex questions, and as a result, no one researcher has all the knowledge, explains LeBlanc. Collaboration is needed between physicists working in various branches of quantum.

Having these different techniques and different backgrounds really helps people come up with new ideas, and thats always what were going for new approaches to solving problems that are really hard to solve, she says.

The fact that Quantum Horizons Alberta focuses specifically on theoretical research sets it apart, according to Maciejko.

Most certainly there will be ramifications for applications, but (the donors) really wanted to support fundamental science, research for the sake of discovering new things, and that was a big point.

As Bird reveals, during the donors discussions, they learned that nearly all the funding was going into applications and commercialization of quantum science and they realized that focusing their funding dollars in another direction would allow them to make a major impact.

There was a real gap in the funding for what we call foundational or theoretical quantum science, says Bird. All the academics we spoke to thought we can only go so far developing applications of what we already know. Eventually, we need to better understand the foundations of this area of science.

I would say Quantum Horizons Alberta is unique in the history of Canadian science, says Maciejko. We have the Perimeter Institute, but this is the first time something like this is happening out west, so its a big deal.

While it can take decades or even centuries for advances in physics to move from the realm of thought to real-world applications, without the theoretical and fundamental components, were missing key pieces of the puzzle, Maciejko says.

The fundamental science of today is the applied science of tomorrow. And if we cut the pipeline, then at some point, applied science is going to run out. Were going to run out of ideas, of inspiration.

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Understanding the building blocks of quantum research is key to ... - University of Alberta

Common sense is useless in the world of the extremely tiny, where the rules of quantum mechanics apply. One of the most amazing differences is that two particles like two photons of light can be entangled in such a way that what happens to one determines what happens to the other, even though they are very far apart. It is what Einstein, skeptical, called spooky action at a distance. The 78-year-old physicist Anton Zeilinger, born in the small Austrian town of Ried im Innkreis, has spent a quarter of a century proving that the most absurd predictions of quantum physics are correct. A little over a decade ago, his team succeeded in teleporting a quantum state between two entangled photons of light. One photon was in La Palma and the other in Tenerife, both part of Spains Canary Islands. There were 89 miles (143 kilometers) between them.

Zeilinger, of the University of Vienna, won the 2022 Nobel Prize in Physics for teleporting information and paving the way for exponentially faster and more secure quantum computers. The Austrian physicist sat down for an interview over coffee on the terrace of a hotel in Valencia overlooking the Mediterranean, during a break in his activity as a member of the jury for the Rey Jaime I Awards, which recognize achievements in research and entrepreneurship.

Question. You first heard about quantum entanglement at a conference in 1976. What did you think?

Answer. I didnt understand what was going on. I just realized that it must be interesting.

Q. How do you explain entanglement to people with no prior background in this field?

A. No one is completely without prior background. The entanglement of two particles is like you have a pair of dice. Three is rolled on one die and three is also rolled on the other. If one die shows six, the other also shows six. And the same number always comes up on both dice.

Q. Einstein said that God does not play dice.

A. I believe that God puts the numbers so that we believe that he plays dice, but he does not play dice. God says: now it is three, now it is two, now it is six. And we believe that God plays dice.

Q. In your Nobel lecture, you stated that not even God knows what information is in the particle.

A. Maybe he knows. Or maybe not. We cannot know.

Q. Do you use God as a metaphor or do you believe in God?

A. Yes. Why not believe? The famous Isaac Newton published books on many subjects, but he wrote much more about religion than physics. He was a religious person.

Q. Two entangled particles can be imagined as twin brothers who behave similarly at a distance because they share the same DNA, but that's not how it works.

A. In entanglement, the two quantum siblings behave the same, but without DNA.

Q. Its more than counterintuitive. Its crazy.

A. It's crazy, yes.

Q. Einstein defined entanglement as a spooky action at a distance. Does it seem spooky to you?

A. Einstein used the German word geisterhaft, which means something like spiritual. It is a phantasmagorical phenomenon if you try to explain it with the usual rules. But in quantum physics, you know how it works.

Q. In your Nobel lecture, you projected a question on the screen: Is the Moon there when no one is looking at it? What is your answer?

A. The important thing is that to prove that the Moon is there, you have to look at it. If you dont look at it, you can only use your experience and your logic to say that it is there. But, with quantum particles, you cant tell the system is there if no one is looking. Einstein asked: Do you really believe that the Moon is not there when no one is looking? And [Danish physicist Niels] Bohr replied: Can you prove otherwise? Can you prove that the Moon is there when no one is looking? And no, you cant.

Q. Niels Bohr stated: It is a mistake to think that the task of physics is to find out what nature is like. Physics is concerned with what we can say about nature.

A. I would go one step further and say: What can be said about nature, in principle, also defines what can exist. So nothing can exist without the possibility of saying something about it.

Q. What is reality then?

A. In physics, we have always made great progress without answering the question of what this is. We only answer the question of what can be measured and how can we observe something. We can observe reality, we can make measurements, but I dont think we can say anything about the essence of reality.

Q. Are you a Christian?

A. Yes, I was raised Catholic, but my mother was a Protestant, so I learned from both. Sometimes I went to the Protestant church with my mother and sometimes to the Catholic mass with my father. It was interesting.

Q. When you see this world of particles doing crazy things, how does that craziness fit in with the idea of an organized God?

A. The Jesuit theologian and philosopher Karl Rahner said: The Christian of the future will be a mystic or he will not exist at all. I agree. It cannot be said that God is organized or is like this or like that. God is not subject to our definitions.

Q. Perhaps God does not exist without the gaze of the observer.

A. It is a different type of observation: it is not with the eyes, it is an observation with the soul.

Q. After your experiment in the Canary Islands, you stated that the teleportation of information plays a vital role in the vision of a global quantum internet, since it provides secure communication without restrictions [...] and an increase in exponential of computing speed. When will we see those promises delivered?

A. Good question. As to when we will have full quantum computing, we dont know. In fact, today I would be more cautious with my statements, because the challenge is enormous. In small quantum computing systems there is a lot of work underway, but for big computers there is still a lot to do.

Q. Google is already making big announcements about imminent quantum computers.

A. They have a quantum computer, but it is small and can only be used for very specialized problems, not more general problems. To have a complete quantum computer, you need about 1,000 quantum bits. And now we are talking about systems with 30 or 50 quantum bits.

Q. You predicted in an interview in 2010 that in 15 or 20 years we would have an interesting quantum computer.

A. Yes, I would say the same today [laughs]. It is impossible to speak about 20 years from now.

Q. You also said, perhaps provocatively, that in the future we will have quantum computers on cell phones.

A. That will be in 50 or 100 years. I didnt say it to provoke, but as a challenge. When the first computers were built, they were huge, taking up an entire room. And then nobody thought that you could have it on a mobile phone.

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Nobel winner Anton Zeilinger: Physicists can make measurements, but cannot say anything about the essence of reality - EL PAS USA

CHEYENNE, WY / ACCESSWIRE / June 8, 2023 / Quantum computing - a technological evolution once thought to be decades away - is now right at our doorstep. While quantum computers may greatly benefit both scientific advancement and industrial application, they also represent a serious threat to the security of our digital infrastructure - particularly for blockchain-based technologies, such as cryptocurrencies. The destabilization of an increasingly crucial part of our global financial system could have major (and potentially devastating) effects.

The Quantum Resistance Corporation, Thursday, June 8, 2023, Press release picture

To shed light on this complex and evolving landscape, Dr. Pierre-Luc Dallaire-Demers ("PL"), Founder/CEO, and William Doyle ("Will"), Core Developer, of Pauli Group spoke about their work at the forefront of quantum-resistant blockchain technologies.

What are the biggest issues for crypto with the growth of quantum computing?

PL: The inherent security weakness of public keys is the biggest issue. Everyone has been led to believe they are almost impossible to break, but the reality is that a quantum computer running with about 1 million qubits - which we will see within the next 5-10 years - will break keys in a matter of hours. As an example, the first 1 million BTC mined in the Satoshi era explicitly list their public keys in the block explorer, and thus getting hacked would have catastrophic consequences on the economics of the blockchain and cascading collapse of the trust for the whole web3 industry since, as most blockchains use the same signature method.

The National Institute of Standards and Technology (NIST) has been working on standardizing cryptographic signature methods that can resist quantum computers - but we need to act ASAP on implementing it on a mass scale.

How long is left before quantum computing is a serious threat or it's too late to act?

Will: I think quantum computing is a serious threat right now. This is because it's unclear exactly when quantum computers will be capable of breaking secp256k1 - and other modern cryptographic primitives, which is when the whole thing will unravel.

Story continues

PL: The algorithm to break elliptic curve cryptography - which crypto uses - was actually present as far back as 2003, but nothing out there was powerful enough to process it - so when Bitcoin came around, everyone felt it was totally safe. It's not. We expect to see machines with millions of qubits by the decade's end, which will be able to perform this task with ease. At that point, non-quantum-secure blockchains will be totally at risk. As quantum computers grow in the 2030s, the rate of key breaking will skyrocket in parallel, rendering old blockchains completely obsolete in the 2040s. Fortunately, we still have a window to upgrade our infrastructures to resist quantum computers, but it's a challenging task that requires immediate action.

Why aren't large networks such as Ethereum doing more to protect their networks?

PL: Large networks are definitely aware of the implications of quantum computing for the security of their blockchains but they're not putting sustained efforts toward upgrading to quantum-resistant cryptography. No major network has a multi-year migration plan either. This absolutely needs to change if they care about the long-term viability of the existing networks.

The main issue is that we expected computers of this power to be over a hundred years away, but they've arrived far sooner than expected - and everyone is sort of scrambling around trying to work out what to do, or ignoring the issue entirely. But if we all get organized we can prepare.

What can crypto investors do now to protect themselves?

PL: The best strategy in the short term is for users to hedge their crypto investment with a post-quantum secure digital asset such as the Quantum Resistant Ledger and move their existing blockchain assets into a quantum-resistant wallet. Pauli Group uses our own Anchor Wallet, which has the strongest quantum-resistant cryptography available to permanently secure assets against the potential vulnerabilities posed by quantum computers.

Describe the professional journeys that led you both here.

PL: My journey with quantum computing began in 2006 when I pursued a Ph.D. in the field and a post-doc at Harvard, then worked as a quantum computer scientist at Xanadu. My interest in cryptocurrencies started in 2013, and over time as I saw quantum computers scaling at a rapid rate I recognized an impending and problematic intersection of these two fields. This led me to found Pauli Group in the summer of 2021.

Will: I have been in the blockchain space for years with a focus on blockchain security. During my time in the industry, I've witnessed a rapid rise in technology that threatens the very decentralized financial freedom that cryptocurrency was created for.

What problem was Pauli Group created to solve?

PL: Pauli Group was born out of an understanding that large-scale quantum computers are no longer a distant possibility but a rapidly approaching reality. The whiplash progress in this field means that these machines could be a reality by the end of this decade, and this poses a significant threat to the security of blockchains. Our aim is to monitor the progress of quantum computers and their ability to break blockchain cryptography and to develop solutions that protect users and their assets in the long run.

Will: Pauli Group was created to innovate at the overlapping space between quantum computing and blockchain technology. We firmly believe that the security, integrity and trust in blockchains must remain uncompromised even in the post-quantum era.

Learn more about the Pauli Group here: https://pauli.group/.

Featured photo by Towfiqu barbhuiya on Unsplash.

Contact:Mike Zeiger4d5a@theqrc.com

SOURCE: The Quantum Resistance Corporation

View source version on accesswire.com: https://www.accesswire.com/760041/Protect-Digital-Assets-From-The-Threat-Of-Supercomputers-QA-With-Quantum-Computing-And-Blockchain-Security-Experts

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Protect Digital Assets From The Threat Of Supercomputers: Q&A With Quantum Computing And Blockchain Security Experts - Yahoo Finance

A University of Minnesota Twin Cities-led team has developed a new superconducting diode, a key component in electronic devices, that could help scale up quantum computers for industry use and improve the performance of artificial intelligence systems.

The paper is published in Nature Communications, a peer-reviewed scientific journal that covers the natural sciences and engineering.

A diode allows current to flow one way but not the other in an electrical circuit. It essentially serves as half of a transistor which is the main element in computer chips. Diodes are typically made with semiconductors, substances with electrical properties that form the base for most electronics and computers, but researchers are interested in making them with superconductors, which additionally have the ability to transfer energy without losing any power along the way.

Compared to other superconducting diodes, the researchers device is more energy efficient, can process multiple electrical signals at a time, and contains a series of gates to control the flow of energy, a feature that has never before been integrated into a superconducting diode.

We want to make computers more powerful, but there are some hard limits we are going to hit soon with our current materials and fabrication methods, said Vlad Pribiag, senior author of the paper and an associate professor in the University of Minnesota School of Physics and Astronomy. We need new ways to develop computers, and one of the biggest challenges for increasing computing power right now is that they dissipate so much energy. So, were thinking of ways that superconducting technologies might help with that.

The University of Minnesota researchers created the device using three Josephson junctions, which are made by sandwiching pieces of non-superconducting material between superconductors. In this case, the researchers connected the superconductors with layers of semiconductors. The devices unique design allows the researchers to use voltage to control the behavior of the device.

Their device also has the ability to process multiple signal inputs, whereas typical diodes can only handle one input and one output. This feature could have applications in neuromorphic computing, a method of engineering electrical circuits to mimic the way neurons function in the brain to enhance the performance of artificial intelligence systems.

The device weve made has close to the highest energy efficiency that has ever been shown, and for the first time, weve shown that you can add gates and apply electric fields to tune this effect, explained Mohit Gupta, first author of the paper and a Ph.D. student in the University of Minnesota School of Physics and Astronomy. Other researchers have made superconducting devices before, but the materials theyve used have been very difficult to fabricate. Our design uses materials that are more industry-friendly and deliver new functionalities.

The method the researchers used can, in principle, be used with any type of superconductor, making it more versatile and easier to use than other techniques in the field. Because of these qualities, their device is more compatible for industry applications and could help scale up the development of quantum computers for wider use.

Right now, all the quantum computing machines out there are very basic relative to the needs of real-world applications, Pribiag said. Scaling up is necessary in order to have a computer that's powerful enough to tackle useful, complex problems. A lot of people are researching algorithms and usage cases for computers or AI machines that could potentially outperform classical computers. Here, were developing the hardware that could enable quantum computers to implement these algorithms. This shows the power of universities seeding these ideas that eventually make their way to industry and are integrated into practical machines.

This research was funded primarily by the United States Department of Energy with partial support from Microsoft Research and the National Science Foundation.

-30-

About the College of Science and Engineering

The University of Minnesota College of Science and Engineering brings together the Universitys programs in engineering, physical sciences, mathematics and computer science into one college. The college is ranked among the top academic programs in the country and includes 12 academic departments offering a wide range of degree programs at the baccalaureate, master's, and doctoral levels. Learn more at cse.umn.edu.

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New research could improve performance of artificial intelligence ... - UMN News

vacuum chamber package.IonQ

Classical machine learning (ML) is a powerful subset of artificial intelligence. Machine learning has advanced from simple pattern recognition in the 1960s to today's advanced use of massive datasets for training and the generation of highly accurate predictions.

Meanwhile, between 2010 and 2020, global data usage increased from 1.2 trillion gigabytes to almost 60 trillion gigabytes. At some point, quantum systems will more easily handle the ongoing exponential growth in data compared to classical computers, which may struggle to keep up. Theoretically, at some point in the not-too-distant future, only quantum computers can handle such massive scale and complexity. Applying this same insight to the realm of ML, it only makes sense that at some point, the real breakthroughs will be coming from quantum machine learning (QML) rather than classical approaches.

IonQ

Although other quantum computing companies are exploring QML, there are several reasons I have focused on advanced QML research being done at IonQ ($IONQ).

One, IonQ's CEO, Peter Chapman, has a rich background in machine learning when he worked with Ray Kurzweil at Kurzweil Technologies. Chapman played a crucial role in developing a pioneering character recognition system that generated text characters from scanned images.urzweil Technologies eventually used that approach to build a comprehensive digital library for the blind and visually impaired.

Two, Chapman is optimistic about the future of QML. He believes that QML will eventually be as significant as the large language models used by OpenAI's ChatGPT and other generative AI systems. For that reason,QML is built into IonQ's long-term quantum product roadmap.

And three, IonQ collaborates with leading companies in the field of AI and machine learning, such as Amazon, Dell, Microsoft, and NVIDIA. These partnerships combine IonQ's expertise in quantum technology with the partner's AI knowledge of their partners.

IonQ hardware and #AQ

IonQ's primary focus is not just on qubit quantity but more comprehensively on the quality of the qubits and how they operate as a system. This qualityalso called qubit fidelityis a critical differentiator for efficiently completing quantum computations, one that IonQ measures with an application-oriented benchmark that it calls algorithmic qubits or #AQ.

#AQ is based on work pioneered by the Quantum Economic Development Consortium, an independent industry group that evaluates quantum computer utility in real-world settings. Here is how #AQ is computed.

IonQ quantum processors

IonQ has created three trapped-ion quantum computers: IonQ Harmony, IonQ Aria and its latest model, a software-defined quantum computer called IonQ Forte.

There are two Arias online. According to Chapman, the second Aria machine was needed to handle increased customer demand and to improve the company's redundancy, capacity and order processing speed.

Additionally, IonQ is working hard to make the IonQ Forte commercially available

IonQ

IonQ Aria and IonQ Harmony are cloud accessible via Google, Amazon Braket, Microsoft Azure and IonQ Quantum Cloud. According to the company, cloud access for IonQ Forte will be announced later. Let's take a deeper look at the different quantum computers that IonQ has built:

Forte recently demonstrated a record 29 AQ, which puts it seven months ahead of IonQ's original AQ goal for 2023.

Note: IonQ's next major technical milestone is achieving 35 AQ. At the 35 AQ level, using classical hardware to simulate quantum algorithms can become very challenging and costly. At that point, IonQ believes it will be easier and less expensive for some customers to run models on actual quantum machines rather than attempting to simulate them classically.

ML + QC = QML

Even though quantum computing is still being carried out by mid-stage prototypes, it has the potential, perhaps within this decade, to solve problems far beyond the capability of classical supercomputers. Meanwhile, as quantum computing prototypes are getting closer to becoming operationally sound, scaled versions of classical ML models are already being used in hundreds of thousands of applications across almost every industry. These range from personalized recommendations on shopping sites to critical healthcare diagnostics, such as analyzing X-rays and MRI scans to detect diseases more accurately than humans can.

QML is a still-developing field that uses quantum computers for challenging ML tasks, even though at this point quantum machines are less practical than classical computers. Combining ML and quantum computing (QC) to produce QML creates a technology that should soon be even more powerful than classical machine learning.

According to Peter Chapman, much of today's QML is created by converting classical machine learning algorithms into quantum algorithms. QML is not without challenges. It has many of the same problems as those associated with current quantum computers, the most prevalent being susceptibility to errors caused by environmental noise and decoherence due to prototype hardware limitations.

"Look at the past research we've done with Fidelity, GE, Hyundai and a few others," Chapman said. "All those projects started with regular machine learning algorithms before we converted them to quantum algorithms."

He explained, however, that IonQ's research has shown QML performance to be superior to many of its classical ML counterparts. "Our QML versions beat comparable classical ML versions," he said. "Sometimes the results show that the QML model did a better job capturing the signal in the data, or sometimes the number of iterations needed to go through the data was substantially less. And sometimes, as our most recent research indicates, the data needed for QML was about 8,000 times less than a classical model needs."

Why QML performs better than classical ML

QML uses superposition and entanglement, two principles of quantum mechanics, to develop new machine learning algorithms. Quantum superposition allows qubits to be in multiple states simultaneously, whereas quantum entanglement allows many qubits to share the same state. This is in contrast to classical physics, where a bit can be in only one state at a time, and where connectivity between bits is only possible by physical means. The relevant quantum properties allow developers to create QML algorithms to solve problems that are intractable using classical computers.

It is important to note that QML is still in its early stages of development. It is not yet powerful enough to solve very large and very complex machine learning problems. Still, QML has the potential to revolutionize classical machine learning by training models faster, providing greater accuracy and opening the door for newer and even more powerful algorithms.

Quantum artificial intelligence

Quantum AI is even newer than QML. About a year ago, IonQ started exploring quantum AI. Its first research effort produced a paper on modeling human cognition that was published in the peer-reviewed scientific journal Entropy. The paper shows that human decision making can be tested on quantum computers. Since the 1960s, researchers have found that people don't always follow the rules of classical probability when making decisions. For instance, the sequence in which people are asked questions can influence their answers. Quantum probability helps clarify that oddity.

The research paper doesn't say that the brain explicitly operates on using quantum mechanics. Instead, it applies the same mathematical structures to both fields, which adds to the intrigue of using quantum computers to simulate human cognition.

"We are excited by the potential for quantum to not only add power to machine learning but to artificial general intelligence or AGI as well," Chapman said. "AGI is the point at which AI is strong enough to accomplish any task that a human can. Some things are almost impossible to model on a classical computer but are possible on a quantum computer. And, I think that AGI will likely be where these kinds of problem sets will be done."

Wrapping up

Quantum Machine Learning is still an emerging field. It is the intersection where techniques from quantum information processing, machine learning and optimization come together to solve problems faster and more accurately than classical machine learning.

It is possible to use classical machine learning algorithms and convert them to quantum machine learning. IonQ has done this successfully several times. These QML models often outperform the original ML models.

QML offers several advantages over traditional machine learning thanks to quantum mechanics in the form of superposition and entanglement. QML can complement the growing trend of using ML models for many classification tasks, from image recognition to NLP.

Analyst's notes:

Here are a few IonQ QML-related research papers I found interesting:

January 2023 Quantum natural language processing (QNLP) is a subfield of machine learning that focuses on developing algorithms that can process and understand natural language (i.e., the languages spoken by humans). IonQ researchers demonstrated that statistically meaningful results can be obtained using real datasets, even though it is much more difficult to predict than with easier artificial language examples used previously in developing quantum NLP systems. Other approaches to quantum NLP are compared, partly with respect to contemporary issues including informal language, fluency and truthfulness.

January 2023 Research by IonQ focused on text classification with QNLP. This research demonstrated that an amplitude encoded feature map combined with a quantum support vector machine can achieve 62% average accuracy predicting sentiment using a dataset of 50 actual movie reviews. This is small, but considerably larger than previously-reported results using quantum NLP.

November 2022 This joint research by IonQ, the Fidelity Center for Applied Technology (FCAT) and Fidelity Investments focuses on generative quantum learning of joint probability distribution functionsGANs, QGANs and QCBMsall of which use machine learning to learn from data and make predictions. The research demonstrates that a relationship between two or more variables can be represented by a quantum state of multiple particles. This is important because it shows that quantum computers can be used to model and understand complex relationships between variables.

November 2021 IonQ and Zapata Computing developed the first practical and experimental implementation of a hybrid quantum-classical QML algorithm that can generate high-resolution images of handwritten digits. The results outperformed comparable classical generative adversarial networks (GANs) trained on the same database. GAN is a machine learning model with two neural networks that compete against each other to produce the most accurate prediction.

September 2021 Researchers from IonQ and FCAT developed a proof-of-concept QML model to analyze numerical relationships in the daily returns of Apple and Microsoft stock from 2010 to 2018. Daily returns are the price of a stock at the daily closure compared to its price at the previous day's closure. The metric measures daily stock performance. The model demonstrated that quantum computers can be used to generate correlations which cannot be efficiently reproduced by classical means, such as probability distribution.

December 2020 In a partnership between IonQ and QC Ware, classical data was loaded onto quantum states to allow efficient and robust QML applications. Machine learning achieved the same level of accuracy and ran faster than on classical computers. The project used QC Ware's Forge Data Loader technology to transform classical data onto quantum states. The quantum algorithm, running on IonQ's hardware, performed at the same level as the classical algorithm, identifying the correct digits eight out of 10 times on average.

Paul Smith-Goodson is the Vice President and Principal Analyst covering AI and quantum for Moor Insights & Strategy. He is currently working on several personal research projects, one of which is a unique method of using machine learning and ionospheric data collected from a national network of HF transceivers for highly accurate prediction of real-time and future global propagation of HF radio signals.

For current information on these subjects, you can follow him on Twitter.

Moor Insights & Strategy provides or has provided paid services to technology companies like all research and tech industry analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and video and speaking sponsorships. The company has had or currently has paid business relationships with 88, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Ampere Computing, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Cadence Systems, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cohesity, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, HYCU, IBM, Infinidat, Infoblox, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Juniper Networks, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, LoRa Alliance, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, Multefire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA, Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), NXP, onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Fivestone Partners, Frore Systems, Groq, MemryX, Movandi, and Ventana Micro., MemryX, Movandi, and Ventana Micro.

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A Quantum Leap In AI: IonQ Aims To Create Quantum Machine Learning Models At The Level Of General Human Intelligence - Forbes