Hi Steve, welcome to the AiThority Interview Series in 2023. Please tell us about your two decades of tech experience so far. How did you arrive at Mastercard?

My career has had several phases. For the first 10 years or so after graduating, I worked at various – mostly small – independent, software companies and consultancies. My position evolved over the years; I started as a developer before advancing to the role of a software engineering manager and then eventually becoming a CTO.

Next, I worked for Science Foundation Ireland (SF), Ireland’s national science funding agency, where I led our investments in topics such as computer science, data science, software engineering, and artificial intelligence.

In 2014 I started at Mastercard, which is where I still currently work today. Initially, I supported and grew a team called Start Path, an engagement program for innovative startups in the fintech space. A few years later, I joined Mastercard Foundry, the innovation and R&D arm within the company, leading research and development for AI, ML, and now also quantum computing. This July, I was appointed to Mastercard’s first class of Distinguished Engineers, a recognition for select Senior Vice President technical experts as part of the company’s continuing commitment to technology, innovation, and career growth. With this distinction, I continue my work with a focus on artificial intelligence and quantum computing. 

You are in charge of Mastercard R&D’s strategy and execution of AI and ML in new product development efforts. What is the biggest challenge to Digital Transformation in the market you cater to?

Of the many years that I’ve worked in technology – this current period is distinct for the speed and scale of innovation taking place. This dynamism is exciting because we’ve only just scratched the surface of what is possible for businesses and consumers, but with it also comes new challenges for enterprises.

For one – leveraging emerging technologies to build new products and services.

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Part of the equation is to understand and introduce technologies like web3, spatial computing, PETs, and AI/ML, while also maintaining and upgrading legacy systems, and aligning with ever-evolving legislation and regulation governing their application.

AI specifically has been top of mind for our market, especially following the fairly recent explosion of generative AI.  Mastercard has been putting AI to work for years, particularly in our products and solutions across open banking, routing, personalization, and fraud that enhance the safety and security of the payments ecosystem.  Although the step change between AI and generative AI is exponential in terms of what you can do with it, our deep roots in AI have afforded us the capabilities, talent, framework, and partnerships to keep a pulse and execute on emerging technologies.  

As a leader in the payments space, and as with any nascent technology, Mastercard has a responsibility to set the precedent for exploring generative AI responsibly.  We developed an AI governance program and guidelines for our data scientists to minimize risks in AI and best serve our customers, invested in partnerships with key institutions like RIT In Dubai and Howard University, and actively encouraged our employees to safety test and learn. 

What technologies within AI and computing are you interested in?

The idea of being able to control a computer system and anything connected to it through programming has fascinated me since I was a teenager.

Today I’m looking at how AI, mixed reality, spatial computing, and web3 have unlocked an entirely new frontier in technology. We’re likely to see several key trends, such as the rapid increase in computational power, both at the edge and in the cloud, and the tokenization of assets to start to coalesce around some of these new computing paradigms.

For AI, the incredible advances born from generative AI and Large Language Models (LLMs) are also contributing to the transformative period we’re in.

Currently, Mastercard is engaging in test-and-learn with generative AI applications to enhance operational efficiency and improve data quality, aggregation, entity resolution, and categorization.

We’re also using ML for certain models that support our open banking solutions, such as credit scoring, financial management insights, account opening, and payments. It enables us to extract, identify, and classify data quickly and more efficiently than rules-based models alone.

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In the longer term, I’m paying close attention to both quantum computing and AGI. With quantum, I’m tracking developments in both hardware and software, to understand how these new devices will help us to solve ever more complex computational problems, and what use cases will arise in our industry. With AGI, at some stage, we may be looking at the prospect of human-like machines that can solve a wide range of complex tasks at scale. 

In the current analysis, it is reported the global quantum computing (QC) market will be at $900 million. How do you see QC disrupting the digital market in the next couple of years? 

At $900m, quantum computing is still a very small part of the overall computing market.

Over the next few years, we’ll see quantum computers – inclusive of quantum annealers – get progressively more performant, capable, and reliable.

Currently, our best guess is that the earliest use cases in banking and payments will most likely be in the optimization space, with other applications such as machine learning coming later.

Rather than a disruption, it’s more probable that we’ll see quantum technology adopted gradually, across industries and companies, as the technology continues to improve, becomes more usable, and its primary use cases become more evident. 

What steps can young technology professionals take to enhance their proficiency in collaborating effectively with Cloud, Automation, and AI-based tools? 

Nothing beats getting “hands-on-keyboard” experience using these technologies.  One of the amazing benefits that all young tech professionals have today is readily available online learning materials. There are tutorials on YouTube for just about every emerging technology imaginable, and through cloud computing, there’s also access to the resources required to explore those areas. Many cloud and tech companies also offer cheap or free trial accounts to help young developers learn their technologies at little or no cost.

On the Mastercard Developers platform, for example, you’ll find a quick start guide that will walk you through how to create a new project using Mastercard’s APIs, and gain access to the Sandbox environment. So, armed with nothing more than a laptop and an internet connection, people can get access to all the technology they could imagine, even quantum computers!

One of the tried and tested ways to build skills in these areas has been through the open-source community – whether it’s contributing to an existing project that you find interesting or relevant, or starting a personal project that scratches your own itch. 

What are your predictions for AI/ML and other smart technologies heading beyond 2024?

As machine and deep learning evolve, so too will their role within our sector. This past year has been about experimentation. In 2024, we expect generative AI to continue to gradually integrate into business operations and products.

Companies are currently focused on internal generative AI applications, like software development co-pilots, knowledge bots and operational efficiency drivers that are serving as testbeds and laying the groundwork for what’s to come. This phase is likely to continue throughout the year, as companies start building the foundations for implementation. As challenges like data privacy, information accuracy and bias are addressed, we anticipate that the range of use cases will expand to include more ambitious and public-facing deployments.

 

One of the most compelling use cases for generative AI in the financial services industry is in open banking. With the aid of fine-tuned LLMs, generative AI can enable the cleaning and categorization of data at a significantly higher through-put and with more accuracy than previously available.

In line with informed data consent protocols, generative AI could streamline personal financial management, for example, by acting as a personal wealth manager to create an encompassed view of an individual’s financial well-being, help formulate college savings plans, procure loans and implement financial strategies – empowering people to navigate their financial lives more adeptly.

Thank you, Steve! That was fun and we hope to see you back on AiThority.com soon.

• About Steve
• About Mastercard

About Steve

Steve is an IT professional with more than 25 years of industry experience in payments, government, and academia. He is currently responsible for leading Mastercard Foundry’s R&D initiatives in emerging technologies, including artificial intelligence, machine learning, quantum computing, 5G and Web3. In this role, Steve leads a team of talented data scientists, data engineers and software engineers to bring new products and services to market.

About Mastercard

Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

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On stage at the IBM Summit, Algorithmiq unveils unprecedented error mitigation results of a large –scale experiment run on IBM hardware

Algorithmiq, a scaleup developing quantum algorithms to solve the most complex problems in life sciences, has successfully run one of the largest scale error mitigation experiments to date on IBM’s hardware. This achievement positions them, with IBM, as front runners to reach quantum utility for real world use cases. The experiment was run with Algorithmiq’s proprietary error mitigation algorithms on the IBM Nazca, the 127 qubit Eagle processor, using 50 active qubits x 98 layers of CNOTS and thus a total of 2402 CNOTS gates. This significant milestone for the field is the result of a collaboration between the two teams, who joined forces back in 2022 to pave the way towards achieving first useful quantum advantage for chemistry.

Quantum computers, when operated, still have high levels of errors that stand in the way of performing meaningful large scale calculations on the hardware. Currently, one of the biggest challenges in quantum computing is to overcome such noise in the execution. Error mitigation techniques are designed to enable running algorithms in the presence of errors. They, however, typically become very runtime inefficient as the problem size and qubit numbers increase. Scalable error mitigation is at the core of Algorithmiq’s drug discovery platform, Aurora, and is precisely what was validated on stage at IBM’s most anticipated annual Quantum Summit – a yearly event where IBM showcases to its clients and close partners the latest advancements in the quantum space and reveals next year’s hardware development roadmap.

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Advancing into the era of quantum utility with Tensor-network Error Mitigation techniques

The results were obtained using Algorithmiq’s proprietary Tensor Network Error Mitigation (TEM) techniques applied to an experiment carried out in collaboration with Ivano Tavernelli’s team at IBM Zurich and designed in conjunction with John Goold’s group at Trinity College Dublin. The powerful technique fully mitigated the noise even as the depth of the circuit increased, a regime where the best error mitigation methods would usually fail to work and recovered the quantum signal from practically nothing (at a value nearing 0).

Algorithmiq’s state-of-the-art methods are able to recover the signal in regimes where some of the previous error mitigation

methods struggled with significant improvements in terms of measurement overhead, which translates into much faster computations within hours instead of tens of years.

The impact of these experiments builds the foundations necessary for the application of quantum computation at scale which will remain relevant along the path to the fault-tolerance era.

Professor Sabrina Maniscalco, Co-Founder and CEO of Algorithmiq said, “It’s a great honour to be presenting this successful milestone with IBM’s team at the IBM Summit. Today represents further validation that Algorithmiq’s core error mitigation techniques are powerful and will enable large scale experiments on specific use cases leading us well into the quantum utility era for real commercial applications. I’ve dedicated over 20 years of my life to the study of noisy quantum systems, as a Professor, and I never thought this type of experiment would be possible so soon. Needless to say, I’m extremely excited about the goals we’ve set ourselves for 2024. Today’s results are just the beginning!”

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Quantum chemistry application results with AstraZeneca

Further to the announcement of the error mitigation technique, Sabrina Maniscalco, CEO and Co-founder of Algorithmiq was back on stage to present additional key achievements by the team’s latest published results, this time with AstraZeneca, IBM and the Hartree Centre on a novel approach suitable to study proton transfer reactions which treats both electrons and nuclei with the same quantum mechanics. Combining the latter with Algorithmiq’s hardware-adapted fermion-to-qubit mapping and compilation algorithms drastically reduced the quantum hardware requirements compared to existing methods (we have observed up to 54% reduction in number of noisy operations) and laid the groundwork for the first hardware experiments.

Guillermo García-Pérez, CSO and co-founder of Algorithmiq: “The significance of these results demonstrates the power of our key enabler, informationally complete measurements which, when combined with best in class hardware are the stepping stone to any scalable quantum simulation and the basis for any meaningful application.”

A new chapter for Qiskit Nature within Algorithmiq

To cement Algorithmiq’s lead in the quantum chemistry software space, IBM announced in their latest blog that the startup would become the new owners of Qiskit Nature code, IBM’s highly curated quantum community for chemistry. This news comes as part of a series of changes to the Qiskit Ecosystem, welcoming external partners to maintain the repositories and to provide the communities a greater role in the development of algorithms and applications.

Matteo Rossi, CTO and co-founder of Algorithmiq commented: “We’re excited to be the trusted new code owners of Qiskit Nature. With our team’s expertise in state-of-the-art methods in quantum chemistry and quantum computing, and by collaborating closely with the community, we aim to develop software that empowers researchers and companies to address the profound quantum simulation challenges ahead in the natural sciences.”

Quantum computing photo-activated drugs with IBM and Cleveland Clinic

Algorithmiq ‘s novel measurement approach combined with its state-of-the art chemistry methods have enabled the team to secure up to $4.25 million from the Wellcome Leap to design new photon drug interactions in cancer prevention and treatment alongside their partners, IBM and Cleveland Clinic.

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New MIT Sloan research presents a framework for business leaders to analyze whether to use quantum or traditional computing to yield the best outcome

MIT Sloan School of Management researchers present a new framework for understanding in which circumstances quantum computing will provide significant advantages to businesses, and when traditional computing will be the more economical choice.

“The Quantum Tortoise and the Classical Hare: A simple framework for understanding which problems quantum computing will accelerate (and which it will not)” is co-authored by Neil Thompson, research scientist at MIT Sloan and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Sukwoong Choi, assistant professor at University at Albany and digital fellow at MIT Initiative on the Digital Economy, and William S. Moses (MIT CSAIL PhD ’23), incoming assistant professor at University of Illinois Urbana-Champaign. This work is part of a larger collaboration between Accenture and the MIT Initiative on the Digital Economy, and is funded by Accenture.
“Many business leaders are looking to quantum computing as the promising successor to classical computing, but research shows–and leaders in  quantum computing agree–it will continue to underperform classical computing in many areas,” says Thompson. “As a result, to understand where quantum computing will perform better first requires understanding why it can be.”

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The research paper finds that quantum computers must meet two conditions to yield an improvement on classical computers. First, they  must be powerful enough to solve the problem at hand, which is a challenge because  most quantum computers are currently so small they can only run toy problems, such as factoring numbers so small that using a  classical computer makes more sense). Second, it must gain enough of an advantage from having a better algorithm that it can run a problem in less time than it would take a classical computer.

While these factors sound straightforward, they have powerful implications regarding which problems will benefit from quantum computing and which won’t. Better yet, they can often be calculated before investing in a project.

“Companies often consistently face fixed sets of problems, and decision-makers might wonder if these problems could be solved more quickly and efficiently with quantum computing,” Thompson says. “Our framework provides a way to analyze the potential impact of switching to quantum computing before making the investment.”

Thompson notes this research can be used across industries—such as automotive, chemistry, and finance—where companies have started to examine applications of quantum computing. In particular, the framework considers how well a quantum computer might solve a problem using Shor’s algorithm, which is one of a few well-known quantum algorithms, compared with how well a classical computer might solve a problem. The analysis reveals that many problems, particularly those of small to moderate size, will most likely not benefit from quantum computing in the near-term.

The reason that quantum computers will often be slower than classical ones (and therefore be the “tortoise” in this paper) is that the hardware itself runs slower, doing fewer operations per second.

“You might think this would doom them to always be slower, but sometimes they have more efficient ways of solving problems that are unavailable to classical computers,” says Thompson These quantum algorithms, if they provide enough of an advantage, can allow quantum computers to outpace their classical brethren. Unfortunately, even if quantum computers could theoretically be better, they often aren’t in practice because current and near-term quantum computers have limited computational capacity, so they aren’t powerful enough to run the problems where quantum computers are better.

The framework has an economic dimension, which the authors term “quantum economic advantage.” This represents the crossover point, when businesses would start wanting to use quantum computing because it represents the lowest-cost way of achieving a particular level of performance.

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This study gives managers and their teams the tools for understanding when quantum economic advantage is achieved, which requires evaluating the two key criteria:

• Feasibility: Can the quantum computer, given its speed and size, effectively handle the problem at hand?
• Algorithm Advantage: If the quantum computer is capable, would it actually process the task faster than a classical computer of comparable cost?

“We need to consider the speed of the computer versus the route,” Thompson said. “Thinking of it like a race, in getting from point A to point B, the algorithm is the route. If the race is short, it may not be worth investing in better route planning. For it to be worth it, it has to be a longer race.”
A consequence of these conditions is that larger problems show the most benefit from quantum computing. For example, computers might be used to look for a particular DNA sequence in a human genome. While the best classical algorithm for this problem is less powerful than the best quantum algorithm, classical computers are much faster and can run more applications at the same time. As problem sizes get larger, the “algorithmic benefit” of quantum computing becomes more important. In particular, the length of a typical human genome (~3 billion DNA “letters”) is not big enough for quantum to be better, so it shouldn’t be used for this purpose. By contrast, large databases of genomes (with more than 1 trillion letters) could be large enough where quantum is a better option.

“Ultimately, this research suggests that quantum computing will be the more attractive option under one of two conditions for businesses,” Thompson said. “First, that the quantum algorithm is exponentially faster than the classical algorithm, or second, that the quantum algorithm is significantly better and the problem size is quite large.”

Building on this analysis, Thompson says future research will delve into which specific problems and industries will benefit most from quantum—and which won’t.

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Quantum Computing Inc.’s partners Assured Cyber Protection Ltd and millionways order Reservoir Computers and Quantum Random Number Generator to enhance their AI capabilities.

Quantum Computing an innovative, quantum optics and nanophotonic technology company announced a milestone in its quest to bring cutting-edge quantum solutions to the market. The Company has secured multiple hardware sales of its state-of-the-art Reservoir Computer and Quantum Random Number Generator to Assured Cyber Protection Ltd and AI firm millionways, kicking off the commercial delivery of its computing technologies.

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The QCi Reservoir Computer, a first-to-market portable reservoir computing device with technology inspired by the power of quantum mechanics, can perform complex computational tasks with unprecedented speed and efficiency to facilitate data analysis, machine learning and artificial intelligence. Its minimal size, weight, power, and cost factors make it particularly suitable for use as an edge computing tool. QCi’s Reservoir Computer uses the Company’s proprietary capabilities to create a small footprint hardware device that requires 80% to 95% less power consumption than traditional computers. The QCi Reservoir Computer series leverages the Company’s unique competencies and expertise in both analog and digital design, resulting in an innovative piece of technology that interfaces seamlessly with a host machine through an Ethernet connection, enabling smooth communications and problem-free submission. The Reservoir Computer, available now, is QCi’s first major step on its path to building a hardware-based, Quantum Reservoir Computer to be released in 2024.

Additionally, the Quantum Random Number Generator (QRNG) developed by QCi has garnered interest from organizations that require true randomness for applications in cryptography, secure communications, and data encryption. With quantum-level entropy, QCi’s solution ensures the highest level of security for digital communications and data protection. QCi’s QRNG offers a unique feature that can provide random number sequences and obey arbitrary statistic properties as customized by end users. This new feature not only enhances various AI applications and security protocols but also supports financial modeling applications such as Monte Carlo simulations.

European-based Assured Cyber Protection Ltd (“Recyber”), a cybersecurity company that combines automation, artificial intelligence and advanced technology to reduce a business’ cybersecurity risk, has been on the forefront of defending digital assets against ever-evolving threats.  By integrating QCi’s Reservoir Computer and QRNG into their security algorithms, Recyber aims to enhance its ability to detect and respond to cyberattacks in real-time, staying ahead of cybercriminals in this ever-shifting landscape.

millionways, a New York City based technology firm, is a leader in the development of AI algorithms used to effectively provide quick turnaround analysis and next-gen feedback to users on their emotional IQ and personality insights based upon various forms of user-generated text or audio-to-text data.  Unlike other machine learning platforms such as ChatGPT, Alexa or Siri, millionways’ proprietary methodology makes behavorial predictions that have clear applications in generating the most informed decision in which social interactions drive behavorial outcomes.  The addition of QCi’s Reservoir Computer to millionways’ assessment process is expected to significantly boost machine learning efficiency, particularly in assessing audio files, dramatically reduce the power consumption and materially speed up the training.  Ultimately, quantum-enhanced AI technologies will enable millionways to provide even more accurate, personalized recommendations to its users, thus improving the overall user experience and increasing customer satisfaction.

Robert Liscouski, CEO and co-founder of Quantum Computing Inc. stated, “Recyber, a leading cybersecurity firm, and millionways, a pioneer in AI-driven decision-making platforms, are well positioned to benefit meaningfully from QCi technologies.  These innovative tools will enable them to efficiently address complex problems and challenges in their respective industries, pushing the boundaries of AI and cybersecurity capabilities.  The diverse application of our Reservoir Computer to the cybersecurity and human behavioral AI domains is exactly where we believe there will be a great opportunity for these types of technologies.  Similarly, our QRNG device, a truly unique random number generating device in the market, will address immediate security requirements for key distribution.”  Liscouski added, “We believe that quantum computing will drive the next era of technological advancement, particularly in the cybersecurity world, and we are excited to be at the forefront of this revolution.  Though QCi is small, as of now, we pack a big punch.  We are working hard to become the leader in quantum innovation and quantum solutions that provide real value to the business and government markets today.”

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Mitch Scherr, CEO and co-founder of Recyber, commented on his purchase of four QCi Reservoir Computers and a QRNG, “Cybersecurity requires constant innovation.  QCi’s quantum-enhanced technologies will strengthen our efforts to safeguard sensitive data and protect our company and its clients from evolving threats.  Recyber and QCi offer game-changing cybersecurity assessment, remediation and unparalleled protection.  QCi’s Reservoir Computer combined with Recyber’s AI technologies will enable Recyber to provide solutions that will protect clients and their Boards from the growing cyber threat in the marketplace today.  Our unique cyber behavioral analysis will be able to scale to small and large enterprises to better protect businesses from exploitation due to poor cyber hygiene and insider threats.  We entered our teaming relationship with QCi just over a month ago and, based on early customer feedback, we anticipate a highly productive future together.”

QCi’s efforts to build quantum reservoir computers is expected to bring quantum capabilities to artificial intelligence and significantly increase the speed and efficiency of AI while significantly lowering power consumption compared to power-hungry classical computers.  QCi is committed to ongoing research and development to further enhance its quantum solutions and expand its product offerings.  The Company’s long-term vision is to democratize quantum information and sensing technologies, making them accessible and practical for businesses and researchers around the world.

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The cutting-edge artificial intelligence (AI) trading tool offered by Quantum AI trading tool helps traders to make more competent or more informed decisions. The tools can help traders make significant profits.

Quantum AI and Callum Ferguson are pleased to announce that their trading bots for cryptocurrency have been released across Europe. Recently, a Canadian branch was opened to help traders make more informed decisions, which allows traders to make big profits. The cutting-edge AI trading tool is helpful in making smarter trading decisions to assist in the growth of the funds used in trading. The platform is built from the ground up to be entirely safe for all users of the AI tool. Trading can be done in a trustworthy and comfortable environment. The AI trading tool can be used in free Demo Mode to allow for familiarity with every feature and tool available from the software. Traders can learn from experience instead of from their mistakes.

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The Quantum AI Trading company platform is simple to understand and intuitive, allowing traders to make trades without the complexities and overthinking that come with most tools. Those traders who use trade automation bots help place cryptocurrency on autopilot. Traders can spend more time in profit and less time stressing about past and future trading decisions. The AI tool has been trained relentlessly on the protocols linked to cryptocurrency trading. The Quantum offers traders a variety of dependable strategies that they can put in place to trade in the same way that professional traders do. The Quantum AI app has undergone gauntlet tests and has been audited by the top professionals in the industry.

When users need help, Quantum AI provides expert support with all services and plans. Regardless of the type of subscription selected, the professional team believes that the entire community should have access to support every second of the day and every day of the year. Users of the tool have expressed a satisfaction rate of 98.79 percent. The support team is always available to customers when it is needed. The pros are committed to ensuring the customer satisfaction rate is as close to 100% as possible.

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Quantum AI can be customized to the needs and preferences of the customers. The tool is available in Italian, German, Dutch, French, Spanish, and English. Traders can easily switch languages at any time. More than 2.6 million transactions are completed every 24 hours. Assets traded reached $710 million. During the months of 2023, new users have grown to 97,862.

Quantum AI is an advanced AI software that uses quantum computing and artificial intelligence to analyze market trends within the cryptocurrency market and make smarter automatic trading decisions. Quantum AI uses advanced algorithms in sync with artificial intelligence to analyze market trends, news, and social media sentiment to posit future price movements of cryptocurrencies; it then executes trades based on these predictions.

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The tool has been extensively tested and has shown consistent profits in both bull and bear markets. However, like any trading tool, there is always a risk involved, and users should exercise caution. The cryptocurrencies that Quantum AI supports include Bitcoin, Litecoin, Ethereum, and Ripple. The Quantum technology algorithm can operate automatic trading on all the crypto markets using market APIs.

The Quantum AI trading app is entirely free of charge. No fees are associated with opening an account. In order to begin using the Quantum AI platform, users can sign up to open an account and start trading immediately. A small amount of capital allows novice traders to increase investment as familiarity with the tool increases gradually.

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SEEQC, the digital quantum computing company, announced  that it is working toward the first all-digital, ultra-low-latency chip-to-chip link between quantum computers and GPUs, compatible with any quantum computing system, in collaboration with NVIDIA.

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“It will enable end users and application developers for the first time to leverage the power of quantum across a wide range of fields — all at the scale of NVIDIA and fully integrated with the best-of-breed classical stack.”

When completed, this will be the first time an active multi-chip module quantum processor will be directly linked with both a GPU- and a CPU-operational platform, creating a tightly coupled, fully digital integration of quantum and classical computing technologies. As SEEQC’s technology is entirely digital, it will remove several analog steps and expensive, bulky and noise-contributing hardware overheads in quantum processing, severely cutting down the bandwidth needed, decreasing latency bottlenecks and unlocking scalable enterprise applications in the future.

SEEQC is the only company in the world creating a fully digital chip-based quantum computing technology that spans all quantum technologies. Through the adoption of the NVIDIA platform, SEEQC will bring a new level of high-speed, low-noise, low-latency quantum computing to enable functional quantum computing at scale.

Integrating classical and quantum computing to meet explosive demand for AI

This pioneering integration will support research and applications in practical, real-world implementations such as quantum artificial intelligence (AI), quantum machine learning (ML) and other datacenter-scale applications to meet the increasing needs of compute- and data-intensive enterprise AI.

“This all-digital integration will take advantage of each system for a low-latency interface while maintaining the highest possible bandwidth performance from each individual system,” said John Levy, CEO and co-founder of SEEQC. “The development we’re taking on with NVIDIA represents the best of breed in both quantum and classical; and, together, both core technologies create unprecedented compute power.”

Integrating quantum and GPUs will advance the NVIDIA CUDA Quantum platform for hybrid quantum-classical computing. This collaboration is a major milestone in SEEQC’s technology roadmap. With GPU acceleration and a software platform that is unique to each industry, SEEQC will now be able to develop a full-stack quantum computing architecture that can enable hybrid quantum AI and ML programs, as well as scalable real-time error correction — all powered by SEEQC’s proprietary Single Flux Quantum (SFQ) technology.

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A technology-compatible innovation, no matter the quantum system

“Tight integration of quantum with GPU supercomputing is essential for progress toward useful quantum computing,” said Tim Costa, Director, HPC and Quantum Computing Product at NVIDIA. “Coupling the NVIDIA GH200 Grace Hopper Superchip with SEEQC’s digital chip architecture — tied together by the CUDA Quantum programming model — will provide a major step toward that goal.”

“With this collaboration, SEEQC and NVIDIA are paving the way for the enterprise-grade quantum computing era,” said Jean-François Bobier, Quantum Computing research lead for Boston Consulting Group (BCG). “A one-hour computation with high speed and low latency turns into 40 days on slower quantum architectures. Therefore, low latency is the key to scalable applications and error correction, which will unlock 90% of quantum computing use cases in value.”

Scalable, heterogeneous quantum computing

The ultra-low-latency, digital chip-to-chip integration of a fully chip-based quantum processor to the Grace Hopper Superchip will deliver a powerful, scalable heterogeneous computer platform. This allows SEEQC to use NVIDIA’s digital architecture seamlessly with its own, advancing its capabilities for operations and processing algorithms.

“Scalable Energy-Efficient Quantum Computing is what SEEQC stands for; it’s a company well known to quantum experts. It will be the first to bring a ubiquitous, enterprise-grade quantum computing solution to market: a chip-based, full-stack QPU with high error correction potential, available across major quantum computing architectures,” said André M. König, CEO of Global Quantum Intelligence. “It will enable end users and application developers for the first time to leverage the power of quantum across a wide range of fields — all at the scale of NVIDIA and fully integrated with the best-of-breed classical stack.”

“We are connecting quantum and classical computing in a highly efficient way, taking advantage of a tight integration of both technologies to power systems that will run the most important quantum and classical applications,” said Matthew Hutchings, CPO and co-founder of SEEQC. “The only way to do this and accommodate high speed, low latency and high bandwidth is to do so on a chip-to-chip basis. This collaboration creates not just the possibility for scalable, real-time error correction in the near-term, but future quantum applications,

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International Data Corporation (IDC) published its second forecast for the worldwide quantum computing market, projecting customer spend for quantum computing to grow from $1.1 billion in 2022 to $7.6 billion in 2027. This represents a five-year compound annual growth rate (CAGR) of 48.1%. The forecast includes base quantum computing as a service as well as enabling and adjacent quantum computing as a service.

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A new IDC forecast for the worldwide quantum computing market projects customer spend for quantum computing to grow from $1.1 billion in 2022 to $7.6 billion in 2027.

The new forecast is considerably lower than IDC’s previous quantum computing forecast, which was published in 2021. In the interim, customer spend for quantum computing has been negatively impacted by several factors, including: slower than expected advances in quantum hardware development, which have delayed potential return on investment; the emergence of other technologies such as generative AI, which are expected to offer greater near-term value for end users; and an array of macroeconomic factors, such as higher interest and inflation rates and the prospect of an economic recession.

IDC expects the quantum computing market will continue to experience slower growth until a major quantum hardware development that leads to a quantum advantage is announced. Until then, most of the growth will be driven by maturation in quantum computing as a service infrastructure and platforms and the growth of performance intensive computing workloads suitable for quantum technology.

IDC also expects investments in the quantum computing market will grow at a CAGR of 11.5% over the 2023-2027 forecast period, reaching nearly $16.4 billion by the end of 2027. This includes investments made by public and privately funded institutions, internal allocation (R&D spend) from technology and services vendors, and external funding from venture capitalists and private equity firms. Of particular note is the growing interest in quantum computing by global government agencies of which 14 (13 countries plus the European Union) have announced quantum initiatives that span multiple years and will generate billions of dollars for quantum computing research.

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The billions of dollars being allocated to the research and development of quantum computing have led to recent advancements in quantum computing hardware and software, as well as new error mitigation and suppression techniques. These advancements fuel speculation that achieving a near-term quantum advantage may be possible using today’s noisy intermediate-scale quantum (NISQ) systems. Over the long term, these investments are expected to result in the delivery of large-scale quantum systems capable of solving some of the most complex problems that challenge today’s scientists and engineers, causing a surge in customer spend towards the end of the forecast period.

IDC sees 2022 as an eventful year in the quantum computing industry. Strategic approaches implemented to reach a near-term quantum advantage using NISQ systems became more defined as vendors published quantum computing road maps emphasizing methods for improving qubit scaling, as well as new techniques for error mitigation and suppression. To improve the accessibility and usability of quantum systems, previously inaccessible quantum modalities became accessible for end-user experimentation, while other quantum hardware vendors announced partnerships for on-premises quantum deployments and quantum software vendors provided frictionless software offerings for nonquantum specialists. Finally, quantum hardware and software vendors announced the anticipated launch of new scientific accelerator platforms that will help with the integration of quantum, AI, and HPC.

“There has been much hype around quantum computing and when quantum computing will be able to deliver a quantum advantage, for which use cases, and when,” said Heather West, Ph.D., research manager, research manager within IDC’s Enterprise Infrastructure Practice. “Today’s quantum computing systems may only be suitable for small-scale experimentation, but advances continue to be made like a drumbeat over time. Organizations should not be deterred from investing in quantum initiatives now to be quantum ready in the future.”

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