Trevor Lanting, Chief Development Officer D-Wave shares the distinct advantages of annealing and gate-model quantum computing for various industries, emphasizing their roles in optimization, materials science, and AI. In this interview he talks about the potential for quantum computing to alleviate the computing demands in AI and ML across multiple sectors.

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Please share your journey to becoming Chief Development Officer (CDO) at D-Wave and what inspired your passion for quantum computing.

I have a background in physics, and I have always been interested in technology development. I am trained as an experimental physicist and my graduate work involved building superconducting instrumentation for microwave astronomy.

Through that training, I realized my passion really centered on developing technology and building tools. When D-Wave was recruiting for an experimental physicist in 2008, I jumped at the chance to join the team. Over the last 15 years, I have been involved with many aspects of our technology development, contributing directly to our annealing quantum computing development, our performance research program, and helping lead our software and algorithms teams. I was involved with work that was instrumental in demonstrating quantum entanglement in the fabric of our annealing processors, a major step in validating our technology approach.

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Several months ago, I stepped into a leadership role helping direct our overall research and product development efforts across software and hardware systems. I am incredibly excited about quantum computing. We are building technology that harnesses quantum mechanics to produce fundamentally new computational tools. As the technology rapidly matures, we are seeing a growing set of use cases that span from the acceleration of scientific discovery to optimization of complex business processes, and emerging machine learning applications.

Can you talk about the primary differences between D-Wave’s annealing and gate-model quantum computers, and how do these technologies benefit industries like AI, logistics, and materials sciences?

Annealing and gate-model quantum computing are two of the leading approaches to building practical large-scale quantum computing technology. These approaches offer distinct and complementary advantages for different use cases and applications, and we are developing both technology platforms.  

Annealing systems are uniquely suited for solving optimization problems. These problems, like tour scheduling, resource scheduling, and cargo loading, occur across many industries, like supply-chain logistics and manufacturing, and solving these problems leads to more efficient operations and direct cost savings.

 For materials sciences, gate-model systems have the potential to simulate the behavior of novel molecules and interactions between these molecules, promising to accelerate material discovery and drug design.

 For AI, annealing quantum computing can enhance machine learning algorithms, particularly in feature selection, model optimization, and providing rich quantum distributions that can be directly harnessed in generative AI architectures.  

Annealing quantum computing does have several advantages over current gate model systems: annealing protocols do not require significant pre-processing overheads associated with many gate-based protocols; annealing processor controls are continuously applied making the processors more resilient to errors and noise; and annealing processors are scaling to enterprise-level problem sizes more quickly. These characteristics make annealing quantum computing ideal for addressing today’s real-world challenges across industries.

How do you see the integration of quantum computing with AI and machine learning evolving, and what challenges and opportunities do you foresee?

It’s becoming apparent that the broader AI industry is facing a severe computing crunch. The amount of compute and the related energy costs needed to keep up with an increasing set of use cases is rapidly escalating. The industry should recognize that quantum computing technology might offer real opportunities to allow the industry to meet the growing demand for larger, more performant, and more energy efficient AI and ML architectures and workloads.

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 At the same time, we are in the early part of exploring how best to harness the power of quantum computing for AI. There is currently work in development here at D-Wave on using quantum distributions for designing modern generative AI architectures. This is an emerging field that involves directly using quantum processing unit samples that are not easily generated by classical computers, all of which could potentially improve how generative AI models are built.

 Customers such as TRIUMF, a Canadian physics lab, Honda Innovation Lab and Tohoku University, are already exploring D-Wave technology to address a variety of AI/ML workloads including pre-training optimization and more accurate and efficient model training.

 D-Wave has introduced a hybrid-quantum approach to optimizing feature selection in AI/ML model training and prediction. This approach is designed to help improve models’ accuracy by employing quantum systems to select the most representative dataset characteristics. Our partnership with Zapata AI continues to explore how the combination of quantum computing and generative AI could accelerate the development of new pharmaceuticals.

What are your predictions for the future of quantum computing, particularly in scalability, practical applications, and mainstream adoption across industries?

 For us at D-Wave, the future of quantum computing is firmly anchored in its practical applications. We’re already witnessing real-world impact across various industries, solving problems to directly improve people’s daily lives. Examples include quantum-optimized routes for grocery deliveries and more efficient supply-chain management. Overall, I expect quantum solutions to impact business operations by improving efficiencies in supply chain management, financial modeling, and resource allocation.

 Scalability is an important factor: the underlying quantum computing systems need to be designed for scalability from the beginning, and this a key reason why we focused our initial technology development effort on superconducting quantum annealing systems. As quantum computers become more powerful, growing in qubit count and quality, their ability to tackle larger and more complex problems will also increase.

 I believe quantum computing will play a meaningful role in drug discovery, accelerating the development of new medications and materials, and will be more broadly adopted across industries that face optimization challenges.

How do you think AI advancements will influence the evolution of quantum computing hardware and software solutions?

 We’ve talked about AI advancements making things faster and more scalable, and of course, this will allow for new discoveries. AI tools could also make quantum computing more accessible by automating some of the complex processes involved in quantum computations, problem formulation, solver parameter selection, and adding more user-friendly interfaces. This aligns with our goal at D-Wave, which is to make quantum computing a practical tool for solving real-world problems across various industries.

Could you share your thoughts on where you see AI, machine learning, and other smart technologies heading beyond 2024?

 In the future, I think we will see quantum-enhanced AI models outperform purely classical AI in many domains. Like any new emerging general-purpose technology, if we can put quantum computing, AI, and machine learning technologies in the hands of a broad and diverse set of users as fast as possible, unexpected and powerful use cases will quickly emerge and we will see these technologies embedded into our daily lives. And as domain experts in a wide range of fields adopt these powerful tools, progress on drug design, materials innovation and simulation, business optimization, and scientific discovery will accelerate. 

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Seema Verma, EVP and GM, Oracle Health and Life Sciences, in this Q&A talks about strategic use of AI and technology to enhance healthcare efficiency, security, and patient care through innovations and collaborative partnerships and more about transforming healthcare by improving data interoperability.

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Hi Seema, welcome to our AiThority Interview series; walk us through your journey at Oracle and your learnings from being in the B2B SaaS industry

After spending four years as chief administrator of the Centers for Medicare & Medicaid Services (CMS), the largest payer in the world, I joined Oracle in April 2023 as senior vice president and GM of Oracle Life Sciences. In January 2024, I also became EVP and GM of Oracle Health, one of the largest EHR providers in the world, which marked a strategic shift for the company as we further strengthen the connection between these industries for the betterment of patients.

I’m about eight months into my new role, and there is still so much to be done as we continue to evolve our offerings, but I’m energized about the path forward. The opportunity to create a more effective, efficient, and secure healthcare system is immense – but achievable. What is unique about Oracle’s approach is that we are not just dipping our toe in the water. We have a breadth and depth of technology – the enterprise applications and cloud infrastructure, as well as the clinical trial applications and the EHR – that enables us to address the entirety of the industry’s challenges. We’re delivering leading technology and AI coupled with a long history of understanding how healthcare works to help health organizations worldwide reduce costs, increase efficiency, and improve patient care.

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Oracle is known for its autonomous database. How is this technology being utilized today within the health and life sciences sector to improve data security?

Oracle is the only healthcare technology provider that uses autonomous databases and operating systems to automatically patch and minimize any threat. At a time when cybercriminals are getting more sophisticated, this is game changing for the health and life sciences sector.

Given the impact that hacks can have on the delivery of care, cybersecurity should really be considered a subset of patient safety and receive similar levels of attention and investment.

The Oracle Autonomous Database is the first automated data management solution that helps eliminate cybersecurity’s biggest weakness – human error and delay. With our solution, healthcare organizations can take advantage of reliable security and compliance for sensitive data.

This includes leveraging AI and machine learning to transform operational reporting and get deeper insights into patients’ needs, helping enable quicker resolutions to critical health problems.

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How is Oracle leveraging AI to enhance interoperability and streamline care coordination among different healthcare providers?

Allowing patient data to flow with the patient through their healthcare journey, no matter where they are, is a priority for Oracle. To this end, we are making significant investments in interoperability and AI and doing so in a way that supports security and safety to create efficiency and cut costs in the healthcare system. With our complete suite for hospital automation, patient engagement systems, and everything in between, we are bringing all of these elements together: clinical, patient, enterprise, and data.

We’re embedding AI across all of these applications. This provides hospitals and life science companies with more intelligence and helps automate entire processes, enabling hospital systems to be more efficient, more secure, and more effective. Recent product innovations we’ve rolled out that are leveraging AI to help connect the healthcare ecosystem include:

• Oracle Health Data Intelligence, which stands at the forefront of the next generation of healthcare interoperability – capable of integrating data from over 2,000 sources and generating insights that are infused directly into the provider’s workflow to help close care gaps and improve quality measures.
• Oracle Clinical Digital Assistant, which takes advantage of generative AI to participate in patient appointments, capture patient-clinician interaction details, and draft structured clinical notes that doctors can quickly review and approve or edit. The offering dramatically reduces the amount of time clinicians spend interacting with the EHR and increases the amount of time they are focusing on patients. This is a game-changer that is helping to bring the joy back to practicing medicine for our customers.

Can you share some insights on the collaborative partnerships Oracle has formed to drive innovation in the healthcare sector?

We are committed to working with companies across the healthcare and life sciences industries to make healthcare more efficient, less expensive, and more effective. In some cases that means delivering new and deeply integrated APIs so customers can extract more value from our technology. For example, NetHealth (formerly known as Tissue Analytics), is focused on improving wound care documentation workflows. Its technology integrates advanced wound management and photo analysis directly within our EHR.

In other cases, we are enabling our partners to easily take advantage of Oracle Cloud Infrastructure (OCI) to deploy and scale their innovative healthcare offerings and services. For example, Imagene runs its AI inference on OCI, providing rapid AI-based molecular profiling from biopsy images to help transform cancer diagnosis and care.

We also join forces with like-minded organizations to expand our capabilities in accelerated computing and AI, so our customers and partners have access to the solutions they need to help solve the industry’s most complex and critical healthcare challenges. This includes enhancing our collaboration with Cohere to develop powerful, generative AI services for organizations worldwide. With Oracle’s comprehensive portfolio of cloud applications, data management expertise, and best-in-class AI infrastructure, combined with Cohere’s state-of-the-art large language models, we are deploying new models for healthcare and embedding generative AI throughout our healthcare-specific applications. We also team with NVIDIA to power healthcare solutions with accelerated computing in the cloud to help enable breakthroughs in research and advance the future of medicine.

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What are the key areas where AI can significantly impact drug pricing and pharmaceutical research, and how is Oracle’s product team contributing to these areas with new enhancements?

We expect AI to play a huge role in the life sciences industry. Obviously, the ability to recognize patterns and analyze vast amounts of data quickly is critical pharmaceutical research. The advances in AI are going to make it easier to pinpoint potential therapies faster and with greater accuracy. In turn, this will help companies to bring products to market more quickly and cost effectively. We are really just scratching the surface in terms of the impact AI will have on this industry. For example, we recently announced new capabilities in our AI-supported Oracle Argus and Safety One Intake solutions to help life science organizations meet evolving regulatory requirements and the rising volume of adverse event case workloads. And we are working to harness the full potential of AI to break down the silos between clinical care and research.

With your extensive experience in healthcare policy, how does Oracle ensure its AI solutions comply with stringent healthcare regulations and standards?

Policy experts around the world are grappling with how to regulate AI. It’s our job to work with our customers and comply with the regulations, which will vary by industry. With personal health information, there will be more conversations between medical providers and governments about how AI is applied. The requirements will likely be more extensive for healthcare than for something like menu management in the restaurant business. We will adjust the technology to meet those requirements and to make the technology easy to apply, even in highly regulated industries.

Before we conclude, can you talk about the future initiatives or advancements we can expect from Oracle in the healthcare and life sciences sector and how these efforts will continue to shape the landscape of healthcare technology?

So much of our work at Oracle is focused on putting patients first and supporting providers and caregivers by reducing the burden of tedious, manual administrative tasks that often overwhelm them and divert their time away from patients. This focus will continue to shape the future direction of our innovations and solutions across Oracle Health and Life Sciences.

The challenges facing our industry are vast and complex, but it’s also an incredibly exciting time. It’s a moment ripe for radical transformation that can propel us toward a genuinely value-based and more innovative healthcare system – the kind of healthcare system that we all want for ourselves, our families, and our communities. One where providers are measured based on the clinical outcomes they achieve and the cost of those results. Where teams of providers are encouraged to keep patients well rather than waiting for them to become sick. Where we connect clinical research to clinical care to get solutions to expedite solutions where none existed.

We have the technology to make this happen. We can create a healthcare system where we reduce the cost of administration, and those dollars can be reinvested into delivering cutting-edge treatments, improving care quality and accessibility and the health of the population. Seeing the work that Oracle is doing and the innovation that is coming from our team, I’ve never been more optimistic about the industry.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

Kunal Purohit, President – Next Gen Services, Tech Mahindra discusses several key initiatives and innovations at Tech Mahindra’s Makers Lab. He talks about Project Indus, an AI-driven enterprise platform that optimizes operational efficiency and focuses on the Hindi language and its dialects through a user-contribution portal in the following Q&A:

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Tech Mahindra’s Makers Lab is known for fostering innovation. Can you talk about some of the most impactful and disruptive solutions from Makers Lab recently?

At Tech Mahindra’s Makers Lab, our mission is to drive purpose-driven and human-centered innovation. One of our most impactful recent initiatives is Project Indus. Born out of a desire to revolutionize the future of work, Project Indus leverages the power of AI to create a seamless and intelligent enterprise platform. This platform optimizes operational efficiency and enhances decision-making processes by providing real-time insights and predictive analytics. Project Indus utilizes an innovative ‘GenAI in a box’ framework and simplifies the deployment of advanced AI models, making it easier for enterprises to integrate and scale AI applications. The initial phase of the Indus LLM targets the Hindi language and its 37+ dialects. The project includes a portal called projectindus.in, where users can contribute linguistic data.

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Our Makers Lab played a pivotal role in the conception and development of Project Indus. By fostering a collaborative environment, we combined the ingenuity of our diverse talent pool with cutting-edge technologies. The project aims to address real-world challenges faced by businesses today, offering scalable and adaptable solutions that drive growth and sustainability. Through Project Indus, Makers Lab exemplifies how innovation can be both disruptive and deeply beneficial, paving the way for smarter, more efficient enterprises.

What is your perspective on the impact of Generative AI (GenAI) on the workplace, particularly in terms of operational effectiveness and employee productivity?

GenAI is fundamentally transforming the workplace landscape, significantly enhancing operational effectiveness and employee productivity. At Tech Mahindra, we perceive GenAI as a catalyst for creating smarter, more efficient processes that drive innovation and deliver value at unprecedented speeds. We’ve introduced GenAI-driven pair programming to support our associates throughout the software development life cycle and have deployed GenAI-empowered co-pilots for boosting personal productivity.

By automating routine tasks, GenAI allows employees to focus on more strategic, creative endeavors, thereby boosting productivity and job satisfaction.

Our approach is holistic, focusing on empowering our workforce with advanced AI tools to foster sustainable growth and innovation. In an ever-evolving market, Tech Mahindra remains dedicated to creating a dynamic, agile workplace where technology and human ingenuity converge to deliver superior outcomes.

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What major ethical challenges do you foresee with integrating AI and quantum computing in the industry, and how is Tech Mahindra addressing them?

The integration of AI and quantum computing promises unparalleled advancements but also presents certain ethical challenges. One major concern is data privacy. Quantum computing’s immense processing power could potentially break current encryption methods, making sensitive data vulnerable. Additionally, the bias in AI algorithms can be magnified by the capabilities of quantum computing, leading to unintended and possibly discriminatory outcomes. At Tech Mahindra, we are proactively addressing these challenges through our Makers Lab initiatives. We are pioneering the development of quantum-safe cryptography to safeguard data in a post-quantum world. Moreover, our AI ethics framework emphasizes transparency, accountability, and fairness.

We are investing in interdisciplinary teams that include ethicists, technologists, and legal experts to ensure our innovations are aligned with ethical standards. By fostering a culture of ethical foresight and continuous learning, Tech Mahindra aims to lead responsibly in this transformative era, ensuring technology serves humanity’s best interests.

What significant AI tools and innovations has Makers Lab developed over the past few years?

At Makers Lab, our mission is to foster innovation by bridging the gap between imagination and reality. Over the past few years, we have harnessed the power of AI to create tools that push the boundaries of technology and deliver real-world impact. One of our standout innovations is the BHAML (Bharat Markup Language) solution that enables coding in native languages. Another remarkable creation is Enterprise Intelligence I/O (Entellio), a futuristic enterprise-grade on-premises chatbot powered by generative and discriminative AI. Some other innovations include Atmanirbhar Krishi, a super app for farmers, providing valuable agriculture related consolidated, curated information, and Panchang Intelligence, an ancient Indian almanac-based rainfall prediction solution.

Our commitment to quantum computing has also been recognized, with Avasant considering us a leading service provider in this cutting-edge field. Furthermore, our collaborative R&D efforts have earned us a place as a case study by the World Economic Forum, showcasing the impact of our innovative solutions. Our dedication to innovation has been acknowledged globally, with accolades such as the ISG Digital Case Study Award for Banking (UBI) in Metaverse 2023, Most Innovative Company 2021 and the Most Innovative Leader by the World Innovation Congress. Additionally, our support for start-ups was honored with the MindtheGap award for mentoring. At Makers Lab, we continue to drive technological advancements, making significant strides in the AI landscape.

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What emerging trends in AI and computing are you most excited about, and how is Makers Lab positioning itself to capitalize on them?

At Makers Lab, we are on the cusp of a revolution in AI and computing, eagerly embracing trends like GenAI, Quantum Computing, and Neuromorphic Engineering. The ability of GenAI to create content that mimics human creativity is reshaping industries from entertainment to healthcare. Quantum computing promises to solve problems beyond the reach of classical computers, potentially transforming everything from cryptography to complex system simulations. Neuromorphic engineering, with its brain-inspired architectures, offers a leap in efficiency and capability for AI systems.

Makers Lab is strategically positioned at the forefront of these innovations. Our multidisciplinary teams are developing quantum algorithms to accelerate machine learning, exploring the potential of neuromorphic chips for more efficient AI, and creating generative AI models that push the boundaries of creativity. By fostering a collaborative ecosystem, we are turning these emerging trends into practical solutions, ensuring that Tech Mahindra remains a leader in the next wave of technological advancement.

How do you ensure continuous learning and development within your team at Makers Lab?

We foster a culture of continuous learning by integrating experiential learning with a collaborative spirit. Our team engages in hands-on projects, exploring emerging technologies like AI, quantum computing, and blockchain. Regular knowledge-sharing sessions ensure that our learning ecosystem remains vibrant. For instance, we recently collaborated with a leading university on quantum algorithms, enabling our team to learn from top-tier researchers. In celebration of World Quantum Day 2024, Tech Mahindra and IQM Quantum Computers partnered to raise awareness, demonstrate, and promote the transformative power, and increase an understanding of quantum science and technology.

By encouraging curiosity and innovation, we stay ahead of technological trends and empower our team to drive groundbreaking solutions. This dynamic approach to learning transforms challenges into opportunities, fueling our mission to create a future-ready workforce.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]