The IBM Institute for Business Value (IBV) has published a beautiful book, good to read and also a substantial addition to any executive office: the fourth edition of The Quantum Decade. The 168-page tome written by over 70 professionals in every industry, clearly lays out the appropriate problems, the approaches to solutions, and the amazing technology being invented as we speak. With dozens of uses cases, and in-depth portrayals, this book is a must-read for every CEO and CTO. Here’s a summary of select sections I found particularly interesting, and a few use cases from the book.
Quantum Thinking
The IBV did a CEO study in 2021 that revealed that 89% of over 3,000 chief executives surveyed did not consider quantum computing as a key technology for delivering business results in the next two to three years. While this lack of recognition may be understandable in the short term, given quantum computing’s disruptive potential in the coming decade, CEOs need to start mobilizing resources to understand and engage with quantum technology now. IBV research also finds that in 2023, organizations invested 7% of their R&D budget in quantum computing, up 29% from 2021. By 2025, this is expected to further increase by another 25%.
Ignoring quantum computing could pose significant risks, the authors assert, with consequences potentially greater than missing out on the opportunity presented by artificial intelligence a decade ago. Phase 1 of the quantum computing playbook involves acknowledging that the computing landscape is undergoing a fundamental shift. This shift from analytics to discovery of forward-looking models that can run on Quantum opens up possibilities for uncovering solutions that were previously impossible.
Phase 2 involves asking important questions: How might quantum computing disrupt and reshape your business model? How could it enhance your existing AI and classical computing workflows? What could be the “killer app” for quantum computing in your industry? How can your organization deepen its quantum computing capabilities, either internally or through partnerships with ecosystems? This is the time to experiment, iterate with scenario planning, and cultivate talent proficient in quantum computing to educate internal stakeholders and leverage deep tech resources.
IBM says it is important to note that quantum computing doesn’t replace classical computing. Instead, quantum forms a progressive partnership with classical computing and AI, where the three work together iteratively, becoming more powerful as a collective than they are individually. In the hardware configuration above, each Quantum chassis is surrounded by classical computers, and the black rows are likely inference processing servers. So one needs to think about how to factor the solution to take advantage of these closely-knit but disparate systems.
Phase 3, known as Quantum Advantage, marks a significant milestone where quantum computing demonstrates its ability to perform specific tasks more efficiently, cost-effectively, or with better quality than classical computers. Today, IBM’s quantum systems deliver utility-scale performance: the point at which quantum computers can now serve as scientific tools to explore new classes of problems beyond brute-force, classical simulation of quantum mechanics. Quantum utility is an important step toward “advantage,” when the combination of quantum computers with classical systems enables significantly better performance than classical systems alone. As advancements in hardware, software, and algorithms in quantum computing converge, they enable substantial performance improvements over classical computing, unlocking new opportunities for competitive advantage across industries
Use Cases
However, achieving business value from quantum computing requires prioritizing the right use cases—those with the potential to truly transform an organization or an entire industry. Identifying and focusing on these strategic use cases is crucial for realizing the benefits of quantum technology. Here are a few examples that IBM articulates in the book.
Exxon Mobile and the Global Supply Chain
ExxonMobil is exploring the potential of quantum computing to optimize global shipping routes, a crucial component of international trade that relies heavily on maritime transport. With around 90% of the world’s trade carried by sea, involving over 50,000 ships and potentially 20,000 containers per ship, optimizing these routes is a complex challenge beyond the capabilities of classical computers. In partnership with IBM, ExxonMobil is leveraging the IBM Quantum Network, which it joined in 2019 as the first energy company, to develop methods for mapping the global routing of merchant ships to quantum computers.
The core advantage of quantum computing in this context lies in its ability to minimize incorrect solutions and enhance correct ones, making it particularly suited for complex optimization problems. Utilizing the Qiskit quantum optimization module, ExxonMobil has tested various quantum algorithms to find the most effective ones for this task. They found that heuristic quantum algorithms and the Variational Quantum Eigensolver (VQE)-based optimization showed promise, particularly when the right ansatz (a physics term for an educated guess) is chosen.
This exploration into quantum computing for maritime shipping optimization not only has the potential to significantly impact the logistics and transportation sectors but also demonstrates broader applications in other industries facing similar optimization challenges, such as goods delivery, ride-sharing services, and urban waste management.
The University of California and Machine Learning
Researchers from IBM Quantum and the University of California, Berkeley have developed a breakthrough algorithm in quantum machine learning, demonstrating a theoretical Quantum Advantage. Traditional quantum machine learning algorithms often required quantum states of data, but this new approach works with classical data, making it more applicable to real-world scenarios.
The team focused on supervised machine learning, where they utilized quantum circuits to map classical data into a higher dimensional space—a task naturally suited for quantum computing due to the high-dimensional nature of multiple qubit states. They then estimated a quantum kernel, a measure of similarity between data points, which was used within a classical support vector machine to effectively separate the data.
In late 2020, the researchers provided solid proof that their quantum feature map circuit outperforms all possible binary classical classifiers when only classical data is available. This advancement opens up new possibilities for quantum computing in various applications, such as forecasting, predicting properties from data, or conducting risk analysis, marking a significant step forward in the field of quantum machine learning.
E.ON and Machine Learning
E.ON, a major energy operator in Europe, is leveraging quantum computing to enhance risk management and achieve its emission reduction goals. With a vast customer base and a significant increase in renewable assets expected by 2030, the company faces the challenge of managing weather-related risks and ensuring affordable energy costs. Collaborating with IBM, E.ON has implemented quantum computing strategies to conduct complex Monte Carlo simulations across various factors like locations, contracts, and weather conditions.
Key quantum computing applications include:
- Using quantum nonlinear transformations for calculating energy contract gross margins via quantum Taylor expansions.
- Performing risk analysis with quantum amplitude estimation to improve dynamic circuit leveraging.
- Integrating quadratic speed-ups in classical Monte Carlo methods to optimize hardware resources.
These strategies have enabled real-time planning, finer risk diversification, and more frequent portfolio risk reassessments, thus aiding in the renegotiation of hedging contracts. E.ON views quantum computing as a pivotal technology for advancing machine learning, risk analysis, accelerated Monte Carlo techniques, and combinatorial optimization for logistics and scheduling, marking a significant shift in managing energy-related challenges.
Wells Fargo and Financial Trading
Wells Fargo is actively exploring the potential of quantum computing for practical applications in the financial sector, partnering with IBM within the IBM Quantum Network. This collaboration grants Wells Fargo access to IBM’s quantum computers via the cloud, allowing for pioneering work in quantum computing use cases, including sampling, optimization, and machine learning, aimed at deriving valuable results from quantum technologies.
A notable area of investigation between Wells Fargo and IBM is sequence modeling, particularly for predicting mid-price movements in financial markets. This involves analyzing the Limit Order Book, which records ask-and-bid orders on exchanges, and focuses on the mid-price—the average between the lowest ask and the highest bid prices at any moment.
Wells Fargo has explored using quantum hidden Markov models (QHMMs) for stochastic generation, a quantum approach to sequence modeling. QHMMs aim to generate sequences of likely symbols (e.g., representing price increases or decreases) from a given start state, similar to how large language models generate text. This quantum approach has shown to be more efficient than its classical counterpart, hidden Markov models (HMMs), offering new ways to enhance artificial intelligence technology in finance through the more efficient definition of stochastic process languages.
JSR and Chip Manufacturing
IBM and JSR are exploring how quantum computing could shape the future of computer chip manufacturing. Gordon Moore famously predicted in 1965 that the number of transistors on a computer chip would double approximately every two years, a forecast that has held true for decades, known as “Moore’s Law.” This progress has been largely enabled by innovations in semiconductor manufacturing, notably the development of a photoresist-based method by IBM in the 1980s. This technique, which uses a light-sensitive material to print transistors on chips, became widespread, with companies like JSR Corporation becoming leading producers.
The continuous miniaturization and performance improvement of chips are challenged by the costs and complexities of designing new photoresist molecules, a task for which modern supercomputers struggle due to the difficulty of simulating quantum-scale phenomena. Quantum computing, which operates on the principles of quantum mechanics, offers a potential solution by efficiently simulating molecular systems, including those comprising photoresist materials.
In a collaborative effort, IBM and JSR Corporation have started to explore the application of quantum computing in this field. A 2022 study demonstrated the use of IBM Quantum hardware to simulate small molecules akin to parts of a photoresist. This research represents a step toward utilizing quantum computing for developing new materials, potentially ensuring that Moore’s Law can continue to apply well into the future by enabling further advancements in semiconductor technology.
Conclusions
As you can see, the new edition of IBM’s Quantum Decade is a fabulous resource that should start more conversations and exploration in board rooms around the world. And thats exactly what IBM intended; by collaborating with early thinkers, we can jump start the Quantum Journey and accelerate the time to real-world solutions.
