Will Quantum Computing Be The Next Super Trend After AI?

Quantum computing, which uses principles of quantum mechanics, is predicted to be the next major technology supertrend. It follows artificial intelligence (AI), which has seen massive investment and significant technological advances in fields such as large language models, which have led to many services such as ChatGPT, Perolexity, and Sora.

Thanks to the quantum bit, or qubit, quantum computing has the potential to revolutionize fields like cryptography, optimization, and drug discovery. As AI’s computational demands grow, quantum computing could meet these needs, indicating a promising future for quantum technology advancements.

Could the excitement in AI continue to bleed over into Quantum? What happens when the fuel runs low for AI? Could Quantum Computing and Quantum technologies be the next super-trend that captures the world’s attention as the frontier of technological progress?

What is a Super Trend?

A super trend refers to a significant and often long-lasting shift in the behavior, preferences, technology, or economics that substantially influences various aspects of society, including markets, consumer behavior, and the global economy.

Unlike short-term trends or fads that might affect a small area of interest for a brief period, super trends have the potential to redefine industries, alter how we live and work, and drive innovation and policy changes on a global scale. These trends are usually driven by a combination of technological advancements, demographic shifts, environmental factors, and changing societal values.

The Rise of AI

The rise of AI has been transformative, with its applications ranging from autonomous vehicles to personalized recommendations on streaming platforms. However, AI’s computational demands are growing exponentially, and classical computing may soon struggle to keep up.

Quantum computing could provide the computational power needed to drive the next wave of AI advancements. For instance, quantum machine learning, a subfield combining quantum computing and machine learning, could solve complex problems faster and more accurately than classical machine learning algorithms.

Introducing Large Language Models (LLMs) like OpenAI’s GPT series represents a pivotal moment in artificial intelligence, especially in how AI understands and generates human language. The landscape before and after the emergence of LLMs showcases a dramatic shift in capabilities, applications, and the overall approach to AI-driven tasks.

Before the advent of LLMs, AI’s approach to language was primarily rule-based or relied on simpler machine learning models. These early systems were effective for specific, narrow tasks such as basic customer service inquiries or pattern recognition in structured data. However, they struggled with complex language understanding, context interpretation, and generating human-like text.

For instance, earlier models like simple neural networks or decision trees lacked the depth and adaptability to grasp the nuances of natural language, resulting in responses that often felt mechanical and were limited to the data they were explicitly trained on. The rule-based systems required extensive manual programming of language rules, making them rigid and unable to learn from new data autonomously.

Quantum Computing

Quantum computing is not just a theoretical concept; it is a reality actively pursued by tech giants such as IBM, Google, and Microsoft. These companies are investing heavily in quantum research to develop quantum computers that can outperform classical computers in specific tasks, a milestone known as quantum supremacy. The race for quantum supremacy is a crucial driver of the current research trends in quantum computing.

Significant advancements in quantum physics and computer science have fueled the growth of quantum computing. In the early stages, the focus was primarily on theoretical research, with scientists like Richard Feynman and David Deutsch laying the groundwork. However, the field has since transitioned into a more practical phase, with tech giants like IBM, Google, and Microsoft investing heavily in quantum research and development. These companies are building quantum computers and developing quantum algorithms and software, creating a robust ecosystem that supports the growth of this technology.

However development of quantum computing is not without challenges. Quantum computers are in their infancy, with only a handful of qubits operational. The most significant number of qubits typically available is around 1,000 (for a gate-based quantum computer). Companies are ready to increase the qubit count. Many are still ranging in the 100s, and we can soon see that there will be many players with thousands of qubits. Some companies, such as PsiQuantum, have been vocal and early about their quest to get to a million qubits. Perhaps the most important bell-weather is IBM, who have gotten to 1,121 qubits and are executing against their roadmap flawlessly.

Not all quantum companies are created equally, and a range of qubit numbers are currently being sported. William Gibson‘s “The future is already here – it’s just not very evenly distributed.“

One of the key predictions for the next decade is the achievement of ‘quantum supremacy’ or ‘quantum advantage.’ At this point, quantum computers can solve problems practically impossible for classical computers to handle. Google’s Sycamore processor has already claimed to reach this milestone in 2019, but the debate on the practicality and reproducibility of the results continues.

Over the next decade, we can expect more robust demonstrations of quantum advantage, with practical applications in areas such as optimization problems, quantum chemistry, and machine learning.

Quantum Bits – Qubits – The Driving Force of Quantum Computing

Quantum computers use quantum bits, or qubits, which can exist in multiple states at once, thanks to a property known as superposition. This lets them perform many calculations simultaneously, potentially solving complex problems faster than classical computers.

The qubit is the foundational unit of quantum information in quantum computing, analogous to the bit in classical computing. However, unlike a bit in one of two states (0 or 1), a qubit can exist in a superposition state, allowing it to represent both 0 and 1 simultaneously. This capability, along with entanglement—where the state of one qubit can depend on the state of another, no matter the distance between them—provides quantum computers with extraordinary properties.

There are different flavors of the qubit. All fundamentally provide a similar paradigm shift but use various underlying techniques to achieve this. Quantum computing has seen diverse approaches to realizing qubits, each with distinct physical mechanisms and technological challenges. Superconducting qubits, for instance, are prominently utilized by companies like IBM with its IBM Quantum Experience and Google, which announced quantum supremacy using this technology.

These qubits operate at cryogenic temperatures and are fabricated using processes similar to those in the semiconductor industry, enabling scalability. On the other hand, trapped ion qubits are championed by companies such as IonQ and Honeywell (now Quantinuum), which use electromagnetic fields to trap ions in a vacuum and lasers to manipulate their quantum states. This technology is noted for its high fidelity and long coherence times, although scaling up presents challenges.

Innovative approaches also include topological qubits, pursued by Microsoft through its Azure Quantum program, aiming to leverage the robustness of Majorana fermions to external disturbances for more stable quantum computations. Meanwhile, photonic qubits, which use particles of light to encode quantum information, are explored by companies like Xanadu with its photonic quantum computing platform, offering advantages in room temperature operation and potential for integration with existing telecommunications infrastructure.

Semiconductor qubits find their proponents in Intel and QuTech, utilizing the familiar silicon-based platforms to harness the spin states of electrons for quantum computing. This method promises compatibility with existing semiconductor manufacturing techniques. Each approach reflects a unique strategy to harness the principles of quantum mechanics, pushing the boundaries of computational possibilities and defining the frontier of quantum technology.

However, the road to a quantum future is not without challenges. Quantum computers are susceptible to environmental disturbances, and maintaining quantum coherence is a significant hurdle. Moreover, quantum programming requires a different approach than classical programming, necessitating a workforce’s skill set shift. Despite these challenges, the potential of quantum technology is immense, and its advancements could usher in a new era of technological innovation, powering the future beyond AI.

A Brief History of Quantum Computing

The history of quantum computing is a fascinating journey that began in the early decades of the 20th century, rooted in the foundational principles of quantum mechanics. However, it wasn’t until the 1980s that the concept of quantum computing began to take a more concrete form, driven by significant theoretical developments.

In 1981, at a conference at MIT, physicist Richard Feynman challenged the computing community with the idea that classical computers could not efficiently simulate quantum systems. He proposed that a new type of computer, based on the principles of quantum mechanics, could overcome this limitation. This visionary idea laid the groundwork for the field of quantum computing.

The 1980s saw the development of key algorithms that demonstrated the potential of quantum computing to solve certain problems more efficiently than classical computers. In 1985, David Deutsch at the University of Oxford described the quantum Turing machine, formalizing the concept of quantum computation. This theoretical framework was pivotal, showing how quantum computers could execute operations on superpositions of states, leading to parallel processing capabilities far beyond those of classical computers.

Later in the decade, in 1989, Deutsch proposed the Deutsch algorithm (related to the Deutsch-Josza Algorithm), which could determine the symmetry of a function in a single evaluation. This task would require two evaluations on a classical computer. This was the first explicit example of a quantum algorithm that could outperform its classical counterpart, illustrating the potential for quantum speed-up in computational tasks.

These early algorithms were foundational, setting the stage for further breakthroughs in the 1990s, including Shor’s algorithm for factoring large numbers and Grover’s algorithm for searching unsorted databases, which cemented the promise of quantum computing in solving complex computational problems more efficiently than classical methods.

The first significant breakthrough in quantum computing came in 1994 when Peter Shor, a mathematician at Bell Labs, developed an algorithm that could factor large numbers more efficiently than classical computers. This marked the beginning of a new era in computing, demonstrating the potential of quantum computers to solve problems that were previously thought to be unsolvable.

Leading the charge in quantum computing are tech giants such as IBM, Google, and Microsoft, each with their unique approach. IBM, for instance, has been a pioneer in this field, launching the IBM Q Experience in 2016, which allows users to run experiments on IBM’s quantum processor via the cloud. Conversely, Google has been focusing on developing quantum supremacy, a term that describes a quantum computer’s ability to solve problems that classical computers cannot. Microsoft, meanwhile, is investing in topological quantum computing, a method that is more stable and less prone to errors than other quantum computing models.

In 2019, Google announced that it had achieved “quantum supremacy” – a term used to describe a quantum computer’s ability to solve a problem that a classical computer cannot solve within a reasonable amount of time. This was a significant milestone in the timeline of quantum computing breakthroughs, indicating that we could be on the cusp of a new technological era.

Understanding Quantum Computing Applications

The key to any quantum computing future is the “killer application”. Or an application that only quantum computing can actually do. A killer app for the internet was email. What is it for quantum? Well, we don’t quite know. Sure, its has been shown the algorithms like Shor’s Algorithm can factorize numbers faster than conventional or classical computers, there are not powerful quantum devices yet. Of course, they could come later.

As time will no doubt surely attest to, we think its only a matter of time before quantum computers prove their “metal” and provide real tangible benefits over classical computers. How exactly remains to be seen, but there are myraid fronts that researchers are exploring, even if quantum computers are not “wall clock” faster, they could provide better quality results in machine learning for example.

One key application – whilst not strictly Quantum Computing, is Quantum Security. Or namely QKD. Anotjer is generating random numbers, which concbentional computers struggle with. But really what most are excited about are the applications that could be possible on the computer front.

Not Just Quantum Computing: Quantum Security

Quantum Key Distribution (QKD) is a groundbreaking approach to secure communication that leverages the principles of quantum mechanics to ensure the confidentiality of information. QKD enables two parties to generate a shared, secret random key, which can then be used to encrypt and decrypt messages. The security of QKD is fundamentally underpinned by the quantum mechanical properties of particles like photons, the basic units of light. Perhaps QKD is one of the killer applications of the Quantum technology arena so far.

The essence of quantum security, as exemplified by QKD, lies in the behavior of quantum particles, which cannot be measured or observed without disturbing their state. This principle is crucial for QKD, as it means that any attempt by an eavesdropper to intercept the key will inevitably alter the quantum states of the particles involved, thereby alerting the communicating parties to the presence of an intrusion.

One of the most celebrated protocols for QKD is the BB84 protocol, proposed by Charles Bennett and Gilles Brassard in 1984. This protocol uses the polarization states of photons to encode the key, with security guaranteed by the no-cloning theorem of quantum mechanics, which prohibits the creation of identical copies of an unknown quantum state.

Predictions for the Next Decade

As we move into the middle of the 20’s, experts predict significant advancements in quantum computing that could power the next era of technology beyond artificial intelligence (AI).

Another forecast for quantum computing in coming years is the development of error-correcting codes and fault-tolerant quantum computers. Quantum bits or ‘qubits,’ the fundamental units of quantum information, are highly susceptible to errors due to environmental noise.

Developing error-correcting codes is crucial to build reliable quantum computers. In the next decade, we can expect significant progress in this area, leading to the development of fault-tolerant quantum computers that can perform long computations without errors.

Fully error correcting computing quantum computers could be the future. Fully error-correcting quantum computers represent an advanced stage in quantum computing technology, where the system can detect and correct quantum errors autonomously. This capability is crucial for the practical and reliable use of quantum computers, given that qubits (quantum bits) are highly susceptible to errors due to decoherence and quantum noise. In classical computing, error correction is a well-established practice, essential for maintaining the integrity of data.

However, in quantum computing, error correction is significantly more challenging due to the quantum properties of superposition and entanglement. Despite these challenges, fully error-correcting quantum computers are a key goal for the industry, as they would enable longer computations by maintaining the qubits’ quantum state over time without degradation.

QuEra is a quantum computing company that emerged from research conducted at Harvard University and the Massachusetts Institute of Technology (MIT). QuEra specializes in developing quantum computers based on neutral atoms. Neutral atom quantum computing utilizes arrays of individual atoms suspended in vacuum and manipulated using lasers. This technology is distinguished by its scalability and the precise control it offers over qubit interactions, making it a promising approach for both quantum simulations and computations.

Surely its Logical: The Quest for Logical Qubits

Its worth taking a brief pause to state that numbers are not everything. Qubit counts might make eye cathing headlines. Whilst qubit counts provide a base metric for comparison, they are not the whole story. When qubit counts are measured, it is typically the physical number of qubits. But each of these qubits is rather noisy. That means that the eventually quantum circuit displays noise and that noise needs to be corrected. Not correctly that noise in a circuit eventually means that the circuit becomes rather useless.

Quantum Machine Learning

This isn’t just a fancy name for beefed up machine learning, QML is one of the hottest topics around that you may not have heard of. QML represents the intersaction of using quantum computers to learn from patterns in data just as machine learning has. Likely you’re interacting machine learning models on a daily basis without much thought. Often these models are seamlessly working in the background doing things such as predicting what adverts to show you or could even be deciding what your credit score is.

The usecases are numerous and varied, and the same is thought to be the same for QML or Quantum Machine Learning. Just as we taught classical machines to learn, researchers have used the vast Hilbert space available to them in the quantum world to provide some inherent advantages over classical compute. There are quantum machine learning algorithms that you can execute today on a quantum computers, whether that is a Quantum Support Vector Algorithm or a Quantum – Classical Neural Network. There are textbooks and QML is quite a trendy topic for researchers with conferences arranged globally and numerous papers published.

Quantum AI

One of the significant opportunities presented by quantum computing lies in its potential to solve complex problems that are currently beyond the reach of classical computers. Quantum computers leverage the principles of quantum mechanics to process information in a fundamentally different way, enabling them to perform calculations at speeds unattainable by traditional computers.

This could lead to breakthroughs in various fields, including cryptography, material science, and drug discovery, among others (Preskill, 2018). However, this immense computational power also poses a threat. Quantum computers could potentially crack encryption codes that protect sensitive information, posing significant security risks (Bennett & Shor, 1996).

Moreover, quantum computing could also enhance AI capabilities, opening up new avenues for machine learning and data analysis. Quantum machine learning, an emerging field, could potentially process vast amounts of data more efficiently than classical machine learning algorithms, leading to more accurate predictions and insights (Biamonte et al., 2017). However, the integration of quantum computing and AI also presents challenges. The development of quantum algorithms is a complex task, and the lack of skilled quantum programmers could hinder progress in this field (Farhi & Neven, 2018).

Quantum algorithms, such as the quantum support vector machine (QSVM), have already shown promise in classification tasks, a key component of machine learning.

Key Quantum Players

The impact of quantum computing on the technological landscape is profound, with the potential to revolutionize industries and redefine the boundaries of computational power. As we delve into the realm of quantum computing, it is essential to identify the key players and strategies that are shaping this innovative field.

In addition to these tech giants, several startups are also making significant strides in quantum computing. Companies like Rigetti Computing, D-Wave Systems, and IonQ are developing quantum computers and software with a focus on scalability and commercial applications. These companies are not only contributing to the advancement of quantum computing technology but are also shaping the strategies for its commercialization and application across various industries.

The strategies adopted by these key players revolve around collaboration, open-source platforms, and a focus on specific applications. Collaboration is a common theme, with many companies partnering with universities and research institutions to advance quantum research.

Open-source platforms, such as IBM’s Qiskit and Rigetti’s Forest, are also prevalent, allowing for a broader community of researchers and developers to contribute to the development of quantum computing. Lastly, a focus on specific applications, such as optimization problems and quantum chemistry, is helping to drive the development of quantum algorithms and software.

Beginnig of A Quantum Super Trend?

The quantum computing future outlook is filled with predictions and possibilities that could redefine our understanding of computation and data processing.

In the economic sphere, quantum computing could significantly enhance productivity and innovation. It has the potential to solve complex problems in seconds that would take classical computers thousands of years. This could lead to breakthroughs in various fields such as drug discovery, climate modeling, and financial modeling, thereby driving economic growth. A study by Boston Consulting Group predicts that by 2050, quantum computing could generate up to $850 billion in annual value.

However, it’s important to note that quantum computing is still in its nascent stages. Many technical challenges, such as error correction and qubit stability, need to be overcome before quantum computers become commercially viable. Nevertheless, the progress made so far in quantum computing is encouraging, and it’s clear that this technology holds immense potential for the future.

On the societal front, quantum computing could transform the way we live and work. It could revolutionize healthcare by enabling the development of personalized medicine and improving diagnostic accuracy.

In conclusion, quantum computing holds the promise of powering the next era of technology beyond AI. Its potential impact on society and the economy is profound, but it also presents challenges that need to be carefully managed. As we move towards this exciting future, it is crucial to foster a balanced approach that maximizes the benefits of quantum computing while mitigating its risks.