Quantum Machine Learning–the Next Evolution for AI-ML

By Vishal Deshpande, Chief Data Analytics Officer

As more organizations realize the potential of machine learning and artificial intelligence, the ability to harness these powerful resources is quickly becoming essential for success. One way this can be achieved is through quantum computing. By leveraging entangled qubits, quantum computing can enable dramatic advances in machine learning models that are faster and more accurate than ever before. Quantum computing offers numerous benefits in performing machine learning tasks compared to traditional methods, including overcoming the limitations of classical computing and providing powerful mathematical capabilities. With its enhanced power and precision in modeling complex patterns, quantum computers can provide businesses with an unprecedented level of AI-driven insights and predictive analytics. In this blog post, we will discuss how quantum computing works and its use in advancing machine learning capabilities for organizations looking to get ahead of the curve with the latest advancements in AI. We’ll also explore some practical applications so you can have an idea of what is possible when using quantum technology to enhance your ML operations.

What is Quantum Computing and Machine Learning

Quantum computing and machine learning are two of the most exciting emerging technologies in recent years. Quantum computing, which harnesses the power of quantum physics to perform computations, is providing an unprecedented level of accuracy and scalability. Similarly, machine learning allows for intelligent automation and deep learning capabilities by leveraging large volumes of data. Imagine what the combination can do to the applications built on them?

The potential for massive scalability creates a new opportunity for deep learning, allowing machine learning algorithms to solve complex problems with greater efficiency and accuracy than traditional computing ever could. This potential for quantum machine learning is just beginning to be explored, but already it has shown excellent promise in areas such as machine vision, natural language processing and optimization. This combination of sophisticated machine learning techniques and quantum computing's power provides us with exciting possibilities for solving some of the world’s hardest problems.

Together, these two technologies are revolutionizing the way modern computing works and can provide insights into problems that existing strategies cannot. Whether it’s drawing inferences from molecular structures or predicting stock market fluctuations, quantum computing and machine learning have the potential to take our understanding of complex systems to the next level.

Benefits of Using Quantum Computing in Machine Learning

Quantum Computing has the potential to revolutionize Machine Learning, allowing natural language processing, predictive analytics, and deep learning tasks to be completed with greater accuracy than traditional computing methods. Quantum computing offers unparalleled speed compared to traditional computers, making it invaluable for faster threat detection and for more accurate predictive analytics. This opens up a multitude of opportunities within Machine Learning to create more powerful models to understand and interpret complex data sets, as well as give rise to new capabilities such as enhanced threat detection and predictive analytics.

In addition, quantum computing makes it possible for computers to store and analyze larger amounts of data, leading to faster and more accurate analysis. It expands existing machine learning techniques by providing access to further layers of information. The possibilities are endless as far as what quantum computing can bring to the world of Machine Learning.

Challenges of Implementing Quantum Computing in Organizations

Utilizing quantum computing for machine learning presents many potential benefits to organizations. However, implementing this technology comes with its own series of challenges. For instance, the cost of research and development, as well as resources to maintain it is expensive, making it cost prohibitive for some companies. In addition to cost considerations, complexity is another major hurdle for many organizations hoping to implement quantum computing. Amassing the knowledge base and skill sets required to make use of this technology effectively is also very demanding in terms of resources and personnel.

Despite these obstacles, rigorous research and development are continuing because of the vast potential offered by quantum computing in machine learning applications. With the right plan, organizations can make quantum computing a reality for their operations, so they can take advantage of all the potential benefits that come with using this powerful technology. Today, all major cloud providers like AWS, GCP, Azure and IBM provide options to ease quantum computing research and development. These include quantum computing resources like IonQ quantum computers, simulators, libraries, frameworks and SDKs like Qiskit, CirQ, TensorFlow Quantum, Q#, PennyLane, QuTip, Amazon Bracket, Azure Quantum to name a few.

Types of Algorithms Used in Quantum Computing for Machine Learning

Quantum algorithms powering Machine Learning represent a unique opportunity to unlock the potential of this technology. Using algorithms specifically designed for quantum systems enables users to solve a wide range of problems and is fundamental for many of the application scenarios which are being explored. Examples include algorithms such as variational algorithms, Grover’s algorithms, quantum linear system algorithms, algorithms for unsupervised feature extraction, and algorithms related to supervised learning and classification. While classical algorithms are limited by their time and space complexity, quantum algorithms can create new opportunities to tackle complex optimization and machine learning problems.

There exist numerous libraries and tool kits such as Qiskit, Qutip, CirQ, PennyLane and TensorFlow Quantum that make it easier for developers to apply these algorithms in their projects. These libraries often provide pre-existing examples adapted from promising research so that developers don't need to reinvent the wheel if they don't want to. Furthermore, Software Development Kits (SDKs) like AWS Bracket and Azure Quantum enable developers to more quickly develop new code which uses quantum technologies as well as provide cloud resources when immediate access to actual hardware is needed. Algorithms like density matrix embedding, variational algorithms, adiabatic algorithms and more can be implemented using these quantum computing libraries, kits and SDKs. These algorithms are an effective way for organizations to bridge the gap between classical computing and machine learning algorithm approaches.

Due to the combination of comprehensive algorithms, user friendly libraries and platforms, along with accessible cloud resources, it is clear that Quantum Computing provides a unique set of advantages for Machine Learning applications.

Real-world Applications of Quantum Computing for Machine Learning

Quantum Computing is the next phase of evolution in the field of IT with practical applications ranging from artificial intelligence to cyber security to pharmaceutical research to finance modeling and many others. It has the potential to enhance machine learning model training and analysis, natural language processing, drug development and traffic control and optimization to name a few. Companies ranging from major IT organizations like IBM, Microsoft, Google and automotive companies like Volkswagen and Daimler Truck AG to large financial institutes like JPMorgan Chase are investing heavily in Quantum Computing.

Training Artificial Intelligence

Quantum computing shows promise in speeding up training for artificial intelligence. Quantum computing can simplify the task of analyzing millions or even billions of data points with accuracy and speed. In 2020, Google, in collaboration with Volkswagen and the University of Waterloo, launched TensorFlow Quantum to accelerate development in quantum computing. TensorFlow Quantum is an open-source library used for prototyping quantum machine learning models. Quantum machine learning explores methodological and structural similarities between specific learning and physical systems, specifically neural networks.

Cyber Security

Quantum computing is predicted to break the encryption algorithms used today to protect infrastructure and sensitive data on the internet. Experiments estimate that a quantum computer with 20 million qubits is capable of breaking such encryption in less than 8 hours.

On the flip side, quantum computing can be used to build much more secure encryption systems. Companies like Google and Microsoft are working on quantum-safe encryption algorithms. These quantum-safe algorithms are expected to assist with securing banking transactions, military communication, medical records, etc.

There are already quantum-ready encryption algorithms that rely on problems other than integer factorization such as lattice based crypto systems or McEliece crypto system. Quantum cryptography is likely to provide quantum-ready encryption algorithms as well.

Finance

Quantum computing has usage in predicting financial markets, building risk analysis and risk classification models, financial portfolio management, and fraud detection among others.

KPMG ran a portfolio management benchmarking pilot in 2021 and found that quantum computing performed better than any other benchmarking methods for near-term investments.

In 2022, PayPal has partnered with IBM to use quantum computing in fraud detection.

Other Applications

Quantum computing has applications in many other fields due to its ability to work efficiently with probabilities and distributions. Some of these are related to optimization problem like the "traveling salesman" problem that aims to find the shortest possible route between multiple cities, hitting each city once and returning to the starting point. Quantum computing shows promise in solving this much faster than classical computing. Examples of research in optimization using Quantum Computing include:

• Automotive Industry - Companies like Volkswagen, Daimler Truck AG and Toyota are exploring the use of quantum computing for optimizing fleet management, traffic control and route optimization.
• Insurance Industry – Anthem is planning to use IBM’s quantum computing solution for data intensive tasks such as identifying health anomalies.
• Logistics and Supply Chain Optimization - companies like Coca Cola Japan has started large-scale pilots exploring the use of quantum computing for supply chain and inventory optimization
• Energy - The U.S. Department of Energy (DOE) has two labs established specifically for integrating quantum computing into grid optimization.
• IBM uses quantum computing for precise weather forecasting, currently serving almost all consumer tech providers like Apple, Amazon, Google and Facebook.

Quantum computing is an emerging technology offering exciting possibilities for machine learning and artificial intelligence applications. All major market researchers predict the field of quantum computing to grow at CAGR rate of 35%+ to reach a market capital of over 125B USD. This is primarily driven by the promise of quantum computing to speed up classical computing operations for AI-ML, optimization, and data analytics and modeling along with its ability to store and process volumes of data at speeds exceeding classical computation due to its ability to store more information in its qubits as opposed to the traditional binary bits used in classical computation.

By leveraging entangled qubits, quantum computers can perform tasks with unprecedented speed and accuracy compared to traditional computer models. This opens a myriad of opportunities in deep learning, natural language processing, predictive analytics and more. While implementing quantum computing solutions presents its own challenges such as cost and complexity, taking the right steps will ultimately provide organizations with powerful insights into complex data sets and enable them to unlock new levels of potential in AI-driven operations.

For details and further discussions on the use of AI-ML and quantum computing, feel free to reach out to the Unissant Advanced Technology group (ATG) that spearheads our innovation center where we are currently researching quantum computing for traditional AI-ML modeling using standard cloud services such as in Azure, AWS or GCP.