Quantum computing and artificial intelligence are both transformational technologies, and artificial intelligence is likely to require quantum computing to achieve significant progress.
Although artificial intelligence produces functional applications on classical computers, it is limited by their computational capabilities. Quantum computing can provide a computational boost to artificial intelligence, enabling it to tackle more complex problems and AGI.
What is quantum AI?
Quantum AI is the use of quantum computing to compute machine learning algorithms. Thanks to the computational advantages of quantum computing, quantum AI can achieve results that are not possible with classical computers.
What is quantum computing?
Quantum mechanics is a universal model based on principles that differ from those observed in daily life. A quantum model of data is needed to process data with quantum computing. Hybrid quantum-classical models are also necessary for quantum computing, including for error correction and the proper functioning of the quantum computer.
- Quantum data: Quantum data is data packets stored in qubits for computation. However, observing and storing quantum data is challenging because of the features that make it valuable, which are superposition and entanglement. In addition, quantum data is noisy; it is necessary to apply machine learning at the stage of analyzing and interpreting these data correctly.
- Hybrid quantum-classical models: It is highly possible to obtain meaningless data only when using quantum processors to generate quantum data. For this reason, a hybrid model emerges, powered by fast data-processing mechanisms such as CPUs and GPUs, which are widely used in classical computers.
- Quantum algorithms: An algorithm is a sequence of steps that leads to the solution of a problem. To execute these steps on a device, one must use instruction sets the device supports. Quantum computing introduces instruction sets based on a fundamentally different execution model compared with classical computing. The aim of quantum algorithms is to leverage quantum effects such as superposition and entanglement to obtain solutions faster.
For more, feel free to read our detailed article about quantum computing.
Why is it important?
Although AI has made rapid progress over the past decade, it has not yet overcome technological limitations. With the unique features of quantum computing, obstacles to achieving AGI (Artificial General Intelligence) can be eliminated. Quantum computing can be used for rapid training of machine learning models and for creating optimized algorithms.
An optimized, stable AI enabled by quantum computing can complete years of analysis in a short time, advancing technology. Neuromorphic cognitive models, adaptive machine learning, and reasoning under uncertainty are among the fundamental challenges of today’s AI. Quantum AI is one of the most likely solutions for next-generation AI.
Breakthroughs in Quantum AI
Quantum Machine Learning Nears Practicality
Researchers at CSIRO and the University of Melbourne discovered that quantum machine learning doesn’t require full error correction. Partial error correction can be used in quantum applications. This approach significantly reduces hardware requirements, enabling quantum machine learning to power real-world applications in the near future rather than decades from now.1
IonQ Achieves Practical Quantum Advantage for AI
IonQ and Ansys ran a medical device simulation on IonQ’s 36-qubit computer that outperformed classical high-performance computing by 12%.2
Quantinuum + NVIDIA Partnership for Generative Quantum AI
Quantinuum partnered with NVIDIA to accelerate the combination of quantum computing and generative AI via NVQLink. Early testers include Amgen (exploring hybrid quantum-machine learning for biologics) and BMW (fuel cell research).3
How does quantum AI work?
Google’s TensorFlow Quantum (TFQ), an open-source library for quantum machine learning, is an example of a suite of tools that combines quantum modeling and machine learning techniques. The aim of TFQ is to provide the necessary tools to control and model natural or artificial quantum systems.
Here’s how it works:
- Convert quantum data to a quantum dataset: Quantum data can be represented as a multi-dimensional array of numbers, called quantum tensors. TensorFlow processes these tensors in order to create a dataset for further use.
- Choose quantum neural network models: Based on the knowledge of the quantum data structure, quantum neural network models are selected. The aim is to perform quantum processing in order to extract information hidden in an entangled state.
- Sample or Average: Measurement of quantum states extracts classical information in the form of samples from the classical distribution. The values are obtained directly from the quantum state. TFQ provides methods for averaging across several runs that involve steps (1) and (2).
- Evaluate a classical neural network model – Since quantum data is now converted to classical data, deep learning techniques are used to learn correlations in the data.
The other steps of evaluating the cost function, gradients, and updating parameters are classical steps of deep learning. These steps make sure that an effective model is created for unsupervised tasks.
What are the possibilities of applying quantum computing in AI?
Researchers’ near-term realistic aim for quantum AI is to develop quantum algorithms that outperform classical algorithms and implement them.
Quantum algorithms for learning
Development of quantum algorithms for quantum generalizations of classical learning models. It can provide possible speed-ups or other improvements in the deep learning training process. The contribution of quantum computing to classical machine learning can be achieved by quickly providing the optimal weight set for artificial neural networks.
Quantum algorithms for decision problems
Classical decision problems are formulated as decision trees. A method for reaching the set of solutions is to create branches from certain points. However, when each problem is too complex to be solved by repeatedly dividing it in half, the efficiency of this method decreases. Quantum algorithms based on Hamiltonian time evolution can solve problems represented by a number of decision trees faster than random walks.
Quantum search
Most search algorithms are designed for classical computing. Classical computing outperforms humans in search problems. On the other hand, Lov Grover provided his Grover algorithm and stated that quantum computers can solve this problem even faster than classical computers. AI powered by quantum computing can be promising for near-term applications such as encryption.
Quantum game theory
Classical game theory is a modeling approach widely used in AI applications. The extension of this theory to the quantum field is the quantum game theory. It can be a promising tool for overcoming critical problems in quantum communication and for implementing quantum artificial intelligence.
What are the critical milestones for quantum AI?
Although quantum AI is an immature technology, there are improvements in quantum computing that increase the potential of quantum AI. However, the quantum AI industry needs critical milestones in order to become a more mature technology. These milestones can be summarized as:
- Less error-prone and more powerful quantum computing systems
- Widely adopted open-source modeling and training frameworks
- Substantial and skilled developer ecosystem
- Compelling AI applications that outperform classical computing with quantum computing.
These critical steps would enable the further development of quantum AI.
AI for Quantum Computing
A Nature Communications review highlighted how AI is advancing quantum computing across the entire stack:
- Hardware design: AI optimizes qubit layouts and chip architecture
- Calibration: Machine learning automates quantum system calibration
- Error correction: AI-powered decoders are now the most precise for identifying and fixing quantum errors
- Circuit compilation: AI designs smarter transpilers that efficiently translate algorithms to quantum hardware.4
Feel free to read more on quantum computing:
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Comments 1
Share Your Thoughts
Your email address will not be published. All fields are required.
Thank you. Where may I study this further?
Quantum Computing vendors are publishing quite some material. We will also be publishing other research on this topic.