Quantum computing and artificial intelligence are both transformative technologies, and quantum computing may be necessary for artificial intelligence to make significant advances.
Artificial intelligence produces functional applications on classical computers, but it is limited by the computer’s computational power. Quantum computing increases the computational power of artificial intelligence, allowing 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 impossible with classical computers.
What is quantum computing?
Quantum mechanics is a universal model based on principles different from those observed in everyday life. Processing data with quantum computing requires a quantum model of the data. Hybrid quantum-classical models are also needed for quantum computing, such as for error correction and proper functioning of quantum computers.
- Quantum data: Quantum data is data packets stored in qubits for computation. However, observing and storing quantum data is challenging due to valuable features such as superposition and quantum entanglement. Furthermore, quantum data contains a lot of noise. Machine learning must be applied at the stage to correctly analyze and interpret these data.
- Hybrid quantum classical model: Only when quantum processors are used to generate quantum data is there a high chance of obtaining meaningless data. This gives rise to hybrid models equipped with high-speed data processing mechanisms such as CPUs and GPUs, which are widely used in classical computers.
- Quantum algorithm: An algorithm is a series of steps that lead to the solution of a problem. To perform these steps on your device, you must use an instruction set that your device supports. Quantum computing introduces an instruction set based on a fundamentally different execution model than classical computing. The purpose of quantum algorithms is to exploit quantum effects such as superposition and entanglement to obtain solutions more quickly.
If you want to learn more, be sure to read our in-depth article on quantum computing.
Why is it important?
Although AI has advanced rapidly over the past decade, it has yet to overcome its technological limitations. The unique capabilities of quantum computing can remove obstacles to achieving artificial general intelligence (AGI). Quantum computing can be used to quickly train machine learning models and create optimized algorithms.
Optimized and stable AI enabled by quantum computing advances technology by allowing years of analysis to be completed in less time. Neuromorphic cognitive models, adaptive machine learning, and reasoning under uncertainty are among the fundamental challenges of AI today. Quantum AI is one of the most likely solutions for next-generation AI.
Quantum AI breakthrough
Quantum machine learning approaches practical application
Researchers from CSIRO and the University of Melbourne have discovered that quantum machine learning does not require full error correction. Partial error correction can be used in quantum applications. This approach significantly reduces hardware requirements and allows quantum machine learning to power real-world applications in the near future rather than decades from now.
IonQ achieves practical quantum advantage for AI
IonQ and Ansys run medical device simulations on IonQ’s 36-qubit computer, outperforming traditional high-performance computing by 12%.
Generative Quantum AI Partnership with Quantinuum + NVIDIA
Quantinuum partners with NVIDIA to accelerate the combination of quantum computing and generative AI through NVQLink. Early testers include Amgen (researching hybrid quantum machine learning for biologics) and BMW (fuel cell research).
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 purpose of TFQ is to provide the tools necessary to control and model natural or artificial quantum systems.
Source: Google
Here’s how it works:
- Convert the quantum data to Quantum dataset: Quantum data can be represented as a multidimensional array of numbers called a . quantum tensor. TensorFlow processes these tensors to create datasets for further use.
- Select the quantum neural network model. A quantum neural network model is selected based on knowledge of quantum data structures. The purpose is to perform quantum processing to extract information hidden in entangled states.
- sample or average: In quantum state measurements, classical information is extracted from a classical distribution in the form of samples. The value is taken directly from the quantum state. TFQ provides a method for averaging multiple runs including steps (1) and (2).
- Evaluate classic neural network models – As quantum data is transformed into classical data, deep learning techniques are used to learn the correlations within the data.
The other steps of evaluating the cost function, gradient, and updating parameters are classic steps in deep learning. These steps ensure effective models for unsupervised tasks.
What possibilities exist for the application of quantum computing in AI?
Researchers’ short-term realistic goal for quantum AI is to develop and implement quantum algorithms that outperform classical algorithms.
Quantum algorithms for learning
Development of quantum algorithms for quantum generalization of classical learning models. This enables speeding up and other improvements to the deep learning training process. Quantum computing’s contribution to classical machine learning can be achieved by rapidly providing optimal weight sets to artificial neural networks.
Quantum algorithms for decision-making problems
Classical decision problems are formulated as decision trees. The way to arrive at a set of solutions is to create branches from certain points. However, this method becomes less efficient if each problem is too complex to be solved by repeated halves. Quantum algorithms based on Hamiltonian time evolution can solve problems represented by a large number of decision trees faster than random walks.
quantum search
Most search algorithms are designed for classical computing. Classical computing outperforms humans at search problems. On the other hand, Lov Grover offered the Grover algorithm and said that a quantum computer could solve this problem even faster than a classical computer. AI powered by quantum computing could hold promise for near-term applications such as cryptography.
quantum game theory
Classical game theory is a widely used modeling approach in AI applications. Quantum game theory is an extension of this theory to quantum fields. This could be a promising tool to overcome critical problems in quantum communication and implement quantum artificial intelligence.
What are the key milestones for quantum AI?
Although quantum AI is an immature technology, improvements in quantum computing are being made that increase the potential of quantum AI. However, significant milestones are required for the quantum AI industry to become a more mature technology. These milestones can be summarized as follows:
- More powerful quantum computing systems with fewer errors
- Widely adopted open source modeling and training framework
- Rich ecosystem of skilled developers
- Fascinating AI applications that surpass classical computing using quantum computing.
These important steps will enable further development of quantum AI.
AI for quantum computing
A review in Nature Communications highlights how AI is advancing quantum computing across the stack.
- hardware design: AI optimizes qubit layout and chip architecture
- calibration: Machine learning automates the calibration of quantum systems
- error correction: AI-powered decoders are now the most accurate at identifying and correcting quantum errors
- Circuit configuration: AI designs smarter transpilers that efficiently translate algorithms into quantum hardware.
Feel free to read on to learn more about quantum computing.
principal analyst
Sem Dilmegani
principal analyst
Cem’s work has been cited by major global publications such as Business Insider, Forbes, and the Washington Post, global companies such as Deloitte and HPE, NGOs such as the World Economic Forum, and supranational organizations such as the European Commission. See more reputable companies and resources that reference AIMultiple.
Throughout his career, Cem has worked as a technology consultant, technology buyer, and technology entrepreneur. He has spent more than a decade advising companies on technology decisions at McKinsey & Company and Altman Solon. He also presented a McKinsey report on digitalisation.
Reporting to the CEO, he led the communications company’s technology strategy and procurement. He also led the commercial growth of deep tech company Hypatos, from 0 to 9 digit valuations with 7-digit annual recurring revenue within two years. Cem’s work at Hypatos has been featured in major technology publications such as 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.
View full profile
