Quantum computing and artificial intelligence (AI) are converging and promise to push the boundaries of computing, transform industries, and accelerate discovery.
Quantum computers utilize the principles of quantum mechanics to perform complex calculations. Exploiting the properties of quantum bits (qubits), such as superposition and entanglement, has the potential to significantly enhance the processing power of AI systems. This quantum AI synergy can tackle computationally intensive tasks that traditional computers cannot reach, potentially facilitating breakthroughs in medicine, materials science, financial modeling, and cryptography.
Although the integration of quantum computing and AI is still in its infancy, partnerships between quantum computing and networking companies aim to accelerate progress in this field. As research and development progresses, experts predict that quantum computing could impact AI by allowing machines to process and analyze vast amounts of data much faster than computers. are doing. For example, quantum-enhanced AI has the potential to optimize complex supply chain networks, reducing costs and increasing efficiency for businesses around the world.
“AI and quantum computing will have a potential impact on a wide range of mission-critical services, including cybersecurity, healthcare and finance, due to their ability to solve complex concepts,” EY Global's Global Innovation Quantum leader Christine Gilkes told PYMNTS. . “EY is already exploring examples of this with partners such as SandboxAQ in drug discovery, IBM in DNA sequencing, and Dell in optimization. can simultaneously test new drug applications and cancer treatments, significantly reducing R&D costs and democratizing the industry.”
Signs of commercial feasibility
Pasqal, a company specializing in quantum computing, and Welinq, a company focused on quantum networking, have announced a partnership to advance the technology. This collaboration will enable Pasqal to connect multiple quantum processors, helping overcome challenges in scaling the technology to achieve more reliable quantum computing.
“Pasqal's CEO and co-founder Georges-Olivier Reymond said in a news release: “Our collaboration is focused on creating concrete solutions by integrating Pasqal's quantum processing precision with his Welinq's innovative networking and quantum memory systems.” . “This is a quantum advance with real-world applications in mind, aiming to solve complex problems with greater efficiency and reliability.”
Matthijs van Waveren, a quantum computing expert at Sopra-Steria CS, told PYMNTS that quantum computing has the potential to revolutionize AI in three key areas: accuracy, speed, and energy efficiency.
“Quantum computing has the potential to bring the following to AI: increased precision, quantum acceleration, and reduced energy usage,” van Waveren said. He said quantum computing can find higher quality classification and segmentation solutions by searching the solution space in a different way, something his company has done for French space agency CNES. cited the project. “We showed that quantum annealer can be used to improve the quality of segmentation of satellite images.”
Asked about the timeline for these applications, Van Waveren said segmentation improvements are already being seen in test systems. But he noted that two hurdles must be overcome before commercial applications become a reality: increasing the number and quality of qubits in quantum computers.
“We expect to see commercial applications of quantum image classification and segmentation within about three years,” he said.
He also discussed the potential impact of AI quantum computing on general applications and commerce, particularly on satellite image analysis.
“Satellite imagery analysis is a growing commercial field,” he said. “The amount of data obtained from Earth observation missions is rapidly increasing due to the continued launch of new remote sensing satellites and the increasing sophistication of remote sensing equipment,” he said. “As AI quantum computing becomes commonplace, we expect that the benefits of quantum speedup and increased precision will enable us to process these extremely large amounts of data.”
Quantum computing could revolutionize AI
Carmen Recio Valcarce, quantum computing engineer and team leader at Moody's Analytics, told PYMNTS that quantum optimization algorithms have the potential to improve the training of AI models, and quantum circuits can improve the training of AI models with more expressiveness. He said that it is possible to better capture correlations and patterns.
Some quantum models already outperform classical models in sampling probability distributions. “The power of quantum computing in improving AI offers potential advances in this field,” she said.
AI can also benefit quantum computing by analyzing quantum mechanical systems, providing inspiration for quantum circuit architectures, and helping to design noise countermeasures and gates for quantum computers.
However, the schedule for these applications has not yet been determined. “Some of the requirements for quantum algorithms that can power AI are still being developed. For example, many of these algorithms require a physical implementation of quantum RAM,” he warned Recio Valcarce. “Although the realization of QRAM has been theoretically proven, concrete hardware implementation is still in progress.”
According to Joe Fitzsimons, founder and CEO of Horizon Quantum Computing, there are two main approaches to applying quantum computing to AI.
“The first approach is to try to train parameterized quantum circuits in much the same way you train neural networks,” Fitzsimmons said in an interview. “The hope is that quantum models could learn to perform calculations using quantum computing rather than classical computing, so neural networks could be more efficient than neural networks. .”
This approach is still experimental, and Fitzsimmons believes the second approach is more promising. “A second approach is to try to accelerate the training and use of machine learning models already used on classical computers. There has been great success in discovering such algorithms, and quantum computers will It is known to be able to dramatically speed up some machine learning models compared to computers.”
Fitzsimons himself has been working on Gaussian process regression, a machine learning model that can be implemented exponentially faster using quantum computing. However, he cautions that these algorithms require error-free quantum computation and specialized hardware, such as quantum random access memory (QRAM), which has not yet been demonstrated.
Despite the challenges, Fitzsimmons is optimistic about quantum computing's potential impact on AI. “It is difficult to overestimate the impact of exponential acceleration on computational speed, even if it is limited to a small number of models,” he said. “This opens up the possibility of applying machine learning techniques to much more complex problems than is currently possible.”