By leveraging the unique capabilities of quantum mechanics, quantum AI aims to overcome the limitations of classical computing and unlock new possibilities.
Quantum artificial intelligence is an emerging field that combines the power of quantum computing with artificial intelligence (AI) to push the boundaries of what's possible. IBM, a pioneer in both AI and quantum computing, is at the forefront of this transformation, leading efforts to integrate quantum technology with AI.
Quantum computing relies on quantum bits, referred to as qubits, which can exist in multiple states simultaneously, unlike classical bits that are either 0 or 1. This phenomenon is referred to as superposition. This ability allows quantum computers to perform multiple calculations at once, vastly increasing their computational power. We say two qubits are entangled, if the pair is linked in such a way that the state of one qubit is directly related to the state of the other, no matter how far apart they are. Qubits that are entangled instantaneously affect one another. This is known as quantum entanglement, and is a fundamental aspect of quantum mechanics that plays a key role in quantum computing.
IBM has been a trailblazer in quantum computing, having introduced the IBM Quantum Platform in 2016, which gives researchers and developers access to quantum computers via the cloud. With the development of the IBM Quantum System One in 2019, IBM has created the first commercially available quantum computer. In 2023, an IBM Quantum System One was operationalized at the IBM facility in Bromont, Quebec.
Recently, IBM unveiled its IBM Quantum System Two, marking the first quantum computer designed for long-term quantum advancements. This system, which is part of IBM's roadmap extended to 2033, integrates quantum and classical computing resources. It contains three IBM Quantum Heron processors.
Quantum computing relies on more than advanced hardware. It also demands efficient software. First released in 2017, IBM’s Qiskit is an open-source quantum computing software development kit, designed to simulate, and run quantum programs on both simulators and actual quantum hardware. It supports building quantum circuits, optimizing quantum algorithms, and running them on IBM’s quantum processors.
IBM envisions a future where quantum computing and AI work in tandem to accelerate breakthroughs in multiple fields. AI relies heavily on machine learning algorithms that require immense processing power to handle large datasets.
Quantum computing accelerates machine learning through its ability to process these vast datasets at unprecedented speeds. This is thanks to principles like superposition, which enables quantum computers to evaluate numerous possibilities at once, rather than one at a time (as classical computers do). This will reduce the time it takes to train models from days or weeks to hours or minutes.
Optimization is a common challenge in AI due to combinatorial explosion, which occurs as the number of possible combinations grows rapidly with an increasing number of variables and is notoriously difficult for classical systems to tackle. Quantum computing, with algorithms like quantum annealing and variational circuits, can tackle these high-dimensional optimizations by analyzing many possible configurations simultaneously (called quantum parallelism).
Additionally, quantum AI has the potential for more accurate predictions by using quantum parallelism. These models achieve greater accuracy by processing vast probability distributions and explore optimal solutions across complex, multidimensional datasets in ways classical systems cannot. This makes quantum systems well-suited for AI applications that require a thorough analysis of all possible outcomes, rather than relying on approximations.
IBM is already making strides in demonstrating how quantum AI can solve real-world problems. IBM is collaborating with businesses to apply quantum AI in logistics, helping companies optimize supply chains by predicting demand more accurately, reducing costs, and improving efficiency. In finance, IBM is exploring how quantum AI can revolutionize risk management by analyzing market data and financial trends at unprecedented speeds, allowing for quicker, more accurate decision-making. In healthcare, IBM collaborated with the Cleveland Clinic, deploying IBM Quantum System One to accelerate research. This partnership focuses on developing quantum-driven pipelines for drug discovery, enhancing cardiovascular risk models, and identifying potential Alzheimer’s treatments. By combining quantum computing with AI and hybrid cloud technologies, IBM enables rapid analysis of complex datasets, addressing challenges with precision and efficiency.
Despite its promise, quantum AI is still in the experimental stage, and there are technical challenges to overcome. One of the primary hurdles is scalability. As quantum systems aim to tackle larger problems, the number of qubits required increases. Current quantum computers often struggle to maintain stable qubit connections without errors. Preserving stability in qubits is challenging due to their susceptibility to decoherence, which occurs when qubits interact with their surrounding environment (temperature fluctuations, electromagnetic radiation, vibrations in the physical structure...). This interaction can cause the quantum state of a qubit to collapse, leading to errors in calculations.
Additionally, many traditional AI algorithms are not directly applicable to quantum systems, requiring researchers to innovate and adapt techniques to leverage quantum capabilities effectively. Finally, there are resource limitations, including access to quantum computing platforms and the necessary cloud resources for hybrid quantum-classical computing environments.
IBM’s continued investment in quantum research and its open-source initiatives are paving the way for widespread adoption. By advancing both the hardware and software needed for quantum AI, IBM is positioning itself as a leader in the future of AI technology. Quantum artificial intelligence represents the convergence of two of the most transformative technologies of our time. As quantum computing matures, it is poised to reshape the future of AI.