The convergence of quantum computing and artificial intelligence (AI) is paving the way for a transformative shift in technology. Known as Quantum AI, this groundbreaking combination brings together the immense computational power of quantum computing with the adaptive and problem-solving capabilities of AI.
This won’t be an incremental improvement, but rather a monumental leap forward. Together, these two technologies have the potential to tackle problems far beyond the reach of even the most powerful classical computers.
Unlike classical computers, which use binary bits, quantum computers use qubits. This allows quantum computers to exist in multiple states simultaneously, using principles like entanglement and superposition.
Intel's Superconducting Qubit-Quantum Chip
With a sufficient number of qubits, quantum computers could theoretically be millions of times faster than the fastest microchip computers today. As a result, quantum computers can tackle complex problems, such as molecular simulations or optimization tasks, with far greater efficiency than classical systems.
If Quantum AI is poised to revolutionize industries and solve complex challenges, then what is holding it back?
A key challenge is that current quantum computers have a limited qubit capacity. This prevents them from handling large datasets, which is the foundation of AI models. Overcoming this barrier requires addressing physical and engineering challenges, such as maintaining quantum states longer, reducing noise interference, and improving qubit coherence.
Quantum computers also work very differently from traditional computers, making it difficult for developers who are used to familiar programming languages. To make quantum computing more accessible, it’s important to develop specialized algorithms and user-friendly tools.
Perhaps even a greater challenge in Quantum AI is error correction. Quantum computers are highly susceptible to errors due to the fragile nature of quantum states. Disturbances such as temperature fluctuations and electromagnetic interference can cause qubits to lose their coherence, leading to incorrect calculations and compromising the accuracy of the system.
To address these challenges, big tech firms such as IBM and Microsoft, and new market entrants such as IonQ and D-Wave Systems, are pushing the boundaries of quantum computing.
Google has introduced AlphaQubit, an AI-powered decoder that identifies quantum computing errors with state-of-the-art accuracy. In a paper published today in Nature, this breakthrough technology was revealed as the result of a collaboration between Google DeepMind’s machine learning (ML) expertise and Google Quantum AI’s error correction knowledge.
AlphaQubit aims to address the error correction issue by grouping multiple qubits into a single logical qubit and regularly performing consistency checks. These checks help identify errors, which can then be corrected to preserve the quantum information.
Google claims that AlphaQubit can use neural networks to predict and correct errors. Trained on data from Google's Sycamore quantum processor, AlphaQubit outperforms previous decoders. According to Google, it can reduce errors by 6% compared to tensor network methods and 30% compared to correlated matching methods.
“We expect quantum computers to advance beyond what’s available today,” explained Google DeepMind and Quantum AI teams via a blog. ”To see how AlphaQubit would adapt to larger devices with lower error levels, we trained it using data from simulated quantum systems of up to 241 qubits, as this exceeded what was available on the Sycamore platform.”
“Again, AlphaQubit outperformed leading algorithmic decoders, suggesting it will also work on mid-sized quantum devices in the future. Our system also demonstrated advanced features like the ability to accept and report confidence levels on inputs and outputs.”
Machine learning could be the solution to error correction in Quantum AI, allowing researchers to follow other challenges yet to be overcome.
While we may be years away from fully realizing the potential of the Quantum AI system, the groundwork is being laid now. Businesses, individuals, and policymakers should start considering the potential impact of Quantum AI on their respective fields.
