Velocity by Booz Allen

From computer vision to cybersecurity and everything in between, this past decade has shown us the versatility and power of machine learning technologies. Given this, we can expect that expanding the capabilities of machine learning will continue to drive progress across the board.

QML, the integration of quantum computing into machine learning systems, offers one promising way to do just that.

Other studies run on quantum simulators point to quantum

the data on this small number of qubits, the original data had to be compressed significantly. Though QML displayed benefits over machine learning models trained on such compressed data, it still lags behind the performance machine learning models trained on the full, uncompressed data. Advancement Within Reach As the power of quantum computers continues to grow, we can expect QML to swiftly catch up to and surpass the current state of the art as it develops into a powerful tool. This rapidly developing technology is poised to bring unprecedented advancements to a wide range of AI application areas, from computational biology to climate modeling, by offering improvements in performance and efficiency. Although QML is still a nascent technology, it has already been validated through small-scale

experiments and theoretical work. It’s essential to acknowledge that, like all emergent technologies, QML has its nuances and challenges. However, given the explosive growth trajectory of quantum computing, we can expect QML to rapidly transition from a fledgling technology with limited practicality to an invaluable tool that improves our ability to solve complex problems beyond the reach of classical computing techniques and advance the national security agenda. Isabella Bello Martinez , Ryan Caulfield , and Brian Rost are scientists at Booz Allen, helping clients understand what quantum computing can do today and how to prepare for the next wave of capabilities.

advantages for a variety of machine learning tasks crucial to applications, such as detecting financial irregularities, increasing battery efficiency, and diagnosing diseases, such as breast cancer and COVID-19. The evidence contained in these studies and many others is strengthened by the fact that it serves to confirm known theoretical advantages of QML. The combination of empirical results and theoretical predictions underscores the reliability and potential of QML for real- world applications. While these initial results are exciting, it’s crucial to clarify that current best QML models cannot yet outperform the best classical machine learning models. The QML examples highlighted above were built using a small number of qubits, with the computer vision being the largest at only eight qubits. To fit

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Quantum computers are increasingly being utilized as part of machine learning, creating the exciting new field of quantum machine learning (QML), which promises to overcome some of the processing power and speed limitations of other machine learning methods. QML is likely set to revolutionize areas such as drug discovery and computer vision by efficiently handling large and complex data sets. While quantum computers excel at specific tasks, they will need to work in tandem with classical computers to enhance their capabilities for certain problems. Relatively small quantum models have the power to perform well on even the largest and most complex datasets that would otherwise require impractically large, classical models. Current QML models have shown promise but they still face challenges, such as the need for data compression due to limited quantum bits (qubits). Despite these hurdles, the rapid growth and development of quantum computing indicates that QML could soon transition from a nascent technology to a transformative force.