When quantum computers handle data, they need to convert it into a form that they can understand, known as quantum data. Traditionally, algorithms for this “quantum compilation” optimize only one objective at a time. However, a new algorithm has been developed by a research team that allows for the optimization of multiple objectives simultaneously, enabling quantum machines to perform multiple tasks at once.
Quantum computers are fundamentally different from classical computers. Instead of using bits (which represent 0s and 1s), they utilize “qubits,” which can exist in several states at the same time due to quantum principles like superposition and entanglement.
For a quantum computer to effectively simulate dynamic systems or manipulate data, it must convert complex input into “quantum data” that it can process. This transformation is referred to as quantum compilation.
In essence, quantum compilation “programs” the quantum computer by turning a specific goal into a series of executable steps. Just as a GPS application translates your desired location into a step-by-step route you can follow, quantum compilation breaks down an overarching objective into a detailed sequence of quantum operations that the computer can execute.
Historically, quantum compilation algorithms have focused on optimizing a single goal at a time. Although this method can be effective, it has its drawbacks. Many intricate applications necessitate that a quantum computer handles several tasks simultaneously. For example, when modeling quantum dynamical processes or preparing quantum states for experiments, researchers often need to run multiple operations concurrently to achieve precise outcomes. Attempting to address each target one by one can be inefficient in such scenarios.
To overcome these obstacles, Dr. Le Bin Ho from Tohoku University spearheaded a team to create a multi-target quantum compilation algorithm. Their findings were published in the journal Machine Learning: Science and Technology on December 5, 2024.
“By allowing a quantum computer to optimize several targets at once, this algorithm enhances flexibility and boosts performance,” Le explains. This advancement leads to better simulations of complex systems and tasks that involve multiple variables in quantum machine learning, making it suitable for diverse scientific applications.
Besides improving performance, this multi-target algorithm paves the way for new applications that were previously constrained by the traditional single-target approach. For example, in materials science, researchers could leverage this algorithm to investigate various properties of a material at the quantum scale simultaneously. In the field of physics, it could aid in studying systems that require a variety of interactions for a thorough understanding.
This breakthrough signifies a major leap in quantum computing technology. “The multi-target quantum compilation algorithm brings us closer to the era when quantum computers can effectively tackle complex, multi-dimensional tasks, providing solutions to challenges that classical computers cannot handle,” Le adds.
Looking to the future, Le plans to explore how this algorithm can withstand different types of noise and investigate methods to improve its efficiency.
