Researchers unveil a transformative algorithm that lets quantum computers handle multiple objectives at once, paving the way for breakthroughs in materials science, physics, and beyond.
Integrated Genetic Algorithm with Variational Quantum Algorithm (GA-VQA) for multi-target quantum compilation optimization. (a) The GA-VQA method uses a parameterized quantum circuit and a classical computer to evaluate and update both the circuit structure and its parameters. (b) The process is as follows: (i) Generate a set of circuits V and parametersΘ. (ii) Evaluate the cost function L(Θ, Vl) for each circuit l ∈ [1, nV]. (iii) If the threshold is not met, use VQA followed by GA to find the best circuit, checking the threshold after each iteration. (iv) If the threshold is still not reached, pass the best circuit V∗ back to VQA and repeat the process until the threshold is met. Here, i and g represent iteration indexes running up to niter and ngene , respectively.
Integrated Genetic Algorithm with Variational Quantum Algorithm (GA-VQA) for multi-target quantum compilation optimization. (a) The GA-VQA method uses a parameterized quantum circuit and a classical computer to evaluate and update both the circuit structure and its parameters. (b) The process is as follows: (i) Generate a set of circuits V and parametersΘ. (ii) Evaluate the cost function L(Θ, Vl) for each circuit l ∈ [1, nV]. (iii) If the threshold is not met, use VQA followed by GA to find the best circuit, checking the threshold after each iteration. (iv) If the threshold is still not reached, pass the best circuit V∗ back to VQA and repeat the process until the threshold is met. Here, i and g represent iteration indexes running up to niter and ngene ,respectively.
Quantum computers differ fundamentally from classical ones. Instead of using bits (0s and 1s), they employ "qubits," which can exist in multiple states simultaneously due to quantum phenomena like superposition and entanglement.
For a quantum computer to simulate dynamic processes or process data, among other essential tasks, it must translate complex input data into "quantum data" that it can understand. This process is known as quantum compilation.
Benchmarked Against Conventional Methods: The study demonstrated that the multi-target algorithm outperforms traditional single-target methods in fidelity and efficiency, particularly for tasks like thermal state preparation and time-dependent quantum dynamic simulations.
Essentially, quantum compilation "programs" the quantum computer by converting a particular goal into an executable sequence. Just as the GPS app converts your desired destination into a sequence of actionable steps you can follow, quantum compilation translates a high-level goal into a precise sequence of quantum operations that the quantum computer can execute. According to the study, quantum compilation involves transforming a target unitary operation into a trainable unitary represented by a quantum circuit.
Traditionally, quantum compilation algorithms optimize a single target at a time. While effective, this approach has limitations. Many complex applications require a quantum computer to multitask. For example, when preparing thermal states, simulating time-dependent quantum systems, or preparing quantum states for experiments, researchers may need to manage multiple operations at once to achieve accurate results. In these situations, handling one target at a time becomes inefficient.
To address these challenges, Tohoku University's Dr. Le Bin Ho led a team that developed a multi-target quantum compilation algorithm. They published their new study in the journal Machine Learning: Science and Technology on December 5, 2024.
"By enabling a quantum computer to optimize multiple targets at once, this algorithm increases flexibility and maximizes performance," says Le.
The team achieved this by integrating Genetic Algorithms (GA) with Variational Quantum Algorithms (VQA) to optimize both the structure and parameters of quantum circuits simultaneously. This leads to improvements in complex-system simulations or tasks that involve multiple variables in quantum machine learning, making it ideal for applications across various scientific disciplines.
Scalability for Quantum Circuits: The algorithm can handle unitaries for up to 10 qubits with moderate circuit depths, showcasing its practicality for current quantum hardware limitations.
In addition to performance improvements, this multi-target algorithm opens the door to new applications previously limited by the single-target approach. For instance, in materials science, researchers could use this algorithm to simultaneously explore multiple properties of a material at the quantum level. In physics, the algorithm may assist in studying systems that evolve or require various interactions to be fully understood. Specific examples explored in the study include thermal state preparation, which involves simulating Gibbs states for materials science applications, and time-dependent quantum dynamic simulations of spin systems.
This development represents a significant advancement in quantum computing. "The multi-target quantum compilation algorithm brings us closer to the day when quantum computers can efficiently handle complex, multi-faceted tasks, providing solutions to problems beyond the reach of classical computers," adds Le.
Looking ahead, Le aims to study how this algorithm can adapt to various types of noise and identify ways to enhance its performance. This focus on noise adaptation is especially critical for the NISQ (Noisy Intermediate-Scale Quantum) era, where quantum devices are highly sensitive to environmental disturbances.
Tohoku University
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