Qubit Wise Architecture Search Enhances Design of Quantum Circuit Architectures

The Qubit Wise Architecture Search (QWAS) method is a novel approach to designing high-performing variational quantum circuit architectures, a crucial aspect of quantum machine learning. QWAS optimizes a part of the circuit on a selected qubit per stage, using the Monte Carlo Tree Search algorithm to partition the search space for the next stage. It also introduces noise adaptive terms, making it suitable for noisy intermediate scale quantum devices. Unlike other methods, QWAS doesn’t depend on the reward function and can be applied to a broader range of problems. However, fine-tuning the supernet architecture to align with specific tasks remains a challenge.

What is the Qubit Wise Architecture Search Method for Variational Quantum Circuits?

Quantum machine learning is a rapidly evolving field that leverages the principles of quantum mechanics to improve machine learning algorithms. One of the key components of these algorithms is the use of variational quantum circuits (VQCs), which allow for the tuning of quantum circuit parameters through classical optimization methods. They have been validated experimentally for various small-scale learning problems, demonstrating potential advantages in domains ranging from chemistry to machine learning tasks.

However, designing a high-performing and robust variational quantum circuit architecture is a crucial aspect for quantum machine learning, especially considering the noise limit of noisy intermediate scale quantum (NISQ) hardware. Many established ansatzes typically comprise repeated layers with a fixed topology of parameterized and non-parameterized gates. To develop a strategy to design VQC in an automated way, researchers have turned their attention to the classical Neural Architecture Search (NAS) framework.

NAS focuses on automating the design of neural network structures but often grapples with the challenge of evaluating a vast number of possible network architectures. The Monte Carlo Tree Search (MCTS) algorithm addresses this issue by iteratively exploring and evaluating segments of the search space, thereby identifying promising neural network structures without exhaustive enumeration.

How does the Qubit Wise Architecture Search (QWAS) Method Work?

The Qubit Wise Architecture Search (QWAS) method is a novel approach proposed to address the challenges in designing a high-performing variational quantum circuit architecture. QWAS progressively optimizes the part of the circuit on a selected qubit per stage, then uses MCTS to partition the search space for the next stage to speed up the search process.

Moreover, by introducing noise adaptive terms, the QWAS method can produce more trainable and robust quantum circuit designs suitable for NISQ devices. This method is akin to making a random picture meaningful by changing a little pixel row by row.

What are the Advantages of the QWAS Method?

The QWAS method offers several advantages over traditional methods. Firstly, it devises a novel qubit-wise strategy to progressively search and optimize one qubit configuration of the BaseNet, leading to a large improvement in performance.

Secondly, it introduces noise adaptive terms into the Monte Carlo Tree Search algorithm to find more efficient and robust quantum circuit designs. This makes the QWAS method particularly suitable for NISQ devices, which are characterized by their noise limit.

How does the QWAS Method Compare to Other Methods?

Compared to other methods, the QWAS method offers a more efficient and robust solution for designing variational quantum circuit architectures. Other methods, such as the reinforcement learning algorithm, depend on the quality of the reward function and are only valid for VQE problems.

In contrast, the QWAS method does not depend on the reward function and can be applied to a broader range of problems. Moreover, it avoids the additional computational overhead and the impossibility to share parameters throughout the entire search, which are issues associated with other methods like the nested MCTS method.

What is the Future of the QWAS Method?

The QWAS method represents a significant advancement in the field of quantum machine learning. Its ability to efficiently and robustly design variational quantum circuit architectures makes it a promising tool for future research and applications.

However, like all methods, it is not without its challenges. One primary concern is the necessity for fine-tuning the supernet architecture to align effectively with specific tasks. As the field of quantum machine learning continues to evolve, it will be interesting to see how the QWAS method and other similar methods develop and improve to meet these challenges.