Quantum computing, using principles like entanglement and superposition, is gaining interest due to its potential applications. The first quantum hardware has been developed with limited computing power and noise issues. A new publication in the Quantum Machine Intelligence Journal looks at the effects of quantum hardware properties on the performances of variational quantum learning algorithms.
Variational Quantum Algorithms (VQAs) are gaining attention for their versatility and ease of implementation. The design of a suitable parametrized ansatz is crucial in VQAs, but an optimal methodology is yet to be found. The structure of quantum hardware also impacts VQAs’ performance. A heuristic metric function has been developed to aid in deciding the best ansatz structure for efficient VQA application.
Quantum Machine Learning and Quantum Computing
Quantum computing, which utilizes quantum mechanical principles like entanglement and superposition, has garnered interest from various academic and business sectors due to its potential applications. The first real quantum hardware has been realized and made available for practical investigations despite being subject to noise and having limited computing power. The current trend suggests that quantum devices in the noisy intermediate scale quantum (NISQ) era will significantly improve in terms of the number of qubits, noise resilience, and programmability, paving the way for real-world applications.
Variational Quantum Algorithms (VQAs)
VQAs have recently attracted attention due to their relatively handy implementation and versatility of application. The core of these algorithms is a variational quantum circuit containing parametrized gates, usually called variational ansatz, which is updated iteratively by an optimizer aiming at the minimization of an objective function built on the specific problem to be solved. VQAs are used to solve optimization problems, find the ground state energy of chemical or molecular compounds, and perform regression or classification in quantum machine learning.
The Role of Ansatz in VQAs
In the design of any VQA, particular care should be posed in defining a suitable parametrized ansatz. The variational quantum circuit affects the performance of the algorithm in a fundamental way. Various approaches have been proposed to define a set of suitable ansatz for quantum computers, ranging from those pointing at hardware efficiency to problem-specific ansatz. However, when there is no clear intuition about the underlying mathematical form of the possible solution, such as in many machine learning problems, a proper methodology for constructing an optimal ansatz is still to be found.
The Impact of Quantum Hardware Structure on VQAs
The effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. It is experimentally shown that complex connectivity in the ansatz introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is necessary to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation.
The Use of Heuristic Metric Function in VQAs
The experimental finding allows the construction of a heuristic metric function that aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data and the latter on a classification task performed on the Iris dataset.
The article titled “The effects of quantum hardware properties on the performances of variational quantum learning algorithms” was published on February 5, 2024, in the Quantum Machine Intelligence journal. The authors of the study are Giuseppe Buonaiuto, Francesco Gargiulo, Giuseppe De Pietro, Massimo Esposito, and Marco Pota. The research explores the impact of quantum hardware properties on the performance of variational quantum learning algorithms. The DOI reference for the article is https://doi.org/10.1007/s42484-024-00144-5.