Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications

TL;DR

This survey assesses the current state of Quantum Machine Learning on near-term, gate-based quantum devices, with a focus on real-world supervised and unsupervised tasks. It analyzes data encoding strategies and two main QML frameworks—quantum kernel methods and variational quantum circuits—along with error mitigation and gradient-based optimization, comparing to classical baselines where possible. The authors identify hardware limitations (noise, decoherence, limited qubits), data-loading bottlenecks, and barren plateaus as key bottlenecks, and propose directions such as standardized benchmarks, improved quantum data preparation, and robust XAI and security considerations. By surveying applications in High Energy Physics, Healthcare, and Finance, the paper highlights practical hurdles and outlines concrete avenues for progression toward scalable, real-world QML on near-term quantum devices.

Abstract

The past decade has witnessed significant advancements in quantum hardware, encompassing improvements in speed, qubit quantity, and quantum volume-a metric defining the maximum size of a quantum circuit effectively implementable on near-term quantum devices. This progress has led to a surge in Quantum Machine Learning (QML) applications on real hardware, aiming to achieve quantum advantage over classical approaches. This survey focuses on selected supervised and unsupervised learning applications executed on quantum hardware, specifically tailored for real-world scenarios. The exploration includes a thorough analysis of current QML implementation limitations on quantum hardware, covering techniques like encoding, ansatz structure, error mitigation, and gradient methods to address these challenges. Furthermore, the survey evaluates the performance of QML implementations in comparison to classical counterparts. In conclusion, we discuss existing bottlenecks related to applying QML on real quantum devices and propose potential solutions to overcome these challenges in the future.

Figures (7)

• Figure 1: Overview of the paper.
• Figure 2: (a) Timeline Trend and (b) Coverage Distribution of Works Related to Quantum Machine Learning on arXiv.
• Figure 3: Description of quantum gates in the order of single and multiple qubits
• Figure 4: Swap test Classifier : The first register is the ancilla qubit $(a)$, the second contains n copies of the test datum $(x)$, the third are the data qubits $(d)$, the fourth is the label qubit $(l)$ and the final register corresponds to the index qubits $(m)$. An operator $U$ creates the input state necessary for the classification protocol. The swap-test and the two-qubit measurement statistics yield the classification outcome.
• Figure 6: Variational Quantum Classifier : The state preparation $\Phi(x)$ followed by $W(\theta)$ which is the parameterized circuit with parameters $\theta$ followed by measurement in the $Z$-basis.