Quantum machine learning, a nascent field that merges quantum computing and machine learning, is gaining traction in academic and business circles. It involves running machine learning algorithms on quantum devices, potentially enhancing problem-solving capabilities. The field covers a wide range of topics, including quantum shadow tomography and machine learning for quantum physics. Currently, quantum computing is in the noisy intermediate scale quantum (NISQ) era, with quantum computers limited by background noise. Quantum error correction (QEC) code is seen as a solution to these limitations, aiming to protect quantum information and enable fault-tolerant quantum computing (FTQC) in the future.
Quantum Machine Learning: An Overview
Quantum machine learning, a field that combines quantum computing and machine learning, has been gaining significant attention in both academic and business circles. This field involves running machine learning algorithms on quantum devices. The concept is still in its early stages of development, with no precise definition yet. Some describe it as the convergence of quantum computing and machine learning, while others view it as the quantum counterpart to classical machine learning.
The Role of Artificial Intelligence and Machine Learning
Artificial intelligence, exemplified by technologies like ChatGPT, has become an integral part of everyday life. Its success largely depends on machine learning, a departure from traditional computer programs that entail crafting explicit instructions to directly solve problems. Machine learning models are trained on real-world data stored in a dataset, often denoted as D, acquiring the ability to tackle problems autonomously. This represents a departure from the conventional von Neumann model of digital computing.
The Need for Quantum Machine Learning
The need for quantum machine learning arises from the fact that classical machine learning relies heavily on linear algebra procedures. Quantum mechanics, at its core, is inherently grounded in linear algebra. Over time, it has become evident that quantum computing has the potential to dramatically enhance a computer’s problem-solving capabilities in certain specific scenarios. For instance, quantum computers can leverage algorithms like the Harrow-Hassidim-Lloyd algorithm (HHL algorithm), which has a complexity of O(log N^2), leading to significant speedup in certain conditions.
The Broad Spectrum of Quantum Machine Learning
Quantum machine learning encompasses a broader spectrum of topics. For instance, quantum shadow tomography has gained prominence, focusing on the characterization of a given quantum state by accumulating data from various measurements. Another facet involves machine learning for quantum physics, which entails employing classical machine learning tools to explore various aspects of quantum physics. Additionally, some developments in both quantum algorithms and quantum hardware are often encompassed within the broader umbrella of quantum machine learning.
The Current State of Quantum Computing
Currently, we are in the noisy intermediate-scale quantum (NISQ) era of quantum computing. Quantum computers are susceptible to background noise, which imposes limitations on our ability to construct quantum computers with sufficient depth for executing tasks demanding fast and precise computations. The quantum computers available today can only handle on the order of around 100 qubits and they all exhibit noise, making it challenging to derive tangible benefits for our daily lives.
The Role of Quantum Error Correction
The solution to the limitations of current quantum computers is known as quantum error correction (QEC) code. QEC acts as a safeguard for quantum information, which is typically lost once it’s measured. However, information protected by QEC can persist if it remains undamaged within certain limits. The objective is to implement QEC codes across all quantum devices, ushering in an era of fault-tolerant quantum computing (FTQC) in the future. This trajectory parallels the history of classical computing, where before the invention of classical error correction, scaling up and running useful algorithms on classical computers was also a formidable challenge.
On January 20, 2024, authors Yunfei Wang and Junyu Liu published an article titled “Quantum Machine Learning: from NISQ to Fault Tolerance” on arXiv, a repository of electronic preprints approved for publication after moderation, hosted by Cornell University. The article explores the evolution of quantum machine learning from Noisy Intermediate-Scale Quantum (NISQ) devices to fault-tolerant systems.
