In recent years, the fields of quantum computing and artificial intelligence (AI) have been advancing at an astonishing pace. While both domains have individually transformed various industries, the convergence of quantum computing and AI has opened up new possibilities and paved the way for a groundbreaking approach known as Quantum Machine Learning (QML). This fusion promises to revolutionize fields such as internet search, fraud detection, and brain mapping by offering exponential speedup and enhanced capabilities. In this article, we will delve into the world of QML and explore its potential to usher in a new era in AI.
Society is entering the age of extreme data, we generate 2.5 quintillion bytes daily, and by 2025, global data volumes are set to hit 163 zettabytes. This exponential growth in data highlights the need for advanced technologies, such as quantum computing, to effectively process and analyze this immense amount of information.
Quantum computers, run on a subatomic level using quantum bits (or qubits) that can represent a 0 and a 1 at the same time. Quantum computing employs fundamental properties of quantum physics: superposition, interference, and entanglement to solve problems exponentially more quickly than a traditional computer for a small set of specialized problems.
Superposition refers to the quantum phenomenon where a quantum system can exist in multiple states concurrently. Quantum interference is what allows us to bias quantum systems toward the desired state. Entanglement is an extremely strong correlation between quantum particles. Entangled particles remain perfectly correlated even if separated by great distances. The idea is to create a pattern of interference where the paths leading to wrong answers interfere destructively and cancel out but the paths leading to the right answer reinforce each other.
One of the areas where Quantum computing is predicted to play important role is Machine Learning (ML), a subfield of Artificial Intelligence which attempts to endow computers with the capacity of learning from data, so that explicit programming is not necessary to perform a task. ML algorithms allow computers to extract information and infer patterns from the record data so computers can learn from previous examples to make good predictions about new ones. Machine Learning (ML) has now become a pervasive technology, underlying many modern applications including internet search, fraud detection, gaming, face detection, image tagging, brain mapping, check processing and computer server health-monitoring.
Quantum machine learning algorithms aim to use the advantages of quantum computation in order to improve classical methods of machine learning, for example by developing efficient implementations of expensive classical algorithms on a quantum computer. The promise is that quantum computers will allow for quick analysis and integration of our enormous data sets which will improve and transform our machine learning and artificial intelligence capabilities.
We need much more advanced AI if we want it to help us create things like truly autonomous self-driving cars and systems for accurately managing the traffic flow of an entire city in real-time.
The Power of Quantum Machine Learning
As the physicists explain, quantum machine learning is a new subfield within the field of quantum information that combines the speed of quantum computing with the ability to learn and adapt, as offered by machine learning.
Quantum Machine Learning represents the intersection of quantum computing and traditional machine learning algorithms. It leverages the unique properties of quantum systems, such as superposition and entanglement, to process and analyze vast amounts of data in parallel. This parallelism grants QML an unprecedented advantage over classical machine-learning approaches, enabling it to solve complex problems with remarkable efficiency.
Quantum machine learning (QML) combines the speed of quantum computing with the adaptive learning capabilities of machine learning. It leverages the unique properties of quantum systems, such as superposition and entanglement, to process and analyze large datasets in parallel. This parallelism gives QML a significant advantage over classical machine learning, allowing it to solve complex problems efficiently. Quantum computing’s power lies in the algorithms it enables, as they exhibit different complexity characteristics than classical algorithms.
QML encompasses various scenarios, including classical algorithms applied to quantum data, quantum algorithms applied to classical data, and quantum algorithms applied to quantum data. The ability of quantum computing to compute multiple states concurrently makes it exponentially faster than classical computers for optimization tasks.
Machine learning consists of two things: data and algorithms. Quantum machine learning is a term used to cover 4 types of scenarios:
- Quantum-inspired classical algorithms on classical data: such as tensor network and de-quantized recommendation systems algorithms.
- Classical algorithms are applied to quantum data: such as neural network-based quantum States and optimizing pulse sequences.
- Quantum algorithms are applied to classical data: such as quantum optimization algorithms and quantum classification of classical data. The main characteristic of quantum computing is the ability to compute multiple states concurrently. A quantum optimization algorithm can combine all possible candidates and yield those that promise good results. Therefore, quantum computing promises to be exponentially faster than classical computers in the optimization of the algorithm.
- Quantum algorithms are applied to quantum data: such as quantum signal processing and quantum hardware modeling.
Quantum data is any data source that occurs in a natural or artificial quantum system. This can be data generated by a quantum computer, like the samples gathered from the Sycamore processor for Google’s demonstration of quantum supremacy. Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. The quantum supremacy experiment showed it is possible to sample from an extremely complex joint probability distribution of 2^53 Hilbert space.
The quantum data generated by NISQ processors are noisy and typically entangled just before the measurement occurs. Heuristic machine learning techniques can create models that maximize extraction of useful classical information from noisy entangled data. The TensorFlow Quantum (TFQ) library provides primitives to develop models that disentangle and generalize correlations in quantum data—opening up opportunities to improve existing quantum algorithms or discover new quantum algorithms.
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If quantum computers have speedups in linear algebra subroutines, it can speed up machine learning.
We all know that linear algebra is the core of machine learning. In particular, a group of linear algebra applications called BLAS (Basic Linear Algebra Subroutines) is the fundamentals of all machine learning algorithms. These subroutines include matrix multiplication, Fourier transforms, and solving linear systems.
Quantum machine learning algorithms aim to leverage the advantages of quantum computation to enhance classical machine learning methods. By harnessing the power of quantum computing, such algorithms have the potential to provide quick analysis and integration of large datasets, transforming capabilities in machine learning and artificial intelligence.
Recent experiments by IBM also demonstrated the potential of quantum computers to improve machine learning tasks, with reduced error rates achieved through entangling qubits. However, to fully harness the exponential speedups offered by quantum computers, the development of dedicated quantum memory that can store and communicate quantum data with the quantum processor is necessary, as the current systems rely on classical memory for data storage and communication.
A research team has demonstrated that overparametrization improves performance in quantum machine learning
In the realm of quantum machine learning, the Los Alamos National Laboratory research team has uncovered a groundbreaking approach that leverages overparametrization to significantly enhance performance, effectively surpassing the capabilities of classical computers. Overparametrization is a technique that entails increasing the number of parameters within a machine learning model. This innovation has often been utilized in classical machine learning to avoid getting stuck in suboptimal configurations during training, allowing models to find their optimal settings more effectively. The critical revelation in the new study lies in the team’s establishment of a theoretical framework that identifies the precise point at which a quantum machine learning model reaches overparametrization. At this juncture, the introduction of additional parameters triggers a remarkable leap in the network’s performance, making the model significantly easier to train.
The implications of this research are substantial, especially in the realm of practical quantum applications. By optimizing the training process in quantum neural networks through overparametrization, quantum machine learning holds the potential to revolutionize fields such as quantum materials research. Complex tasks that are exceedingly challenging for classical computers, such as classifying different phases of matter, can now be tackled with enhanced speed and accuracy, thanks to the incorporation of quantum mechanics principles like entanglement and superposition. In essence, this research opens the door to achieving the coveted quantum advantage in machine learning, heralding a promising future for quantum AI and its ability to address problems that have been beyond the reach of classical computing methods.
Quantum parallelism can help train models more faster
Quantum parallelism, enabled by quantum superposition, offers the potential to accelerate the training of machine learning models. Quantum superposition allows quantum computers to simultaneously work on multiple quantum states. The idea is that if we can train a model using the superposition of all possible training sets, the training process may become faster and more efficient.
However, the potential benefits of quantum parallelism in training models have certain limitations. Firstly, it has been found that quantum computers do not provide exponentially fewer data requirements for training models compared to classical computers. The belief that quantum parallelism could drastically reduce the amount of data needed for training is inaccurate. While there may be some cases where linear speedups are possible, the exponential reduction in data is not achievable.
The other aspect of quantum parallelism is the potential for faster model training. This idea is based on the speedup achieved by quantizing classical algorithms using Grover’s algorithm. However, the speedups obtained from quantizing classical algorithms are typically limited to quadratic improvements at best, rather than exponential. Therefore, while quantum parallelism can offer some speedup in training models, it is important to have realistic expectations about the magnitude of the speedup.
In summary, quantum parallelism has the potential to contribute to faster model training, but the extent of the speedup is limited and may not be exponential. Researchers continue to explore and develop quantum machine learning algorithms that can leverage the advantages of quantum parallelism effectively.
Quantum Computers can model highly correlated distributions in a way classical computers can’t.
Quantum computers possess the ability to model highly correlated distributions in a manner that classical computers cannot replicate. This is due to the inherent quantum properties of superposition and entanglement, which enable quantum systems to represent and process complex relationships between data points more efficiently. However, it is important to note that merely having this capability does not guarantee a quantum advantage over classical computers.
Recent research has demonstrated that the ability to model highly correlated distributions alone is not sufficient to achieve superior performance in machine learning tasks. In fact, classical models have been shown to outperform quantum ones, even when the datasets are generated using quantum processes. This highlights the need for dedicated hardware and specialized algorithms specifically designed for Quantum Machine Learning (QML).
Scientists have recognized the potential of quantum computing in the field of machine learning and are actively exploring the application of general quantum computers as well as developing dedicated architectures for QML. The development of dedicated hardware and QML algorithms will be crucial to fully harness the power of quantum computing and realize its potential for advancing machine learning and artificial intelligence.
2022: Google AI develops new method for training quantum neural networks
In 2022, researchers at Google AI developed a new method for training quantum neural networks that requires significantly less data than previous methods. This could make quantum machine learning more accessible and practical.
Traditionally, quantum neural networks require a lot of data to train. This is because quantum computers are still in their early stages of development, and they are not yet able to process large amounts of data. The new method developed by Google AI uses a technique called “quantum annealing” to train quantum neural networks with less data. Quantum annealing is a type of optimization algorithm that can be used to find the lowest energy state of a system. In the case of quantum neural networks, the system is the network itself, and the energy state is the accuracy of the network.
The new method developed by Google AI was able to train a quantum neural network with 100 qubits to an accuracy of 99% using only 100 training examples. This is a significant improvement over previous methods, which required thousands or even millions of training examples to achieve the same accuracy.
The new method developed by Google AI could make quantum machine learning more accessible and practical. This is because it will reduce the amount of data that is needed to train quantum neural networks. This could make quantum machine learning more affordable for businesses and researchers.
In 2017, researchers from Caltech and USC demonstrated the first application of quantum computing to machine learning. They developed a quantum-compatible method that effectively extracted a rare signal from noisy data, specifically focusing on identifying the Higgs boson particle. Unlike traditional approaches, the quantum machine learning technique performed well even with small datasets, showing promise for quantum advantages in processing complex data.
While the current quantum computers are limited in size and do not yet demonstrate a fundamental advantage over classical computers, a study by MIT and IBM suggests that larger quantum computers could indeed offer such an advantage. The researchers performed a simple calculation using a two-qubit quantum computer and concluded that advanced feature-mapping methods could enable the classification of highly complex datasets beyond the capabilities of classical computers. Although achieving quantum advantage in machine learning is still a distant goal, these developments indicate a promising path forward.
Another research project led by Michael Hartmann at Heriot-Watt University aims to build a dedicated neural network computer based on quantum technology, rather than relying solely on AI software. The goal is to combine quantum computing and neural networks to achieve unprecedented speeds and make complex decisions rapidly. By constructing neural networks using superconducting electrical circuits and leveraging quantum principles, the project seeks to scale up quantum neural networks without computational errors, potentially revolutionizing the field of artificial intelligence. While the full utilization of this technology may take several years, the impact could be significant once quantum neural networks surpass classical AI software in real-world applications.
D-Wave Quantum computer learned to ‘see’ trees in 2017
In 2017, scientists successfully trained a D-Wave quantum computer to recognize trees by feeding it hundreds of satellite images of California. The quantum computer used its qubits to analyze various features of the images and determine whether clumps of pixels represented trees. After being trained, the D-Wave computer achieved an accuracy rate of 90% in identifying trees in aerial photographs. This breakthrough paves the way for quantum computers to tackle complex machine learning tasks, such as pattern recognition and computer vision, with potential applications in climate forecasting and weather pattern analysis.
In the new study, physicist Edward Boyda of St. Mary’s College of California in Moraga and colleagues fed hundreds of NASA satellite images of California into the D-Wave 2X processor, which contains 1152 qubits. The researchers asked the computer to consider dozens of features—hue, saturation, even light reflectance—to determine whether clumps of pixels were trees as opposed to roads, buildings, or rivers. They then told the computer whether its classifications were right or wrong so that the computer could learn from its mistakes, tweaking the formula it uses to determine whether something is a tree.
After it was trained, the D-Wave was 90% accurate in recognizing trees in aerial photographs of Mill Valley, California, the team reports in PLOS ONE. “Classification is a tricky problem; there are short trees, tall trees, trees next to each other, next to buildings—all sorts of combinations,” says Nemani.
Researchers Prove that Robots Learn Faster with Quantum Technology, reported in March 2021
Quantum Machine Learning represents a new frontier in AI that offers exponential speedup and enhanced capabilities in fields such as internet search, fraud detection, and brain mapping. By harnessing the power of quantum computing, QML algorithms can process and analyze massive datasets, providing faster and more accurate results compared to traditional approaches.
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