# Evidencing quantum effects in machine learning

TLDRPhD student Amira from the University of Quzida Natal explores the utility of quantum resources in machine learning. Collaborating with Maria Shill from Xanadu, they investigate quantum phenomena's role in quantum computing advantages. Drawing from quantum foundations and cognition, they develop a simple model using the temporal CHSH inequality to measure quantum effects and gain insights from quantum cognition, suggesting a new paradigm for understanding quantum machine learning.

### Takeaways

- 🔬 The speaker, Amira, is a PhD student researching quantum machine learning and has worked at IBM Quantum and collaborated with Maria Shi at Xanadu.
- 🧠 The research aims to understand which quantum resources are useful for machine learning and to pinpoint quantum phenomena that contribute to quantum computing advantages.
- 📈 There's a challenge in quantum computing to identify the source of power that gives quantum algorithms an advantage over classical ones.
- 🤔 The speaker questions the necessity of understanding quantum phenomena's role in algorithms, especially if they provide exponential advantages.
- 💡 The talk references the historical development from simple perceptron models to complex deep neural networks in classical machine learning, noting the ongoing mystery of what makes deep networks so effective.
- 🌟 Quantum machine learning models, like parameterized quantum circuits, are compared to the evolution of classical models, with the hope of finding a 'quantum perceptron'.
- 🚧 The speaker argues that comparing quantum machine learning models to classical models with decades more development is not fair, suggesting a need for a different approach.
- 🔍 The research goal is to find a setting in quantum machine learning where quantum nature can be probed and understood, focusing on tools to measure quantum effects.
- 🔗 The approach leverages ideas from quantum foundations and quantum cognition to understand quantum machine learning, using the CHSH inequality as a tool to measure quantum effects.
- 📊 The study uses synthetic data sets to train quantum models and observes whether the models leverage quantum phenomena, finding that some models do indeed exhibit non-classical behavior.
- 🔮 The research concludes that a deeper understanding of quantum effects in machine learning could lead to new paradigms and more effective quantum machine learning models.

### Q & A

### What is the primary focus of Amira's PhD research?

-Amira's PhD research primarily focuses on quantum machine learning, investigating the utility of quantum resources for machine learning tasks.

### What is the difficulty in identifying the source of quantum advantage in algorithms?

-Identifying the source of quantum advantage in algorithms is challenging because it is not straightforward to pinpoint exactly which quantum phenomena contribute to the advantage offered by quantum computing algorithms.

### Why is understanding the quantum phenomena in machine learning important?

-Understanding the quantum phenomena in machine learning is important to develop more effective quantum algorithms and to gain a deeper insight into the mechanisms that could potentially offer advantages over classical counterparts.

### What is the significance of the perceptron model in the context of machine learning?

-The perceptron model is significant as it represents a foundational concept in machine learning. It was one of the earliest models that could be theoretically probed and studied well, paving the way for the development of more complex models like deep neural networks.

### What is a parameterized quantum circuit and its role in quantum machine learning?

-A parameterized quantum circuit is a type of quantum machine learning model where some operations in a quantum circuit are parameterized and then trained, tuned, or tweaked to fit data for machine learning tasks. It plays a role in quantum machine learning by providing a framework to explore quantum advantages in learning algorithms.

### Why is it not fair to compare quantum machine learning methods with classical ones based on current hardware?

-It is not fair to compare quantum machine learning methods with classical ones based on current hardware because quantum computers are still in their early stages, while classical hardware has decades more research and development behind it, allowing for the training of models with billions of parameters.

### What is the Temporal CHSH inequality and its relevance to quantum machine learning?

-The Temporal CHSH inequality is a form of Bell inequality used to test for non-classical correlations in quantum systems. It is relevant to quantum machine learning as it provides a criterion to measure quantumness or non-classical behavior, which can be used to understand when quantum phenomena are useful for machine learning tasks.

### How does quantum cognition contribute to the understanding of quantum machine learning?

-Quantum cognition contributes to the understanding of quantum machine learning by providing an interpretative framework where quantum theory is used to model cognitive behavior. This can help in developing intuition about why and when quantum phenomena might be leveraged in machine learning models.

### What is the significance of the experiment where a quantum model is trained on data generated from observables that violate Bell's inequality?

-The significance of training a quantum model on data generated from observables that violate Bell's inequality is that it demonstrates the model's ability to learn and exhibit quantum behavior. This experiment helps in understanding when quantum phenomena are being leveraged for machine learning tasks.

### What are the potential next steps in the research of quantum effects in machine learning?

-Potential next steps in the research could involve using models that allow for direct study of the contribution of quantum effects to machine learning, developing new paradigms for machine learning based on this understanding, and exploring data that has a natural inductive bias for certain quantum models.

### Outlines

### 🧠 Quantum Machine Learning Research

Amira, a PhD student at the University of Quizida Natal, discusses her research on quantum machine learning, particularly focusing on quantum resources useful for machine learning. She reflects on the broader question of identifying quantum phenomena that contribute to the advantage of quantum computing algorithms over classical ones. Amira highlights the difficulty of pinpointing the source of quantum advantage and compares this challenge to the ongoing mystery of what makes deep neural networks so successful in classical machine learning. She introduces the concept of a quantum perceptron model and the idea of parameterized quantum circuits as a starting point for exploring quantum machine learning, emphasizing the need to understand the role of quantum phenomena like entanglement and interference in enhancing algorithms.

### 🔬 The Challenge of Quantum Machine Learning

Amira elaborates on the challenges of doing quantum machine learning research, especially when comparing it to classical models with billions of parameters. She argues that comparing quantum methods to state-of-the-art classical hardware is unfair due to the early stage of quantum technology. Amira suggests that there needs to be more justification for using quantum methods and a deeper understanding of how quantum phenomena aid in machine learning tasks. She outlines a three-point approach to tackle these issues: finding a tool to measure quantum effects, developing a simple quantum machine learning model for study, and gaining intuition about the model's quantum behavior.

### 🔄 Drawing Insights from Quantum Foundations and Cognition

The speaker discusses the interdisciplinary approach to quantum machine learning by looking into quantum foundations and quantum cognition. Quantum foundations, which seeks to understand the counter-intuitive aspects of quantum theory, provides tools like Bell tests to measure quantum effects. Quantum cognition applies quantum theory to model cognitive behavior, potentially offering insights into quantum machine learning models. Amira suggests that by combining these fields, researchers can develop a better understanding of when and why quantum phenomena are leveraged in machine learning.

### 📊 Developing a Quantum Machine Learning Framework

Amira explains the development of a simple quantum machine learning model inspired by quantum cognition. She uses the temporal CHSH inequality as an example of a Bell inequality to measure non-classical behavior. The model involves a qubit representing an initial belief state, which is updated based on information processed through observables. This framework allows for the creation of synthetic data sets to train quantum models and investigate their ability to learn from data generated by observables that either do or do not violate Bell's inequality.

### 🚀 Future Directions in Quantum Machine Learning

In conclusion, Amira summarizes the work done to define a simple criterion for non-classical behavior using Bell's inequality, borrow interpretations from quantum cognition, and construct a framework to parameterize observables in quantum machine learning. She presents a trivial example of how this framework can be used to study the contribution of quantum effects in machine learning. Amira advocates for the use of models that allow for direct study of quantum effects in machine learning and suggests that this research could lead to a new paradigm for machine learning. She invites interested individuals to reach out for further discussion and anticipates the publication of their paper for wider dissemination of their findings.

### Mindmap

### Keywords

### 💡Quantum Machine Learning

### 💡Quantum Resource

### 💡Entanglement

### 💡Quantum Computing

### 💡Perceptron Model

### 💡Deep Neural Networks

### 💡Parameterized Quantum Circuit

### 💡Variational Circuits

### 💡Quantum Cognition

### 💡Bell Inequality

### Highlights

Amira, a PhD student at the University of Quzida Natal, discusses her research in quantum machine learning.

The research aims to understand which quantum resources are useful for machine learning.

It's challenging to pinpoint the quantum phenomena contributing to the advantage of quantum algorithms.

The importance of understanding quantum phenomena in algorithms is compared to the success of deep neural networks.

The idea of finding a 'quantum perceptron' model is introduced.

Parameterized quantum circuits are discussed as a popular model in quantum machine learning.

The goal is to develop a model that can harness quantum phenomena for machine learning tasks.

The challenge of comparing quantum machine learning models to classical models with decades more research.

The need for a deeper understanding of quantum phenomena in machine learning beyond empirical success.

The complexity of quantum machine learning frameworks involving data, models, circuit design, and optimization.

The concept of 'quantum effects' and how they are measured and analyzed is debated.

The approach to study quantum machine learning involves finding tools to measure quantum effects, simple models, and developing intuition.

Quantum foundations and quantum cognition are fields that provide tools and intuition for quantum machine learning.

The use of Bell tests from quantum foundations to measure non-classical behavior.

Quantum cognition provides a framework to model cognitive behavior with quantum theory.

A simple quantum machine learning model inspired by cognition is developed to study quantum effects.

The model uses a temporal CHSH inequality to test for quantum behavior.

The model is trained on synthetic data sets to investigate the need for quantum phenomena in learning.

Findings show the model can learn observables that violate Bell's inequality, indicating quantum behavior.

The research advocates for models that allow direct study of the contribution of quantum effects in machine learning.

The study is a small-scale step towards understanding when quantum phenomena are leveraged in machine learning.