# The mystery of Neural Network: linked with quantum mechanics and universe

Pintu Pal, Subhodeep Moitra, Deblina Banerjee

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## To cite this version:

Pintu Pal, Subhodeep Moitra, Deblina Banerjee. The mystery of Neural Network: linked with quantum mechanics and universe. 2024. hal-04821194

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Preprint submitted on 5 Dec 2024

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# The mystery of Neural Network: linked with quantum mechanics and universe

Pintu Pal

Department of Computer
Applications
Techno College Hooghly
Chinsurah, West Bengal, India
Email:
dr.ppal.aec@gmail.com

Subhodeep Moitra

Department of Computer
Applications
Techno College Hooghly
Chinsurah, West Bengal, India
Email:
subhodeep2000@gmail.com

Deblina Banerjee

Department of Computer
Applications
Techno College Hooghly
Chinsurah, West Bengal, India
Email:
banerjeedeblina07@gmail.com

## Abstract

This short literature puts light on the conceptual connections between the neural networks (NNs), quantum mechanics, and the universe. It underlines the fact that share reliance on the high-dimensional and the probabilistic principles. Neural networks, as a computational models, shows complex behavior through the non-linear transformations in high-dimensional spaces, where the interchange of weights and the activations replicates the abstract representations found in the quantum systems. Similarly, to the way the quantum mechanics operates with a probabilistic framework, emphasizes the uncertainty and entanglement at the microscopic scales. Drawing on these analogies, this literature puts forward an idea that the universe itself may be viewed as a vast computational framework, where the inter-related processes resemble the layered operations of NNs. By understanding these interdisciplinary connections, we focus to shed light on the underlying mechanisms of the complex systems. This can further inspire new approaches in artificial intelligence and physics, which can open new dimensions toward a unified understanding and explanation of information, computation, and the nature of reality.

Keywords: Neural Networks, Machine Learning, Quantum Mechanics, Universe, Computer Science

## Introduction: Conceptual Connections Between Neural Networks, Quantum Mechanics, and the Universe

In examining the complexities of neural networks (NNs), there emerges an analogy between their operations and the probabilistic nature of quantum mechanics and the structural dynamics of the Universe. At the quantum level, things are highly probabilistic, much like the inner workings of NNs, which are very complex due to their high-dimensional representations. Both NNs and the Universe at the quantum level are full of uncertainties, where things do not operate in a deterministic approach as we understand them in real life (Schrödinger, 1926). These connections may offer a deeper

understanding of the complex systems and can lead to exciting interdisciplinary advancements.

## High-Dimensional Spaces, Non-Linear Transformations, and Probabilistic Nature in Neural Networks, Quantum Mechanics, and the Universe

The NNs and Deep Learning (DL) models work in high-dimensional spaces, where the activation of neurons denotes data points in these spaces. The transformations that occur through the hidden layers of NNs map these points through complex and non-linear operations (Bishop, 2006). In some sense, the abstract representations of data in NNs can be viewed as analogous to how matter and energy in the universe remain
entangled and intersect in ways that are not fully
understood (Feynman, Leighton, & Sands,
2011).

Just as space-time is complex and
multidimensional in the fields of physics, the
weights and activations in a NN operate across
multi-dimensions that do not necessarily
correlate with any direct observable facts
(Hassabis et al., 2017). In fact, the feature space
in NNs often remains abstract, much like the
hidden layers of reality in quantum physics. The
quantum mechanics is probabilistic by nature.
The particles and the quantum state don't have
any deterministic positions or velocities and they
define by the probability distributions (Feynman
et al., 2011). Similarly, NNs in complex
architectures, like those using attention
mechanisms, show probabilistic behavior. For
instance, the attention mechanism does not
deterministically decide which part of an input to
focus on, but instead depends on learned
probabilities from past training data to focus on
different parts of an input (Goodfellow, Bengio,
& Courville, 2016).

The non-linearity in NNs, such as the activation
function, can be linked to quantum
superpositions, where various features interact
and combine in a non-inherent way (Schuld &
Killoran, 2019). Training a NN resembles the
behavior of quantum systems, which is not
deterministic and largely depends on past
training data, weights, and so forth (Bishop,
2006). The connection between the NNs,
quantum mechanics and the universe also has
some philosophical advantages. The NNs much
like the universe are systems that processes
information in a complex, non-linear technique
across multiple dimensions. If we consider the
training of a NN as a quantum system, then we
may think the weights in the NN as a quantum
state which evolves based on the interaction
(data) and the back propagation as the type of
feedback that updates the states for reaching a
more optimized state (solution) (Penrose, 1994).
The universe from a computer science
perspective can be viewed as a large

interconnected network of information
processing systems where each individual
element like the planet, star or particle can be
considered as a neuron in a NN which is
processing and transmitting information across
the various scales and dimensions (Bennett,
1988). So in this approach the internal working of
the NNs and the universe may be part of a deeper
and a more fundamental computational
framework. The unknowns in the quantum
mechanics and the NNs can be lined to the fact
that we struggle to predict and explain the
behavior of a particle at the quantum levels due to
the high dimensionality and probabilistic nature
(Bohm, 1980). Similarly, we struggle to fully
understand how a specific neuron in a Deep
Neural Network (DNN) contributes to the final
decision because the network operates in a
similar degree of abstraction and high-
dimensional space (Goodfellow et al., 2016).

The Theory of Everything (TOE) in physics
attempts to connect the general theory of
relativity, which describes large-scale
phenomena like gravity, with quantum theory,
which describes the behavior of particles at the
smallest scales. If we were to connect this theory
with NNs, we could think of deep learning as part
of a larger, deeper computational framework that
mimics or is inspired by the dynamics of the
universe at both large and small scales (Einstein,
1954). There can also be drawn further
resemblances, suggesting that NNs represent a
soft computing model for the universe. NNs can
be viewed as a simplified model of complex
systems which captures behaviors and patterns
emerging from systems operating in high
dimensional spaces just like the Universe itself
(Schuld & Killoran, 2019). The feedback loops
like the back propagation in NNs resembles the
feedback loops in the nature like the ecosystems
or the feedback in the quantum measurements
(Penrose, 1994).

Ultimately, it can be viewed that both the
universe and NNs operate in high-dimensional
spaces where actions are often not
understandable or explainable by human
intuition (Bennett, 1988). The mysteries of the
universe, whether in computer science, physics,
or the human brain, may be part of a larger
computational structure. If we can bridge the gap
between machine learning, quantum physics, and
the human brain, we might begin to visualize a
unified way to understand and explain how
complex systems like NNs or the Universe
evolve, learn, and make sense of the areas around
them (Bohm, 1980).

Conclusion

The thing is that NNs, quantum mechanics and
the universe shares a fundamental quality where
all of them operates in high dimensional spaces
where the behavior emerge in non-linear and
probabilistic ways. The exact connections among
them remains unexplored but it is clear that both
the NNs and the quantum mechanics operates on
the principles that is reflected by the complexity
of the Universe itself.

Finally, it can also be suggested that for fully
understanding and explaining the complete
practical working of the NNs, it might eventually
need bridging these concepts which can further
lead to new dimensions in both the artificial
intelligence and the fundamental physics.

**References**

Bennett, C. H. (1988). The thermodynamics of computation – A review. *International Journal of Theoretical Physics*, *21*(12), 905–940.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Bohm, D. (1980). Wholeness and the Implicate Order. Routledge.

Einstein, A. (1954). Ideas and Opinions. Bonanza Books.

Feynman, R. P., Leighton, R. B., & Sands, M. (2011). The Feynman Lectures on Physics, Vol. 3: Quantum Mechanics. Addison-Wesley.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Hassabis, D., et al. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245–258.

Penrose, R. (1994). Shadows of the Mind: A Search for the Missing Science of Consciousness. Oxford University Press.

Schrödinger, E. (1926). An undulatory theory of the mechanics of atoms and molecules. Physical Review, 28(6), 1049–1070.

Schuld, M., & Killoran, N. (2019). Quantum Machine Learning: What Quantum Computing Means to Data Mining. Springer.