# Generative AI-enabled Quantum Computing Networks and Intelligent Resource
Allocation

Minrui Xu, Dusit Niyato, Jiawen Kang, Zehui Xiong, Yuan Cao, Yulan Gao, Chao
Ren, and Han Yu Minrui Xu, Dusit Niyato, Yulan Gao, and Han Yu are with the
School of Computer Science and Engineering, Nanyang Technological University,
Singapore 639798, Singapore (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>han.yu@ntu.edu.sg). Jiawen Kang is
with the School of Automation, Guangdong University of Technology, China
(e-mail: kjwx886@163.com). Zehui Xiong is with the Pillar of Information
Systems Technology and Design, Singapore University of Technology and Design,
Singapore 487372, Singapore (e-mail: zehui_xiong@sutd.edu.sg). Chao Ren is
with the School of Electrical and Electronic Engineering, Nanyang
Technological University, Singapore 639798, Singapore (e-mail:
chao.ren@ntu.edu.sg). Yuan Cao is with the School of Communications and
Information Engineering, Nanjing University of Posts and Telecommunications,
Nanjing 210003, China (e-mail: yuancao@njupt.edu.cnz).

###### Abstract

Quantum computing networks enable scalable collaboration and secure
information exchange among multiple classical and quantum computing nodes
while executing large-scale generative AI computation tasks and advanced
quantum algorithms. Quantum computing networks overcome limitations such as
the number of qubits and coherence time of entangled pairs and offer
advantages for generative AI infrastructure, including enhanced noise
reduction through distributed processing and improved scalability by
connecting multiple quantum devices. However, efficient resource allocation in
quantum computing networks is a critical challenge due to factors including
qubit variability and network complexity. In this article, we propose an
intelligent resource allocation framework for quantum computing networks to
improve network scalability with minimized resource costs. To achieve
scalability in quantum computing networks, we formulate the resource
allocation problem as stochastic programming, accounting for the uncertain
fidelities of qubits and entangled pairs. Furthermore, we introduce state-of-
the-art reinforcement learning (RL) algorithms, from generative learning to
quantum machine learning for optimal quantum resource allocation to resolve
the proposed stochastic resource allocation problem efficiently. Finally, we
optimize the resource allocation in heterogeneous quantum computing networks
supporting quantum generative learning applications and propose a multi-agent
RL-based algorithm to learn the optimal resource allocation policies without
prior knowledge.

###### Index Terms:

Quantum key distribution, distributed quantum computing, resource allocation,
reinforcement learning.

##  I Introduction

Quantum computing networks can integrate existing networking and computing
infrastructure with quantum communication and computing devices to enhance
network security, scalability, and sustainability [1]. Specifically,
incorporating Quantum Key Distribution (QKD) into data center networks [2],
especially for generative AI systems, shows significant promise in bolstering
privacy protection during the training and inference phases of large
foundation models [3]. Furthermore, the interconnection of quantum devices in
distributed quantum computing architectures enables the tackling of complex
computational tasks, which are currently infeasible for classical computers to
solve in a reasonable period of time. However, quantum computing networks are
still in a developing stage. Currently, quantum channels can generate several
kilobytes of secret keys per second, and Noisy Intermediate-Scale Quantum
(NISQ) devices are limited to maintaining only hundreds of qubits.

In quantum computing networks, devising effective resource allocation schemes
is a critical but unresolved issue, which is essential for the seamless
integration of classical computing and networking infrastructure for
generative AI with emerging quantum devices [4]. The inherent dynamics of
quantum resources, such as qubit instability and decoherence, pose challenges
in providing quantum cryptography and quantum computing services, which are
crucial for securing and enhancing classical infrastructure. Specific
challenges in quantum computing networks include uncertain fidelities of
qubits and entangled pairs, the coexistence of multiple communication
protocols, and the heterogeneity of networks comprising diverse nodes and
connections. Addressing these challenges necessitates the efficient allocation
of quantum resources. This includes allocating additional qubits for error
correction and utilizing entangled pairs for quantum state purification.
Furthermore, developing translation policies is vital for interoperability
among networks utilizing different protocols. Effective resource allocation
frameworks are also crucial to ensure seamless connectivity and functionality
across various types of quantum nodes and links.

Fortunately, the inherent uncertainties in quantum computing networks can be
effectively modeled and addressed by utilizing reinforcement learning (RL),
which is capable of modeling the dynamics of quantum links and devices [5]. RL
agents learn optimal quantum resource allocation policies through interactive
engagement with the quantum computing network environment. To optimize this
interaction, state-of-the-art RL algorithms employ deep, quantum, or
generative neural networks for policy representation. Specifically, deep
learning enables RL agents to simplify the approximation of the Q-function in
extensive state and action spaces. Additionally, generative AI enables RL
agents to create interaction trajectories and support long-term decision-
making in quantum computing networks. Moreover, RL agents using quantum
policies, parameterized by quantum neural networks, can expedite action
selection in centralized quantum resource allocation scenarios. The
synergistic benefits of integrating generative AI with quantum computing are
manifold:

  * •

Generative AI for quantum computing networks: Generative AI significantly
enhances the sample efficiency and training stability of intelligent resource
allocation algorithms, which can be achieved through long-horizon planning and
the synthesis of trajectory experiences [6].

  * •

Quantum computing networks for generative AI: By utilizing quantum computing
and communication, the training and inference processes of generative AI
models can be made more secure, rapid, and energy-efficient. Moreover, by
leveraging the principles of quantum mechanics, generative AI has the
potential to introduce innovative architectures and models for a wide range of
general or specific tasks.

Therefore, the synergy of generative AI and quantum computing networks offers
the potential to develop novel algorithms for intricate resource allocation
problems, as well as expand the range of applications within quantum computing
networks.

Figure 1: An overview of intelligent resource allocation in quantum computing
networks.

In this article, we propose an intelligent resource allocation framework for
quantum computing networks for allocating quantum resources with uncertain
fidelities. First, we introduce the quantum computing networks which consist
of two main services, i.e., quantum key distribution and distributed quantum
computing, and three types of quantum resources, i.e., quantum channels,
computation qubits, and entangled pairs. Furthermore, we propose an
intelligent allocation framework for quantum resources in quantum computing
networks. We introduce existing state-of-the-art RL algorithms, including,
deep reinforcement learning (DRL), deep generative learning, and quantum
reinforcement learning (QRL). In addition, the challenges in the intelligent
resource allocation framework are highlighted. Finally, we leverage a
heterogeneous quantum computing network that supports quantum generative
learning applications to validate the effectiveness of the proposed
intelligent resource allocation framework.

Our main contributions can be summarized as:

  * •

We propose an intelligent quantum resource allocation for quantum computing
networks enabled by generative AI. On the one hand, the security and
scalability of training and inference of distributed machine learning systems
for generative AI can be enhanced in quantum computing networks. On the other
hand, generative AI can enable more effective and robust intelligent resource
allocation algorithms in quantum computing networks.

  * •

We highlight the potential applications and challenges for resource allocation
in quantum computing networks, where the resource allocation problem is
formulated as stochastic optimization under the uncertain fidelities of qubits
and entangled pairs.

  * •

We present a use case of multi-agent reinforcement learning for quantum
resource allocation in heterogeneous quantum computing networks to illustrate
the effectiveness of the proposed framework. The experimental results
demonstrate that the proposed framework can reduce the computation latency and
energy consumption in providing quantum generative learning applications.

TABLE I: Comparative Summary of Quantum Computing Network Types

Network Types |  Point-to-point Quantum Networks |  Multi-protocol Quantum Networks |  Heterogeneous Quantum Networks
---|---|---|---
Objectives |  Establish secure connections between two quantum computing nodes using quantum channels |  Integrate multiple experimental quantum networks into a global quantum network |  Extend range and coverage, enhance network robustness
Resource Types |  • Quantum channels |  • Quantum channels • Computation qubits • Entangled pairs |  • Quantum channels • Entangled pairs
Limitations |  Fidelity degradation of quantum signals over long distances |  Additional protocol translation costs |  High complexity in coordination and management

##  II Quantum Computing Networks

Quantum computing networks facilitate collaboration and information exchange
between multiple classical computers and quantum devices over optical backbone
networks and space-air-ground integrated networks (SAGINs), as illustrated in
Fig. 1. Specifically, quantum computing networks enable the secure
collaborative execution of large-scale computation tasks and advanced quantum
algorithms [1]. In quantum computing networks, tasks like training and
inference of foundation models can be securely conducted across multiple data
centers using quantum cryptography, such as QKD which ensures secure
communication by using quantum mechanics principles for key generation and
distribution. Furthermore, advanced quantum algorithms can be decomposed and
compiled into smaller, manageable quantum circuits suitable for execution on
NISQ devices. These NISQ devices execute quantum circuits using qubits, the
fundamental units of quantum information, and quantum gates, which facilitate
quantum operations. To enable information exchange in a quantum network,
entangled pairs, i.e., pairs of qubits in a quantum-mechanically linked state,
are generated and distributed. This distribution allows for information
exchange through a process known as entanglement swapping, a key technique for
quantum communication [7]. By establishing hybrid connections that bridge
classical and quantum devices via quantum channels, quantum computing networks
can address and mitigate limitations inherent in both classical and quantum
systems, including security vulnerabilities, quantum noise, and scalability
issues. However, due to current hardware constraints in quantum technology,
quantum circuits must be effectively mapped to correspond with the original
circuit design of the advanced quantum algorithms, ensuring compatibility and
optimal functionality.

###  II-A Quantum Key Distribution

QKD is used in quantum computing networks for secure peer-to-peer
communication between computation nodes [2]. By leveraging the non-cloning
theorem which states that an unknown quantum state cannot be copied exactly in
quantum mechanics, QKD focuses on cryptographic key exchange via quantum
channels over fiber optic links for ground-based communications and free-space
links for aerial and satellite communications. During QKD-secured exchanges
between two computational nodes, both the sender and receiver deploy their
quantum communication infrastructure, establishing a hybrid link composed of
classical and quantum channels. The process begins with the sender preparing
and transmitting a sequence of quantum states, typically encoded in photons,
through the quantum channel to the receiver. Upon receipt, the receiver
performs quantum measurements on these states using specific measurement bases
that are compatible with the sender’s preparation method. Afterward, the
sender and receiver engage in a public comparison of a subset of their
measurement results over a classical channel to estimate the quantum bit error
rate in the transmission. They then apply quantum error correction protocols
to rectify any discrepancies in the key, followed by a process called privacy
amplification. This step involves distilling a shorter, but more secure, final
key from the error-corrected key, eliminating any partial information that an
eavesdropper might have gained. The distinct advantage of QKD lies in its
ability to generate secret keys over a distance in an information-
theoretically secure manner, relying on the fundamental laws of quantum
physics rather than the assumed difficulty of computational problems.

###  II-B Distributed Quantum Computing

Distributed quantum computing involves the use of multiple interconnected
quantum processors to collaborate to execute quantum circuits of advanced
quantum algorithms [1]. This approach is designed to surpass the inherent
limitations of single quantum processors, particularly in terms of the number
of qubits available and overall computational power. The essence of
distributed quantum computing lies in the coordination and execution of
quantum algorithms across a network of interconnected processors. In this
setup, each quantum processor is assigned a specific portion of the overall
computational task. These processors then operate simultaneously, executing
their respective components and communicating through a coherent quantum
network. Such parallel and coordinated execution harnesses the collective
computational power of the network, effectively amplifying the capabilities
beyond what individual processors can achieve. Resource requirements of
distributed quantum computing can be estimated during the compiling of quantum
circuits and realized during the execution through quantum Message Passing
Interface (MPI) via entanglement swapping. Furthermore, the integration of
distributed quantum computing into existing workflow models is facilitated by
extending workflow languages. This extension enables the mapping of complex
quantum computing tasks to native modeling constructs, ensuring both the
portability of these workflows across different quantum computing platforms
and ease of orchestration and execution.

###  II-C Quantum Resources

As listed in TABLE I, there are three main types of quantum resources in
quantum computing networks, including quantum channels, computation qubits,
and entangled pairs.

####  II-C1 Quantum Channels

To establish secure communication links between two computing nodes, quantum
channels can be utilized to exchange secure keys for message transmission.
Quantum channels facilitate the distribution of symmetric keys encoded as
quantum states, enhancing communication security against eavesdropping attacks
and quantum attacks. To generate quantum channels over optical transport
networks, a suite of specialized components is required, including QKD
transmitters, QKD receivers, trusted relays, untrusted relays, local key
managers, and the security infrastructure [2]. Specifically, QKD transmitters
prepare and send quantum states while QKD receivers detect these states.
Trusted relays are secure nodes that assist in extending the range of quantum
communication, while untrusted relays facilitate quantum repeater technology
without the need for full trust in the relay. As more quantum channels are
allocated within these networks, there is a corresponding increase in the
secure key rates, which is crucial for effective quantum communication.
However, a significant challenge arises from the inherent point-to-point
nature of quantum communication. Unlike classical channels that can broadcast
to multiple recipients, quantum channels typically connect just two nodes
directly. To address the increasing demands, it is crucial to allocate and
reserve quantum channel resources according to user requests flexibly to
improve the scalability of quantum computing networks.

####  II-C2 Computation Qubits

In each quantum processor, a certain number of qubits are generated to encode
and process classical information and perform operations using quantum gates
[8]. While qubits can be used to run quantum algorithms for solving complex
computational problems more efficiently than classical computers, the limited
quantity of physical qubits, errors, and decoherence pose challenges in
scaling quantum computing to achieve quantum advantages. Consequently, qubit
allocation becomes a critical issue in quantum computing networks, where
logical qubits, which represent the abstract quantum information, can be
mapped onto physical qubits. This process is referred to as qubit allocation.
Furthermore, the qubit allocation problem can be conceptualized as analogous
to the quadratic assignment problem in classical optimization theory, which is
known to be NP-hard, and is computationally challenging to solve as the size
of the input increases. The primary objective of qubit allocation is to find
an optimal mapping strategy, which aims to minimize the impact of quantum
noise and errors while simultaneously maximizing the efficiency and
scalability of the quantum computing network.

####  II-C3 Entangled Pairs

The exchange of information between two quantum nodes can be achieved through
entanglement swapping of entangled pairs [9], which are pairs of qubits that
are quantum-mechanically linked, such that the state of one (regardless of the
distance separating them) instantaneously influences the state of the other.
When entanglement connections span long distances, the fidelity of entangled
pairs may degrade due to imperfect measurements on quantum repeaters during
entanglement swapping operations. To ensure the fidelity of entangled pairs,
the iterative purification operation is applied to the entangled pairs. This
purification process involves the use of multiple entangled pairs to distill a
smaller number of pairs with higher fidelity. The fidelity of a purified
entangled pair is quantitatively assessed by comparing its original fidelity
with the fidelity post-purification. By iteratively applying this purification
process, the fidelity of entanglement connections can be enhanced to meet the
requisite quality standards for reliable quantum communication. The
purification process has its trade-off between generating additional entangled
pairs and those used for the actual information exchange.

###  II-D Potential Applications

####  II-D1 Distributed Machine Learning Systems

Distributed machine learning systems within quantum computing networks offer a
robust framework for collaborative AI model training and inference across
multiple computing nodes. During the collaborative training process across
multiple computing nodes, the raw data used for training is kept in local
databases for training local models [3]. Upon completion of local model
training, these computing nodes engage in a process of model sharing, which
involves transmitting the trained models to peer nodes or central servers for
model aggregation. In this way, the integrity of the original data can be
preserved and the creation of a more robust and comprehensive global model can
be trained through this aggregation process. However, the transmission of
these models poses a significant risk, particularly in terms of the security
of valuable and potentially sensitive AI models. To mitigate the risk of these
models being intercepted, monitored, or stolen during transmission, QKD links
are established between computing nodes.

####  II-D2 Sensor Networks, Smart Grids and Internet of Things

Sensor networks, smart grids, and the Internet of Things are constantly
collecting diverse types of data from environmental parameters to user
interactions for real-time monitoring, analysis, and decision-making. During
the process of data transmission, some highly sensitive sensing data may be at
risk of being intercepted and stolen, which can have an impact on high-risk
applications. For example, in secure smart grids, where data on power
consumption and distribution needs to be protected, quantum computing networks
can provide secure communication channels through quantum key distribution
[10]. This ensures that the data remains confidential and cannot be tampered
with, enhancing the overall security of the smart grid system. By harnessing
the advanced computational power of quantum computing, the analysis of
voluminous sensing data can be performed with heightened efficiency and
precision. This not only leads to more accurate decision-making but also
contributes to substantial resource savings.

##  III Generative AI-enabled Intelligent Resource Allocation for Quantum
Computing Networks

With the benefits of being adaptable to changing environments, RL is used to
enhance the efficiency of resource allocation in quantum computing networks.
For example, in the resource allocation problem of QKD networks with multiple
tenants, RL-based algorithms are proposed to improve the blocking probability
of tenant requests and the utilization of secret-key resources [5].
Specifically, RL agents can model the dynamic environments as a Markov
Decision Process (MDP), considering states, actions, rewards, and transition
probabilities. In the environment of quantum computing networks, the MDP can
usually be defined as follows:

  * •

State space: The state space in quantum computing networks encompasses key
configurations and situations of the network, which includes real-time
monitoring of computing nodes, communication links, application requests,
network topology, the number of qubits in quantum nodes, the quantity of both
quantum and classical channels in quantum links, and the operational protocols
of the quantum network.

  * •

Action space: The action space is defined by the decisions made by RL agents
at each time slot, which involves the allocation of available quantum
resources, such as quantum channels, computation qubits, and entangled pairs,
for the efficient execution of QKD protocols and quantum algorithms.

  * •

Reward function: The reward function is formulated to reflect the net utility
derived from providing quantum communication and computing services, which
depends on both the benefits of service delivery and the costs associated with
provisioning quantum resources.

  * •

State transition probability: Based on the present state of the quantum
network environment, RL agents determine the allocation of quantum resources.
Following this decision, the environment evolves into a subsequent state,
guided by a state transition probability model.

Subsequently, RL agents learn the policy to optimize the long-term expected
efficiency of resource utilization under changing environmental conditions. A
summary of existing RL algorithms can be found in TABLE II, which introduces
advantages, target scenarios, complexity, and famous algorithms of different
schemes.

TABLE II: Comparison of Different Learning-Based Quantum Resource Allocation
Schemes (N = number of classic computing operations, H = length of
trajectories, Q = number of quantum computing operations)

Schemes |  Deep Reinforcement Learning |  Deep Generative Learning |  Quantum Reinforcement Learning
---|---|---|---
Advantages |  Efficient management of high-dimensional state and action spaces |  Improved sample quality and training stability |  Offers quantum advantage with quantum policy representation and optimization
Target Scenarios |  Decentralized resource allocation |  Long-horizon planning via trajectory synthesizing |  Centralized resource allocation
Computation and Sample Complexity |  $O(N^{2})$ |  $O(HN)$ |  $O(QN)$
Algorithms |  Deep Q-network & Deep Deterministic Policy Gradient |  Decision transformer & Decision diffuser |  Quantum-inspired RL & VQC-based Q-learning

####  III-1 Deep Reinforcement Learning

With the expanding scale of quantum networks, both the state space and action
space in the MDP formulated by RL agents enlarge correspondingly. This growth
presents a challenge in learning an optimal policy due to the “curse of
dimensionality.” Fortunately, using deep neural networks to represent complex
policies, DRL addresses this challenge to effectively manage high-dimensional
state and action spaces by integrating deep learning techniques with
traditional RL. DRL’s use of neural networks empowers agents to effectively
manage high-dimensional state and action spaces. For instance, the Deep
Q-network (DQN) algorithm, proficient in outputting discrete actions,
leverages deep neural networks for approximating Q-values in complex
environments. Conversely, the Deep Deterministic Policy Gradient (DDPG)
algorithm, suitable for continuous action spaces, employs deep neural networks
in both its actor and critic components. These capabilities significantly
reduce the computational burden and time required for optimal resource
allocation decisions in quantum networks.

####  III-2 Deep Generative Learning

With the inherent capability of generating trajectories for extended decision-
making periods, generative AI plays a pivotal role in simulating prospective
states in dynamic environments [6]. Specifically, diffusion models, a type of
generative AI, excel in optimizing complete trajectories through iterative
denoising steps. This process allows for the generation of intricate action
distributions that surpass the limitations of unimodal distributions.
Furthermore, these models demonstrate significant advantages such as enhanced
sample quality and improved training stability. In scenarios involving long-
horizon planning and data synthesis, generative models are particularly
beneficial. For example, the Decision Transformer uses a Generative Pre-
trained Transformer (GPT) architecture to predict actions autoregressively,
based on the desired rewards, past states, and actions. Therefore, it can
generate trajectories that match desired returns on RL tasks based on GPT’s
generative capabilities. DRL agents can extrapolate and generate higher
returns than the maximum episode return available in the dataset, showcasing
their ability to generate novel and optimized actions. In addition, the
Decision Diffuser is a conditional generative model for sequential decision-
making that simplifies the decision-making pipeline by framing it as
conditional generative modeling, eliminating the need for reinforcement
learning. It outperforms existing offline RL approaches on standard benchmarks
by sampling for high returns [6]. This allows it to generate future actions
that achieve the desired return. Generative AI data synthesizers significantly
augment the quantity and variety of training data available for DRL agents.
This expansion is critical during the policy improvement phase, where agents
rely on diverse experiences accumulated during the exploration phase. However,
it is important to note the heightened computation and sample complexity
associated with these approaches.

####  III-3 Quantum Reinforcement Learning

Beyond using DNNs to represent the policy of RL agents, quantum reinforcement
learning utilizes quantum computing to calculate the policies represented by
quantum neural networks [11]. Leveraging fundamental quantum mechanical
principles like superposition and entanglement, QNNs exhibit enhanced
generalization capabilities, offering significant improvements in policy
exploration and exploitation tasks. Furthermore, QRL introduces advantages
such as improved sample quality and training stability, particularly
beneficial in centralized quantum resource allocation for large-scale and
complex networks. With these quantum advantages, notably computational
acceleration, QRL policies attain faster convergence rates and superior
performance, characterized by reduced inference delay. Additionally, unlike
traditional RL approaches, QRL tends to exhibit lower computational and sample
complexity, making it a more efficient solution for resource allocation in
quantum computing networks.

###  III-A Challenges in Resource Allocation for Quantum Computing Networks

####  III-A1 Uncertain Fidelities of Qubits and Entangled Pairs

During the process of allocating quantum resources, e.g., qubits and entangled
pairs, to execute quantum services for users in quantum computing networks,
there can be challenges in efficiently provisioning resources due to
fluctuations in user demand and quantum circuit requirements [12]. These
fluctuations can be attributed to factors like varying computational
requirements, the specifics of quantum algorithms, and dynamic changes in
quantum network topologies. Additionally, natural noise inherent in quantum
computing and communication systems further complicates these fluctuations. A
primary source of this uncertainty is the fidelity of qubits and entangled
pairs, as it determines the reliability and accuracy of quantum states during
computation and transmission. For remote collaboration over long distances
between computing nodes, maintaining high-quality entanglement and sustained
coherence times is essential for the successful completion of distributed
quantum computing tasks. To ensure the required fidelities, strategies such as
allocating additional qubits for quantum error correction techniques and
employing entanglement purification protocols with additional entangled pairs
are implemented.

####  III-A2 Multiple-Protocols Quantum Networks

In the current developmental phase, various small-scale experimental quantum
networks have been established, primarily focusing on testing and refining
quantum communication and computing algorithms [13]. However, the use of
disparate networking protocols in these quantum networks presents a
significant barrier to achieving global interconnectedness and
interoperability. To facilitate the realization of large-scale, interconnected
quantum networks, protocol translation policies have been proposed. These
policies aim to serve as a bridge, enabling communication and coordination
among small-scale quantum networks operating on different protocols. While
protocol translation policies are instrumental in ensuring secure and
successful interoperability between different QKD protocols, thus enhancing
network scalability, they necessitate additional quantum resources. These
resources are critical for optimizing the probability of successful
translation and minimizing the risks associated with quantum data relaying.

####  III-A3 Heterogeneous Quantum Networks

The heterogeneous quantum networks comprise a diverse array of quantum nodes,
such as terrestrial nodes, satellite nodes, and unmanned aerial vehicle (UAV)
nodes, interconnected by various quantum links, such as fiber links and free-
space links [14]. These networks not only significantly extend the coverage
range of quantum communication but also enhance the practical usability and
adaptability of quantum applications, such as QKD and entanglement
distribution, across varied environments. However, the mobility of satellite
and UAV nodes introduces greater complexity and uncertainty in resource
allocation compared to more static terrestrial networks, primarily due to
dynamic network topologies and variable link stability. To ensure seamless
coverage and integration across space-based, aerial, and terrestrial segments,
sophisticated resource allocation frameworks are essential. These frameworks
should be capable of enhancing real-time data transmission, enabling flexible
and adaptive satellite- and UAV-based QKD services, and employing a universal
provisioning model to optimize resource utilization and minimize operational
costs of the QKD services.

##  IV Use Case: Multi-agent Reinforcement Learning-based Resource Allocation
for Quantum Generative Learning

In heterogeneous quantum computing networks, comprising mobile, edge, and
cloud nodes, a collaborative framework is essential to deliver a variety of
quantum services. As a notable service, quantum-generative learning can
generate content with quantum-generative adversarial networks and compress
information through quantum autoencoders. This technique is a quantum analog
of classical autoencoders, known for compressing and then reconstructing input
data. In quantum generative learning, the quantum autoencoder efficiently
processes complex quantum data, offering advantages in speed and
dimensionality over classical counterparts. This process, however, is
computationally demanding, necessitating extensive collaboration among the
varied computing nodes within the quantum network. Given the diversity of
these nodes in terms of computational power and functionality, allocating
resources optimally poses a significant challenge. Centralized approaches to
resource allocation are impractical due to the complexity and scalability
issues associated with managing such a diverse and dynamic network [1].
Therefore, a decentralized approach as shown in Fig. 2, possibly utilizing
multi-agent reinforcement learning (MARL), is better suited. In this context,
each agent (node) learns to make decisions about resource allocation based on
the network’s current state and its own capabilities, leading to a more
efficient and better scalable solution for supporting quantum generative
learning applications.

Figure 2: The intelligent resource allocation framework with discrete and
continuous action spaces.

In the multi-agent scenario for resource allocation in quantum generative
learning, each agent navigates a complex environment, modeled as a Partially
Observable Markov Decision Process (POMDP), which is modeled for decision-
making problems where the agent has incomplete knowledge about the
environment’s state. In this context, each agent’s observations include local
service statuses and computing capacities, providing limited but crucial
information for decision-making. Each agent employs a policy, i.e., a strategy
or set of rules, to make resource allocation decisions. These decisions
involve determining whether to offload quantum generative learning tasks to
either edge or cloud nodes and deciding the proportion of tasks to offload.
This offloading strategy is critical as it affects the computational load
distribution and overall network efficiency. The reward signal for each agent
is the global resource cost, incentivizing agents to find cost-effective
resource allocation strategies. The experiences from these interactions are
stored in replay buffers, which are used to refine and improve the agents’
policies over time. Furthermore, DRL agents learn from past experiences in
replay buffers, aiding in the optimization of future decisions. The primary
objective of each agent is to minimize the long-term expected cost within the
quantum computing networks which involves balancing immediate rewards with
future gains. By optimizing their policies, the agents aim to achieve
efficient resource allocation in the dynamic environment of quantum computing
networks.

In quantum computing networks, each computing node can run quantum generative
learning applications, such as quantum autoencoders [15]. Quantum autoencoders
are a type of quantum circuit designed to compress quantum states onto fewer
qubits while preserving the information from the initial state. In this way,
the dimensionality of the information can be reduced while the fidelity is
maintained. For each quantum autoencoder, there is an input layer, a
bottleneck layer for compression, and an output layer for reconstruction. For
instance, to compress n qubits into k qubits using a quantum autoencoder,
there are n qubits for the input state, n-k qubits for the reference state,
and one auxiliary qubit. Therefore, the total number of qubits required would
be 2n-k+1 qubits. In the experiment, each computing node executes the quantum
autoencoders to compress information from [6,9] qubits to [3,5] qubits. In the
networks, the number of mobile nodes is set to 10, the number of edge nodes is
set to 5, and there is one cloud node. The cost is calculated based on the
execution latency and energy consumption according to the quality of service
(QoS) settings. We leverage multi-agent reinforcement learning algorithms to
allow decentralized decision-making among computing nodes. As we can observe
from Fig. 3, the proposed MARL algorithm can converge the optimal policy for
resource allocation in heterogeneous networks. Compared to the random policy,
the cost can be reduced by at least 80% under different QoS settings.
Therefore, the proposed algorithm can effectively reduce the system cost by
allocating quantum resources properly for running quantum generative learning
applications.

Figure 3: The convergence of proposed multi-agent RL algorithms under
different QoS settings, where d is the weight of latency and e is the weight
of energy consumption.

##  V Conclusions

In this article, we have proposed an intelligent resource allocation framework
for quantum computing networks with uncertain quantum resources and service
demands. We have discussed several services in quantum computing networks for
improving the security and sustainability of existing networking and computing
infrastructure. Furthermore, to improve the scalability of quantum computing
networks, we have proposed an intelligent resource allocation framework, which
can leverage RL agents, from generative learning to quantum learning, to model
the environments and learn the optimal allocation policy without prior
knowledge. Finally, we have designed a use case of quantum generative learning
service provisioning in heterogeneous quantum computing networks and proposed
a MARL algorithm to learn the optimal policy.

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# Intertwining Communities:
Exploring Libraries that Cross Software Ecosystems

Kanchanok Kannee, Raula Gaikovina Kula, Supatsara Wattanakriengkrai, Kenichi
Matsumoto
Nara Institute of Science and Technology, Japan
Email<EMAIL_ADDRESS>{raula-k, wattanakri.supatsara.ws3,
<EMAIL_ADDRESS>
###### Abstract.

Using libraries in applications has helped developers reduce the costs of
reinventing already existing code. However, an increase in diverse technology
stacks and third-party library usage has led developers to inevitably switch
technologies and search for similar libraries implemented in the new
technology. To assist with searching for these replacement libraries,
maintainers have started to release their libraries to multiple ecosystems.
Our goal is to explore the extent to which these libraries are intertwined
between ecosystems. We perform a large-scale empirical study of 1.1 million
libraries from five different software ecosystems, i.e., PyPI, CRAN, Maven,
RubyGems, and NPM, to identify 4,146 GitHub repositories. As a starting point,
insights from the study raise implications for library maintainers, users,
contributors, and researchers into understanding how these different
ecosystems are becoming more intertwined with each other.

Software Ecosystems, Open Source Libraries

††conference: MSR ’23: Proceedings of the 20th International Conference on
Mining Software Repositories; May 15-16, 2023; Melbourne, Australia

##  1\. Introduction

Popular use of third-party libraries has become prominent in contemporary
software engineering (Kula et al., 2018), which is evident by the emergence of
different library repositories like NPM, PyPI, CRAN, Maven, and so on. These
massive repositories also depend on each other, thus forming a complex
software ecosystem of dependencies.

For various reasons, a developer may realize that a library used in their
applications requires replacement, either due to the availability of newer
versions that fix defects, patch vulnerabilities, and enhance features. In
such cases, the developer seeks an appropriate replacement and has led to
various efforts in library recommendation (Cossette and Walker, 2012).
However, when faced to switch programming language, the options may become
limited. With an increase of the diversity in technology stacks and third-
party library usage (Islam et al., 2021), developers eventually will face the
need to switch a programming language and their subsequent libraries specific
to that language (Chen et al., 2016a, b; Teyton et al., 2013).

(a) @bokeh/bokehjs

(b) bokeh

Figure 1. Our example of the Bokeh library, released to the PyPI (Bokeh) and
NPM (BokehJS) ecosystems

To facilitate the movement from one programming language to another, a library
may release a version that is specific for that library ecosystem, thus the
library maintainers decide to create a library that intertwines two or more
ecosystems. Figure 1 depicts a library that serves two different ecosystems
(cf. Figure 1a and Figure 1b). The library is Bokeh (bok, [n.d.]b), which is a
popular interactive visualization library for modern web browsers. The
official GitHub repository that hosts the common repository, while there are
two versions of the library hosted on both the official PyPI(bok, [n.d.]a) and
NPM registry (bok, [n.d.]c). By 2022, the Python release of Bokeh had over
2.94K dependent repositories, and 590 other libraries in the ecosystem that
rely on this library. It has 120 releases and was first released on October
25th, 2013. The GitHub repository that hosts the cross-ecosystem library is
mainly implemented in Python (i.e., 56.7%) and TypeScript (i.e., 41.1%).
According to its homepage, BokehJS is written primarily in TypeScript and in
JavaScript. Furthermore, Bokeh has attracted 603 contributors to its GitHub
repository.

In this short paper, we conduct a large scale quantitative study to explore
the extent to which these cross-ecosystem libraries are intertwined within
these ecosystems. Since prior work (Valiev et al., 2018) shows that the
ecosystem plays an important role in the sustained activities of a software
project, we explore the extent to which these libraries may require
involvement from multiple ecosystems. Our study complements the research
conducted by Constantinou et al. (Constantinou et al., 2018) which
investigated the presence and characteristics of cross-ecosystem libraries in
twelve software distributions. We mine five of the most popular and widely
adopted library ecosystems (i.e., CRAN, Maven, PyPI, RubyGems, and NPM). Our
study is a large-scale quantitative analysis of 1,110,059 libraries to
identify 4,146 GitHub repositories with 567,864 contributors, asking two
research questions:

  * •

(RQ1) _How dependent is the ecosystem to a cross-ecosystem library release?_
Results: We find that cross-ecosystem libraries belong to four out of the
seven ecosystem pairs are depended upon (i.e., NPM, Maven, PyPI and RubyGems).

  * •

(RQ2) _What percent of contributors to a cross-ecosystem library repository
are from different ecosystems?_
Results: A majority (median of 37.5%) of contributors come from a single
ecosystem, while a significant portion of contributors do not belong to any of
those two ecosystems (median of 24.06%).

The results reveal that communities do reach beyond the boundaries of a single
programming language. We make our dataset available which is a large
quantitative study that covers over 1.1 million libraries, and 500 thousand
contributors. The second contribution is a replication package with all data
and scripts available at https://doi.org/10.5281/zenodo.6524901.

##  2\. Data Preparation

### Target Package Ecosystems

We selected five popular and well-studied software ecosystems (Decan et al.,
2016; César et al., 2021; Decan et al., 2019). NPM is a package manager for
the JavaScript programming language that was recently purchased by Microsoft
via GitHub on March 16, 2020(npm, [n.d.]). PyPI is the library ecosystem that
serves the Python programming language, which is interpreted as a high-level
general-purpose programming language. CRAN is the library ecosystem that
serves the R programming language, which is a free software environment for
statistical computing and graphics (RTh, [n.d.]). Maven is the library
ecosystem that serves the Java programming language, which is a general-
purpose programming language that follows the object-oriented programming
paradigm and can be used for desktop, web, mobile, and enterprise
applications(Jav, [n.d.]). RubyGems is the library ecosystem that serves the
Ruby programming language, which is a dynamic, open-source programming
functional programming language with a focus on simplicity and
productivity(Rub, [n.d.]).

Table 1. Overview of our dataset # Lib. Releases | As of 12 Jan 2020
---|---
with GitHub Repo URL | initial libraries
NPM | 818,787 | 2,357,829
PyPI | 138,001 | 420,350
CRAN | 5,551 | 21,526
Maven | 36,762 | 456,756
RubyGems | 110,958 | 176,987
Total | 1,110,023 | 3,433,448
# pairs | 4,146

Figure 2. 4,146 libraries that cross five ecosystems.  Table 2. Contributors from the 1,110,023 libraries | As of | March 2022 |
---|---|---|---
| Median | Max | Total
# Contrib. |  |  |
per paired library. | 8 | 1,727 | 49,674
per lib. | 4 | 16,606 | 567,864

### Detection Method

To identify a library that crosses multiple ecosystems, we use the GitHub
library repository as the linking heuristic. For instance, as shown in our
motivating example, the GitHub library repository URL serves as a link between
two analogical libraries. Our assumption is that GitHub should be the common
platform by which these libraries host their source code. We also use this
filter as a quality sanity check. Similar to prior work (Zerouali et al.,
2019; Decan et al., 2019; Alfadel et al., 2021), we then queried the
Libraries.io dataset for library ecosystems that indeed listed a GitHub
repository URL as their library repository. For each library ecosystem, we
collect a list of all libraries from the Libraries.io dataset(Lib, [n.d.]). We
mined the dataset version (1.6.0)(Katz, 2020). Once we were able to collect
the list of libraries that hosted their library repository on GitHub, we then
proceed to cross-reference between libraries that are hosted in different
library ecosystems.

Table 1 shows a summary of the extracted 1,110,023 GitHub repositories, while
Figure 2 depicts GitHub repositories belonging to libraries that are released
to multiple ecosystems. We notice from this figure that these libraries are
usually comprised of pairs (i.e., NPM $\cap$ PyPI).

Table 2 shows the summary statistics of contributors. We then mined to answer
RQ2. We use the GitHub API(Git, [n.d.]) to collect all contributors that made
commits to all repositories. We use the API request
https://api.github.com/repos/$\\{$owner$\\}$/$\\{$repo$\\}$/commits to collect
all the commit information. After collecting all contributors for each
library, we then merged the contributors’ list based on the ecosystem, so that
we have a merged list of contributors. We classified a contributor as
belonging to an ecosystem if they made commits to at least two or more
libraries. Due to compliance with GitHub API terms of usage, we slowly
downloaded this information for two months to collect all contributor
information.

##  3\. Dependence on the Ecosystem (RQ1)

Table 3. Summary statistics of the # of Dependents, showing statistical differences including significance (Sig.) the libraries with multiple ecosystems (Eco. Pairs) are more dependent than regular libraries. | # Dependents |
---|---|---
| Mean | Median |
Ecosystem | Eco. Pairs | Regular | Eco. Pair | Regular | Sig.
| 2664.71 | 761.39 | 53 | 1 | * S
NPM PyPI | 405.35 | 29.43 | 26 | 0 | -
| 2884.94 | 761.39 | 71 | 1 | -
NPM Maven | 75.06 | 59.35 | 5 | 0 | * N
| 4003.08 | 761.39 | 19 | 1 | -
NPM RubyGems | 2291.94 | 335.90 | 16 | 0 | -
| 21.43 | 19 | 12 | 0 | -
CRAN PyPI | - | - | - | - | -
| 94.21 | 59.35 | 20 | 0 | -
Maven PyPI | 91.42 | 29.43 | 23 | 0 | -
| 269.47 | 59.35 | 34 | 0 | * S
Maven RubyGems | 1104.91 | 335.90 | 56 | 0 | * S
| 132.66 | 29.43 | 7 | 0 | * S
PyPI RubyGems | 569.63 | 335.90 | 8 | 0 | * S
| The effect sizes level: N(negligible), and S(small)
---
:p-value $<$ 0.05

### Approach

To answer RQ1, we collected library dependents, which is the number of other
libraries in the same ecosystem that declares this library as a dependency.
Note that for the analysis, we compare against a pair of ecosystems (e.g., NPM
$\cap$ PyPI), resulting in two dependency scores. For example, Font-
Awesome(For, [n.d.]) is one of the top open-source libraries on GitHub, used
by millions of designers, developers, and content creators. In this case, the
library has a dependency score for both the PyPI (i.e., 29 dependents) and
Maven (i.e., 22 dependents). For evaluation, we compare the libraries that
pair different ecosystems to a baseline (i.e., other regular libraries) and
report the statistical summary (i.e., mean, median).

To statistically validate our results, we use the Mann-Whitney U test (Mann
and Whitney, 1947), (Wilcoxon, 1945), which is a non-parametric statistical
test. To show the power of differences between metrics from cross-ecosystem
libraries and regular libraries (i.e., libraries belong to one ecosystem), we
investigate the effect size using Cliff’s $\delta$, which is a non-parametric
effect size measure (Romano et al., 2006). The interpretation of Cliff’s
$\delta$ is shown as follows: (1) $\delta$ $<$ 0.147 as Negligible, (2) 0.147
$\leq$ $\delta$ $<$ 0.33 as Small, (3) 0.33 $\leq$ $\delta$ $<$ 0.474 as
Medium, or (4) 0.474 $\leq$ $\delta$ as Large. To analyze Cliff’s $\delta$, we
use the cliffsDelta package(nei, [n.d.]).

### Results

Table 3 shows the evidence that the cross-ecosystem library is highly
dependent upon by the multiple ecosystems. Results show certain pairings with
NPM and RubyGems have a large pairing. Statistically, we find that the number
of dependents against the baseline of regular libraries are significantly
different (p-value $<$ 0.05), with a negligible to small association as the
reported effect size for different combinations. We note that negligible and
small effect sizes are noticeably smaller but not so small as to be trivial
according to statistical analysis. Further research needs to examine how
various factors, such as the size of the ecosystem, contribute to the small or
negligible statistical variances.

Summary for RQ1: Four out of the seven ecosystem pairs of libraries depended
on cross ecosystem libraries. (i.e., NPM, Maven, PyPI and RubyGems)

##  4\. Distribution of Contributors (RQ2)

### Approach

To answer RQ2, we investigate whether a cross-ecosystem library attracts
contributions from the targeted ecosystems. For data collection, we first
collected all contributors from the 1,110,059 projects. For our analysis, we
calculated a percentage of three types of contributors of each cross-ecosystem
library:

  * •

Both (%) refers to the percentage of contributors that have made prior
contributions to libraries that belong to both ecosystems.

  * •

Single (%) refers to the percentage of contributors that have made prior
contributions to a single ecosystem.

  * •

Independent (%) refers to the percentage of contributors who do not have any
contributions to any ecosystem libraries.

Since we are dealing with a large dataset, we used the PySpark(PyS, [n.d.])
arrays to handle the data processing tasks of classifying and and merging
contributors into these three groups. To statistically confirm our
classification of contributors, we use McNemar’s Chi-Square test (McNemar,
1947). This is a non-parametric statistical test used to find the change in
proportion for paired data. We test the null hypothesis that ‘the percentage
of contributions classified is the same’ We also measure the effect size using
Cohen’s $d$, a non-parametric effect size measure by (Cohen, 2013). Effect
size is analyzed as follows: (1) $d<0.2$ as Negligible, (2) $0.2\leq d<0.5$ as
Small, (3) $0.5\leq d<0.8$ as Medium, or (4) $0.8\leq d$ as Large.

### Results

From Figure 3 we make two observations. First, we find that there is a higher
percentage of contribution (a median of 37.5%) that is supported by only one
ecosystem. (see Figure 3). It is important to note that our analysis does not
identify the original ecosystem. The second observation is that a significant
portion of contributors (median of 24.06%) that do not belong to either
ecosystem. The result shows that these contributors may be just specific to
the project itself. This is more than contributors that belong to both
ecosystems (median of 20%).

Table 4 shows statistical comparison between groups of contributors. We find
significant differences between (p-value $<$ 0.05) where a library that has a
higher percentage of single ecosystem contributors will have a lower
percentage of contributors from both ecosystems.

Figure 3. Answering RQ2, we show how contributions for a cross-ecosystem
library originate from a single ecosystem. Table 4. Statistical significance
test results related to RQ2.  Contributions (%)
---
Single-Both * | S
- | -
Both-Neither * | N
| The effect sizes level: small(S) and negligible(N)
---
:p-value <0.05

Summary for RQ2: A majority (median of 37.5%) of contributors come from a
single ecosystem, while a significant portion of contributors do not belong to
any of those two ecosystems (median of 24.06%).

##  5\. Limitations

A key threat in the construct validity exists in the matching approach using
the GitHub URL, which reduces our study to GitHub projects. Also, results are
limited to the five ecosystems: NPM, PyPI, CRAN, Maven, and RubyGems, which
threatens the generalization of our claims to other ecosystems, and is seen as
immediate future work.

##  6\. Research Agenda

As an alternative to replacement libraries, our results show promising results
on the phenomenon of releasing a library to multiple ecosystems. We summarize
three main questions below:

### Should maintainers consider releasing to
multiple ecosystems?

Results from RQ1 highlight that not only this phenomenon exists, but that in
some ecosystems that library is dependent upon by the ecosystem. This does
raise the question of whether or not maintainers should consider following
suit. However, as shown in RQ2, current contributions are not shared by both,
but only a single ecosystem. Results from the study also raise questions on
how developers may need to be proficient in multiple programming languages?
For future work, there needs to be a qualitative analysis or case studies to
gain a deeper understanding about what are the concrete benefits of opening up
to a new ecosystem (e.g., attracting contributors, increasing code quality and
finding bugs), and motivations for releasing to multiple ecosystems. This
could be done by either mining the library repositories, or by survey
interviews.

### Will this phenomenon solve the need to find replacement libraries?

From the results of RQ1, we cannot conclude the answer to this question.
However, the results from RQ1 show that the more dependent and somewhat mature
libraries are taking up this trend. Another interesting research direction is
to see the impact of these libraries on the existing libraries that already
provide these functionalities. An engaging avenue is how these libraries will
compete with already established libraries in the new ecosystem that provide
the same functionalities. This may be at the finer grain of certain libraries,
certain ecosystems, and user surveys of maintainers.

### How will cross-ecosystem libraries impact ecosystem-level topics like
governance, and management?

From a research perspective, the growing intertwining between different
ecosystems will bring forth interesting implications at the ecosystem level.
From one point of view, the results indicate that the boundaries of a
community are not limited by the programming language. However, with this
expansion, the extent of governance and management is unknown. For instance,
to what extent do these libraries abide by the specific rules and regulations
that are enforced by each ecosystem? And how are bug fixes and specific
security vulnerabilities propagated through different ecosystems? Since RQ2
states that their libraries are more likely to receive contributions from the
original ecosystem, does that mean that the boundaries between these two
ecosystems become closer?

## Acknowledgement

This work is supported by Japanese Society for the Promotion of Science (JSPS)
KAKENHI Grant Numbers 20K19774 and 20H05706.

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# Lessons from Digital India for the Right to Internet Access

Kaustubh D. Dhole<EMAIL_ADDRESS>Department of Computer
ScienceEmory UniversityP.O. Box 1212AtlantaGeorgiaUSA43017-6221

(2018)

###### Abstract.

With only 65% of Indian houses having access to the Internet, digital India
faces a significant Internet divide across gender and city types. Rendering
essential services inaccessible to almost a third of the population
necessitates not only provisioning a fundamental right to Internet access but
taking specific constructive steps to assure its simple, affordable and safe
accessibility. Establishing such a right would also pave way for other
ancillary rights required for data privacy, protection from Internet’s
possible harms and the requirement to be treated fairly. We first discuss two
arguments on the universal right to Internet access; from Merten Reglitz, a
senior lecturer on Global Ethics and from Vincent Cerf, one of the founding
creators of the Internet who has had a profound influence on the field. We
specifically argue why Internet access should be treated as a fundamental
right. We discuss the learnings from India, contextualizing them with the
global debate and argue for establishing Internet access as a fundamental
right in India and elsewhere in the form of government legislation to
eliminate Internet divide.

Internet, fundamental right, fairness

††copyright: acmcopyright††journalyear: 2018††doi:
XXXXXXX.XXXXXXX††conference: CSCW; November, 2022; Situating Network
Infrastructure with People, Practices, and Beyond††price: 15.00††isbn:
978-1-4503-XXXX-X/18/06††ccs: Information systems World Wide Web††ccs: Social
and professional topics Computing / technology policy

##  1\. Introduction

Many of India’s digital plans including vaccine distribution, online
education, and socio-economic welfare premised on the assumption of widespread
and uniform Internet connectivity with high average bandwidth. Online payments
were meant to be simple to switch to after India’s demonetisation. However,
with only 65% of Indian houses having access to the Internet, digital India
faced a significant Internet divide along various lines including gender and
city types (Figure 1). This renders essential services inaccessible to almost
a third of the population and motivates both establishing a right to Internet
access and taking specific constructive steps to assure its easy
accessibility. Establishing such a right would also pave the way for other
auxiliary rights required for data privacy, protection from Internet’s
possible harms, and the requirement to be treated fairly. Due to our society’s
shift towards digitisation and the accompanying dependence on the Internet, it
is crucial to not just acknowledge the significance of the Internet but also
to campaign for universal access to it in India as well as across the world.

Figure 1. Gender & City wise Internet usage rate in India between 2019 and
2021 (Basuroy, 2021)

Beyond India, Internet divides manifest in most parts of the world, especially
in regions which are highly populated but partially digitised. However,
India’s case is interesting and globally relevant for a variety of reasons.
First, India is a functioning democracy with independent judicial, executive
and legislative organs, and hence many of India’s domestic implications can be
generalised to many other democratic nations. Second, India has both, a large
user base with Internet access as well as one without. This contrast provides
diverse use cases to study such as large populations missing out on government
schemes to large populations accessing affordable Internet. Finally, as we
shall see, numerous Indian courts have already passed judgements pertaining to
the Right to access the Internet, thereby advancing the global debate of the
right further.

Our contributions in this article are two-fold. 1) We first discuss two
arguments on the universal right to Internet access from Merten Reglitz, a
senior lecturer on Global Ethics and Vincent Cerf, one of the founding
creators of the Internet who has had a profound influence on the field. We
specifically argue why Internet access should be treated as a fundamental
right. 2) We then discuss the learnings from India, contextualising them with
the global debate and argue for establishing Internet access as a fundamental
right in India and elsewhere in the form of government legislation.

##  2\. The Larger Debate

Whether Internet access should be considered a human right or not has been
debated quite extensively in the last couple of years. Apart from large
national and international organizations like the United Nations (unh, 2021),
Merten Reglitz (Reglitz, 2020), Tim Berners Lee(Berners-lee and Halpin, 2018)
and Vincent Cerf (Cerf, 2017) were among the forefront of this debate each
providing unique perspectives. Let us consider the thoughts of Reglitz and
Cerf first which positioned themselves on the opposite sides of the debate.

###  2.1. Reglitz’s arguments for the Right to Internet Access

Reglitz (2020) argues that Internet access should be considered a universal
and moral human right and, everyone should have unsupervised and uncensored
access to it. He urges for it to be made available publicly free of charge to
those who cannot afford it. Internet access, according to Regtlitz, is more
than mere luxury or special privilege. It is necessary to lead a minimally
decent life and hence it should be considered a universal entitlement.

Importantly, he reasons the Internet to be pragmatically essential to protect
and promote other well-established and uncontroversial basic human rights like
free speech and free assembly. Additionally, the Internet is key to advance
democracy for example by its potential to hold global players accountable. The
level of impact is such that once Internet access is widely available, a lack
of access undesirably excludes people from having meaningful political
influence. As Reglitz puts it, “Without such access, many people lack a
meaningful way to influence and hold accountable supranational rule-makers and
institutions. These individuals simply don’t have a say in the making of the
rules they must obey and which shape their life chances.” There have been a
plethora of studies which measure the impact of internet access on political
participation. For example, Zhuravskaya et al. (2020)’s study found that free
Internet (and social media) do enhance accountability by informing the public
and helping the coordination of protests in places where the main public
concerns are related to corruption, subversion of power, and autocratic
control of conventional media.Shandler et al. (2020)’s early findings
indicated that the absence of Internet connection considerably reduced task
completion for activities related to political expression and political
association and conditionally affected task completion for freedom of
information related activities. Why wouldn’t then, a lack of internet access
itself make for a strong argument for Internet access to be deemed a right?

Besides, the setup costs are not extremely burdensome according to Reglitz -
accessing politically vital opportunities on the Internet like blogging and
news does not require sophisticated networks such as 5G or glass fibre
connections. We will see in the next sections how an Indian telecommunications
firm provided Internet access at extremely affordable rates. While there are
strong concerns of misuses of the internet, the wide applicability can itself
serve as a solution to contain many of them.

###  2.2. Cerf’s arguments against the Right to Internet Access

Cerf (2017), on the contrary, argues that besides having a high bar for
something to be considered as a human right, it should also be fundamental
enough, like the things we as humans need in order to lead healthy, meaningful
lives, like freedom from torture or freedom of conscience. According to Cerf,
the Internet does not pass the high bar to qualify as a human right and is not
generic or fundamental enough. Cerf argues that, at some point in the past, it
was hard to make a living without horses, but we recognized the right to the
generic phenomena of “making a living” rather than the right to have a horse.
As per Cerf, the Internet acts more as a valuable means for critical freedoms
of speech and access to information but is not an end in itself. When we
accept that telephone access must be available everywhere by government policy
i.e. as a civil right and not necessarily as a human right, which the UN
conflates, Cerf asserts that we analogously must consider the same for
Internet access too.

###  2.3. Problems with Cerf’s arguments

The Internet is vast and multidimensional and far from being an analog of a
horse. Cerf’s analogy to compare the Internet with the horse is a bit
misleading. The horse is an extremely specific object which pursues precise
goals of say, faster transportation (in the context of old times). The closest
Internet counterpart today, albeit in hindsight, probably is a specific
website and not the whole Internet, which is clearly more multi-dimensional
and a not a mere efficiency enhancing technology. Besides hosting multifarious
webpages, it enables access to a plethora of even other technologies as well
as exponentially faster communication, smooth reach and influence in cultural
and socio-political circles. So, it will doubtlessly render the unreached
segment of the population handicapped. Cerf’s insisting on the Internet as
just another means treats the Internet as a minor increment over other
traditional means like a pen or a physical newspaper and ignores the
exponential rapidity, enormity and influence of it.

###  2.4. From the lens of Moral philosophy

Deontology: 18th century German philosopher Immanuel Kant, was famous for his
conceptions of deontology. He defined the Categorical Imperative as a
commandment or moral code that everyone must follow regardless of their
desires or extenuating circumstances. Kant’s categorical imperative looked at
the will or intention motivating an action to judge the action’s morality.

Rule Utilitarianism: On the other hand, rule utilitarianism, another line of
consequentialist ethical theory, decided that an action was morally right if
it could conform to a rule that would lead to the greatest good. Rule
utilitarianism judged an action on the basis of its consequences.

So, can we further take these arguments from moral philosophy to make a clear
case whether there should or shouldn’t be a human right to Internet access?
Kant’s Categorical Imperatives are principles which are intrinsically valid
and should be obeyed in all circumstances. Kant looks at the will motivating
the action. It’s not clear if we can compartmentalize the intention of
providing internet access into exactly good or bad - The Internet could be
used for both, say for performing an online charity fundraiser as well as for
promoting genocide. Kant would probably not have been in agreement to vouch
for a universal right where the intentions seem hazy.

However, the consequences of providing the right seem to be alluding towards a
more decisive picture as compared to the intentions. Reglitz and Cerf both
laud the positive impact that Internet has had on people’s lives. Besides, not
provisioning a right has had a plethora of negative consequences too. Rule
utilitarianism, would find strong reasons to equate the provision of the right
to Internet access as an increase in overall happiness.

It is no doubt that Internet access has immense transformative potential and
to deny any segment of population access would be to deny them a lifestyle
needed to survive a minimum life.

##  3\. The Indian Context

Now that we have hopefully established that the right to Internet access is
crucial and necessary from a theoretical point of view, let us look at some of
its arguments as well as implications in a more practical setting,
particularly in the Indian context. We will see why India’s case furthers the
argument in favour of the right to Internet access. Moreover, India’s digital
endeavours treat Internet access as a civil right. The next subsections will
elaborate why such a civil right conception can be insufficient.

###  3.1. Digital Divide

As of March 2022, according to the Telecom Regulatory Authority of India
(TRAI, 2022), just close to 60 percent of India has Internet access. But more
importantly, almost 100 percent of urban population has access against 37
percent rural. Moreover, Internet access is concentrated in the top 30 cities.
With 57.1 percent of the male population and 33.3 percent of the female
population, men used the Internet more often than women in India, a whopping
gender gap of 22 percent(Basuroy, 2021). The gender gap is larger for people
who have internet access. A male Indian is twice as likely as a female Indian
to have Internet access. Such massive divides along lines of gender and city
types can easily put millions at a disadvantage.

In January 2022, 10 school students, hailing from remote villages of
Gadchiroli of the state of Maharashtra complained to the state High Court
about lack of access of Internet affecting their studies. The court probed the
Department of Telecom (DOT) to gather details. After thorough investigation,
the judges released a statement, “The DoT reply brings on record a very
disturbing picture about Gadchiroli where not less than 829 villages lack
mobile Internet connectivity. If this is the situation prevailing there, one
can very well imagine what would happen to the future of Maharashtra’s next
generation which would come from this district. No physical classes are being
held and children from these 829 villages are also unable to participate in
the digital classes”.

Such a case of digital divide would not be uncommon to witness around the
world where other rights would be sacrificed due to Internet divides.(Hampton
et al., 2020)’s survey focusing on rural broadband in 15 Michigan school
districts, discovered that children who rely solely on a cell phone for access
or who do not have access to the Internet at home perform worse on a number of
measures, including digital abilities, homework completion, and grade point
average. Additionally, they are less likely to plan to get a college or a
university degree. No wonder, Reglitz pointed out, that in such a virtual
world some forms of freedom of expression and freedom of assembly are now only
accessible via the Internet and those without access are unjustifiably
disadvantaged when exercising these rights.

###  3.2. Internet Lockdowns

With regards to Internet lockdowns, most governments have always argued to
have taken utilitarian stands i.e. with greater long-term good in mind. Indian
laws include the provision to shut down the Internet in the interest of public
safety or events of emergency. In the last 10 years, India was subjected to a
total of 682 lockdowns (India, 2022) each implemented in a different region
for different time spans and for different reasons. While some of them
exercised in the states of Assam and New Delhi were intended to curb protests
against the government’s citizenship amendment bill, some of them were
preventive in nature in anticipation of violence. Eg, terrorist activity in
parts of the state of Jammu & Kashmir would always be intensified during any
major political event involving cases of stone pelting to terrorists attacking
civilians. In August 2019, when the government was about to abrogate, a
crucial article, viz. Article 370 in the Indian constitution, the government
instituted an Internet ban across the state to prevent .

If we were to make a conceptual distinction of lockdowns using traditional
ethical methodology, it would be important to first delineate two types of
Internet lockdowns - preventive lockdowns, exercised in anticipation of
possible violence or political unrest and emergency or reactive lockdowns,
exercised to suppress political unrest or stop communication post witnessing
an event. Let us consider the case of the lockdown during the Abrogation of
Article 370. If we were to treat provisioning of access as a moral act,
deontologists might not be happy if such preventive lockdowns are permitted,
as it would seem immoral to restrict Internet access. On the contrary, a
consequentialist might argue for reducing possible violence and loss of lives
and hence, allow authorisation of such a preventive lockdown. In the cases of
reactive lockdowns, the consequences are not obscured and it would not be hard
for the deontological and consequentialist forms to come to terms. Eg. when
the computers within a network have been infected with a data stealing
malware, a temporary shutting off of Internet could be justified.

Preventive lockdowns have been a contentious issue in India. The very serious
objections of utilitarianism, are raised against the case of the Abrogation of
Article 370; Utilitarianism, specifically Rule Utilitarianism disregards
freedom and permits extremely unequal distributions of value, excusing the
pain of the disadvantaged i.e. the ones whose internet access was revoked by
higher overall advantage viz. reduced loss of lives. Nonetheless, when
Anuradha Bhasin filed a petition challenging the constitutionality of the
lockdown during the abrogation, the Supreme Court heard the case and also took
a utilitarian stand, recognizing the need to balance national security but
still gave orders to restore Internet services.

###  3.3. Arbitrary Revoking of Access

In the famous Faheema Shirin vs State of Kerala case of 2019, Faheema
Shirin(Asha, 2019), a resident at a women’s hostel challenged new regulations
at her hostel which restricted the use of mobile phones and laptops. The High
Court of Kerala declared that the restriction was an unreasonable infringement
upon the right to access the Internet, the right to privacy, and the right to
education. This case was important for a couple of reasons; first, that
Internet restrictions need not be exercised only for political benefit but
even for arbitrary reasons; and second, how other crucial rights are heavily
dependent on the Internet.

###  3.4. Derivability of Other Rights?

Less sophisticated alternatives to the Internet can be exploited to curb other
derived rights like the Right to Free Speech. It is no surprise that a lot of
other rights are intertwined with and derived from the right to Internet
Access. The connection with other rights can itself be abused for power
without a separate explicit provision. Despite the positive verdict of Faheema
Shirin’s case, it would not be hard to think of a situation in which an
Internet regulating body, revokes Internet access in order to restrict another
derived right but is not liable since less sophisticated alternatives might
exist to still fulfill the right for the purpose of bookkeeping. Even with
regards to the lockdown during the abrogation, when the Supreme Court ordered
lifting of the Internet shutdown to not restrict the Right to Free Speech, the
government quickly restored Internet services, albeit, the speed was throttled
to 2G.

The throttling during the Abrogation of Article 370 too would be viewed
differently from deontological as well as utilitarian points of view. At the
juncture when the Right to Internet Access assumes a separate right,
deontology would look at it as a conflict between the citizen’s safety i.e.
their Right to Life and the Right to Internet, despite having Internet at 2G
speeds. The utilitarian point of view would rather permit such throttling by
prioritising the Right to Life over the Right to Internet. Note that this does
not invalidate for Internet Access to deserve a special right. It in fact,
acknowledges that Internet regulations are necessary, especially when in
conflict with other rights.

It would be hard to disagree with Cerf, when he pointed out that we rather
optimize rights derived from the Internet like critical freedoms of speech and
access to information. But there is a caveat here - It can hardly happen when
we treat the Internet as “substitutable” with other alternatives or simply as
“just another means” in Cerf’s terminology. Undeniably, Internet access is
vast, unique and non-substitutable, but still not worthy of deregulation.

###  3.5. Zero Liability of Private Players

India’s Internet grew exponentially due to government efforts coupled with
private players. While private investment is what boosted India’s Internet
connectivity, it still does not exempt governments from ensuring universal
access. Private entrepreneurs and businesses can be unreliable as private
businesses would generally only invest when it makes sense economically. A
substantive government obligation would in fact put appropriate pressure for
governments to deliver without expecting monetary benefits.

##  4\. Concerns Post the Right

While we establish India’s challenges and present how those justify a separate
right to Internet Access, it is important that as a moral right we also
justify the relevant duties that its realisation would impose on others. A few
concerns have been popularly voiced against the right to Internet Access or at
least in general against Internet’s current technological form and it is
utmost critical to try to address them.

  * •

First, is it in our interest to invest and maintain large amounts of physical
resources like cables, broadband connections, etc. to provide Internet access
to the remaining half of the world today?

  * •

Second, is it also worth bearing the cost of misinformation harms, concerns of
data privacy and other harms that come with the Internet?

  * •

Third, whether the realisation of such a right would involve compromising
other rights or even undermining other efforts towards improving living
standards like electrification? Eg, Shouldn’t we first address the global
electrification divide when nearly a billion people(ene, 2022) in the world
are without electrical power?

###  4.1. Setup Costs

Cerf stated that the positive act of providing Internet access would be too
onerous while Reglitz on the other hand argued that the setup costs to have
universal Internet access are not so burdensome. The story of one
telecommunications private firm in India, Reliance Jio, providing strong
empirical substance to Reglitz claim is vital to understand. In September
2016, it entered the Indian market with data plans and handsets at nominal
rates when India’s mobile data consumption ranking was 155th. Within a span of
3 years, the company amassed nearly 370M subscribers (min, 2020) taking India
to the first place. India’s data usage per smartphone rose from 0.9GB in 2016
to 11GB per month in 2019 (sho, 2019). Jio built a 2.7 lakh kilometer optical
fibre net for a cost of $22B and offered users attractive Internet plans which
were almost 30x cheaper than its competitors, alongwith no roaming charges and
free calls. Jio was able to pull this off because once the expensive 4G
network infrastructure was setup, running the network had relatively low
operating costs.

Only 4G: When most other telecommunication operators in India were focused on
developing 3G in 2010, Jio made a rather audacious bet on 4G. Today, Jio
operates a network that is exclusively 4G/LTE, giving it an advantage over its
competitors. Jio simply needs to manage one network - the one for data;
whereas others need to manage a network for voice as well as one for data.
Compared to its competitors, Jio requires far less physical equipments.
Additionally, the voice networks that others must manage use outdated
technology, consume more energy, and take up more space. This significantly
raises their cost, which Jio completely avoids(jio, 2020).

Low-Cost Phone Calls: Jio could practically offer phone calls for free due to
their low network utilization. They do not incur any additional costs for
managing a separate voice network. Additionally, unlike high data content like
HD videos, phone conversations produce extremely few packets that need to be
sent. Thus, they are able to offer unimaginably cheap plans(jio, 2020).

Customer Retention: Jio’s strategy was always to draw customers in with
affordable data, then hook them with entertainment apps like JioSaavn for
music, JioCinema for movies, JioNews for news, etc. And finally, sell these
users important services like internet shopping, education, healthcare, etc.
to earn money off of them(jio, 2020).

Thanks to inexpensive Jio phones, even people with modest means, such as
farmers, daily wage workers and low-income households, had access to the
Internet.

###  4.2. Misinformation Harms

Providing a right would contextualise Internet usage within a legal framework
and grant powers to penalise bad actors. The Internet no doubt has a plethora
of harms as well as benefits. The question is whether provisioning a right
would promote bad actors and the consequences thereafter? To answer these
would require ensuring that Internet access is well protected and there are
legal provisions in place to be able to provide a safe environment on the
Internet. With the development of a right, other auxiliary rights such as data
protection would be necessitated. Protecting the right to Internet access
would imply subsequent data protection and data privacy legislations. But from
a technological point of view, the possibility of Internet harms should not
deter us from providing Internet to all. Just because rash vehicle drivers can
be harmful to the society, we do not discourage people from driving on the
road.

###  4.3. Comparison with other Rights

The third question directly delves into some of the most influential arguments
of Cerf - the need to have a high bar for something to be a human right.
Cerf’s definitional distinction of a civil right and a human right leaves no
room for the idea that human rights can change as social contexts change, and
rather subtly suggests that Internet divides would not cause significant harm.
Internet access is indeed a special right unlike other rights. It would be
unfair to have similar expectations as other rights.

Are our responsibilities for Internet access more significant than, say,
making sure everyone has access to electricity? As Chike Aguh, CEO of
EveryoneOn puts it, human rights are not inalienable(web, 2016). Prioritizing
free speech over the right to education would not be a good idea. All rights
coexist, are undeniably significant, and Internet access functions as a
primary right that makes practically all other rights possible. But that
doesn’t mean we shouldn’t give one right precedence over another. Such a
conundrum might not exist in nations with 100% electrification rates, such as
India, the United States, Germany, and France, where electrification would not
be a concern (ele, 2022a) and Internet connection would not be a potential
issue. But in places like South Sudan, Chad or Liberia (ele, 2022b),
electricity is imperative to achieve Internet access. So, while some regions
would need prioritizing one right over another, it would be impossible to make
an exclusive choice of electricity over Internet or vice versa.

##  5\. Conclusion

India’s digital journey is crucial to steer this debate in the right
direction. Studying the challenges of a digital democracy with large
populations of both, Internet haves and have nots, provides us with important
lessons weighing in favour of a human right. The children of Gadchiroli were
denied access to education when their villages did not have Internet. The
Faheema Shirin vs State of Kerala case of 2019 case providing a landmark
judgment, recognized the importance of the right and cautioned us against the
possibility of arbitrary revocation. The Anuradha Bhasin case underlined how
treating Internet as just a means rather than an end can be easily exploited
by substituting it with other less sophisticated alternatives. The cases also
emphasized the need to look at the right from a more utilitarian perspective
to be able to correctly prioritize it with other rights.

The success story of Jio providing affordable internet to nearly 300 million
people painted a rough picture of the duties that such a right would entail.

Cerf might be satisfied with India overly fulfilling its duty by provisioning
a civil right, but Reglitz might argue for more effort to eliminate digital
divide. Universal Internet access would not occur naturally without
intentional promotion but Internet divides would. The human right to Internet
access in the form of government legislation can put appropriate obligation on
governments to rule out Internet divide, have a legal system tied around the
same for safe and effective usage, and promote other derived rights.

##  6\. Acknowledgements

We would like to specifically thank Kristin Williams, Alexis Newton and Deepak
Dhole for providing thoughts and other students of the Social and Ethical
Issues in Computing class at Emory University.

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