# Toward a Universal Quantum Computing Cloud: Characteristics and Requirements

Quang Nguyen-Luong, Viet Dung Pham, Rossy Nhung Nguyen, Hung Q Nguyen, Dinh V-Trung, Asif Sharif

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Quang Nguyen-Luong, Viet Dung Pham, Rossy Nhung Nguyen, Hung Q Nguyen, Dinh V-Trung, et al.. Toward a Universal Quantum Computing Cloud: Characteristics and Requirements. IEEE Symposium on Wireless Technology and Applications (ISWTA), IEEE, Dec 2025, Penang Island, Malaysia. 10.5281/zenodo.16921160. hal-05226717v2

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# Toward a Universal Quantum Computing Cloud: Characteristics and Requirements

1st Quang, Nguyen-Luong

Department of Astrophysics

CEA Paris-Saclay

Paris, France

luongquangbmt@gmail.com

2nd Viet Dung, Pham

Instrument Department

Vietnam National Space Center

Hanoi, Vietnam

vdinventing96@gmail.com

3rd Rossy Nhung, Nguyen

QUANOVA

Vietnam National University Hanoi

Hanoi, Vietnam

rossyng@quanova.org

4th Hung Q., Nguyen

Institute for Quantum Technologies

Vietnam National University Hanoi Vietnam Academy of Science and Technology

Hanoi, Vietnam

hungngq@hus.edu.vn

5th Dinh-V-Trung

Institute of Physics

Hanoi, Vietnam

dvtrung@iop.vast.ac.vn

6th Asif Sharif

Department of Research & Development

BOLTZ.AI

Paris, France

Asharif2@gmail.com

Date of submission: 2nd Jul 2025, Date of acceptance: 20th Aug 2025, Date of current version: 2nd Sep 2025. 10.1109/ACCESS.2024.0429000

**Abstract**—Quantum computing is emerging as a powerful complement to classical computing, offering new computational paradigms across diverse quantum hardware architectures. As access to quantum resources expands, there is a need for a unified model to support interoperability, scalability, and ease of use. This paper introduces the concept of a *Universal Quantum Computing Cloud* (UQCC) and presents its essential characteristics at high levels —spanning service abstraction, virtualization, user experience, and system integration. By defining UQCC, we aim to support future development and adoption of cloud-based quantum platforms across research and industry.

***Index Terms***—cloud, cloud computing, quantum computing, quantum computing computing cloud, service oriented

## I. INTRODUCTION

QUANTUM computing has emerged as a transformative paradigm that leverages fundamental quantum principles—such as superposition, entanglement, interference, par- allelism—to tackle problems that are computationally infeasible for classical systems [1]. Over the past decade, rapid progress in quantum hardware, algorithms, and development environments has accelerated the transition of quantum com- puting from theoretical constructs to practical, cloud-delivered services [2]. Along the side of QC, cloud computing has evolved much more rapidly and reshaped how classical com- putational resources are consumed, enabling scalable, on- demand, pay-per-use access to infrastructure and platforms [3], [4]. The convergence of these domains—quantum computing and cloud computing—has led to the emergence of *quantum computing cloud*: a model where users remotely execute quantum algorithms on real quantum processors via cloud interfaces [5].

Early demonstrations of this concept validated its feasibility. Devitt [6] showed that quantum computing experiments could be performed in the IBM Quantum cloud, paving the way for democratized access to quantum resources and broader partic- ipation from academia and industry. More recently, Grossi et al. [7] proposed a serverless architecture to further abstract quantum workloads from backend complexities, underscor- ing the importance of modularity, elasticity, and developer- friendly tooling in future quantum cloud ecosystems. A re- cent comprehensive survey by Nguyen et al. [18] highlights the evolution of quantum cloud platforms, taxonomies of service models, and critical challenges including hardware heterogeneity, limited standardization, and the absence of a unified abstraction framework. Their work emphasizes that future quantum cloud services must overcome these limitations by providing transparent, architecture-agnostic access across devices and programming paradigms.

To address this need, we introduce the concept of a *Universal Quantum Computing Cloud* (UQCC)—a next-generation framework designed to unify the access, control, and orchest- ration of diverse quantum computing systems under a single cloud-native infrastructure. In this paper, after discussing dif- ferent types of quantum processing unit (QPU) in Section II, we formally define UQCC in Section III, describe levels of service abstraction in Section IV, outline its essential charac- teristics in Section V, and position it within the Gartner Hype Cycle in Section VI. This work aims to lay the foundation for future research, development, and deployment of interoperable quantum cloud ecosystems.

## II. RELATED WORK

The fast advancement of quantum computing has prompted a parallel development of cloud-based quantum services. Early foundational efforts such as IBM Quantum Experience, now rebranded as IBM Quantum Platform, and Microsoft’s Azure Quantum Computing have demonstrated the viability of re-

<footnote>We were partly supported by a grant from the Simons Foundation (916424, N.H.) in addition to the enthusiastic support of IFIRSE/ICISE staff. Dinh-V-Trung acknowledge grant.</footnote>
<table><caption>TABLE I
COMPARISON OF QUANTUM QUBIT TECHNOLOGIES</caption><thead><tr><td>Qubits Types</td><td>Where the Quantum Information is Stored</td><td>Example Companies</td><td>Status</td><td>Ref</td></tr></thead><tbody><tr><td>Nuclear Magnetic Resonance (NMR)</td><td>Spins of atomic nuclei manipulated using radio-frequency pulses</td><td>EPFL</td><td>First demonstration of an actual quantum computer but it is not scalable beyond ~10 qubits</td><td>[8]</td></tr><tr><td>Superconducting qubits</td><td>Energy eigenstates of Josephson junction-based electronic resonant circuits</td><td>IBM, Google, IQM, Rigetti</td><td>Most mature, with 100+ qubit systems</td><td>[9]</td></tr><tr><td>Trapped-ion qubits</td><td>Ions trapped in electromagnetic fields; qubit is encoded in internal states of ions</td><td>IonQ, Quantinuum (Honeywell), Alpine Quantum</td><td>High fidelity gates; slower but very accurate</td><td>[10]</td></tr><tr><td>Photonics</td><td>Uses polarization, path, or time-bin encoding of photons</td><td>Xanadu, PsiQuantum, Quan-dela, Orca computing</td><td>Room temperature; good for networking, photon loss and scaling are issues</td><td>[11]</td></tr><tr><td>Quantum dot qubits</td><td>Electron spins confined in semiconductor nanostructures</td><td>Intel, TU Delft</td><td>Promising for integration with classical semiconductor tech</td><td>[12]</td></tr><tr><td>Topological qubits</td><td>Qubits encoded in Majorana fermions; immune to local noise</td><td>Microsoft</td><td>Experimental physics ongoing, high potential for fault-tolerant qubits</td><td>[13]</td></tr><tr><td>Neutral atom qubits</td><td>Qubits are encoded in the quantum states of individual neutral atoms (such as rubidium or cesium) that are trapped and controlled using arrays of optical tweezers or lattices.</td><td>Atom Computing, QuEra, Pasqal</td><td>It is feasible to trap and re-arrange hundreds or thousands of atoms in scalable 2D arrays. and Rydberg interactions allow entanglement between non-neighboring qubits. It is suitable for both digital gate-based quantum computing and analog quantum simulation.</td><td>[14]</td></tr><tr><td>Diamond NV center qubits</td><td>Spin states of nitrogen-vacancy (NV) centers in diamond</td><td>Quantum Brilliance</td><td>Mostly for sensing; some use in computing</td><td>[15]</td></tr><tr><td>Donor atom qubits (e.g., Phosphorus in Silicon)</td><td>Spins of single atoms doped into silicon</td><td>Silicon Quantum Computing</td><td>Long coherence times; ultra-small footprint; hard to control and scale</td><td>[16]</td></tr><tr><td>Optomechanical qubits/Hybrid qubits</td><td>Combines mechanical vibrations with optical or microwave control for qubit encoding</td><td>Yale Quantum Institute</td><td>Research stage; potential use in memory and interfacing</td><td>[17]</td></tr></tbody></table>

motely accessible quantum resources. These platforms offer limited universality and interoperability but serve as precursors to the more comprehensive vision of UQCC.

Several papers have explored the architecture and performance of individual quantum cloud platforms [5], [6] and an up-to-date survey of all available quantum computing clouds is present in [18]. However, few works attempt a holistic definition or characterization of a truly universal quantum cloud system. Contrarily, since its commencement around 2017, classical cloud computing has been rapidly developed and it reached the level of productivity in only 8 years, as we will see in Sect. VI. Cloud computing introduced a transformative paradigm in which users access computing resources hosted on remote infrastructures, typically without needing to know the physical location of the hardware [3], [19].

To the best of our knowledge, this paper presents the first formal definition of a UQCC, synthesizing both the quantum computational requirements and cloud delivery models into a unified framework.

Quantum computing is fundamentally based on the concept of the *qubit*, the quantum analog of the classical bit. While classical bits take on definite values of either 0 or 1—typically

realized via transistor-based switching—qubits exploit the principles of quantum mechanics to exist in a superposition of states. Once excited, a qubit can assume a linear combination of both 0 and 1 until measurement causes it to collapse into one of the classical outcomes [20].

Four key properties differentiate qubits from classical bits: superposition, entanglement, interference, and quantum parallelism. Superposition allows a qubit to exist in multiple states simultaneously, enabling exponential scaling of quantum state space with the number of qubits. Interference is a where the wave-like nature of particles/qubits causes their probability amplitudes to combine, either constructively or destructively, influencing the measurements. Entanglement is a uniquely quantum phenomenon in which the state of one qubit becomes intrinsically linked to the state of another—such that measuring one instantly affects the state of the other, regardless of the physical distance between them [21]. This interdependence is foundational to quantum algorithms and protocols. Quantum parallelism enables a quantum computer to process a superposition of inputs simultaneously, dramatically increasing computational efficiency for specific tasks.

To construct a functioning quantum computer, a QPU, must satisfy a set of criteria established by DiVincenzo [22]. These
criteria outline the minimal physical requirements necessary
for scalable and practical quantum computation:

* A scalable physical system with well-characterized qubits,

* The ability to initialize qubits to a known fiducial state,

* Sufficiently long quantum coherence times,

* A universal set of quantum logic gates,

* Qubit-specific, high-fidelity measurement capability.

Various QPU technologies have been employed to build
systems that satisfy these conditions, including superconduct-
ing circuits, trapped ions, photonic systems, and spin-based
architectures. A comparison of existing qubits technologies
and their associated QPU is provided in Table I [23], [24].
Although detailed discussion of QPU is beyond the scope
of this paper, we notice that each QPU type has its own
encoding scheme, execution model for quantum algorithms. As
such, there is an increasing demand for a unified cloud-based
platform that enables access to multiple types of quantum com-
puting backends. This motivates the need for interoperability,
service and architectural abstraction—core principles of the
UQCC.

### III. PROPOSED DEFINITION OF UQCC

In this section, we propose a formal definition of the
UQCC for the first time in the literature. According to the
Oxford Language Dictionary, the term *universal* refers to
something “relating to or done by all people or things in
the world or in a particular group; applicable to all cases.”
Since its inception, cloud computing has become foundational
to modern IT ecosystems—enabling what has been described
as “everything that we currently do” in computing [3], [4]. In
the context of quantum computing, the notion of universality
is twofold. It encompasses both *computational universality*,
referring to the ability to execute a full range of quantum
algorithms, and *platform universality*, denoting the ability to
access and operate across diverse quantum architectures via
cloud-based services. Inspired by the desire of a easy to
use quantum computing system on the cloud, we define the
*Universal Quantum Computing Cloud* (UQCC) as:

> A comprehensive pool of virtualized quantum resources accessible via the internet, which includes various heterogeneous quantum hardware systems. These resources can be dynamically reconfigured to accommodate variable workloads (scalability), allowing for optimal resource utilization. The infrastructure is typically accessed through a pay-per-use model, where guarantees are provided by the infrastructure provider via customized Service Level Agreements (SLAs), and usage is free from architectural constraints.

UQCC aspires to offer a unified, inclusive, and user-friendly
suite of quantum computing services, thus contributing sig-
nificantly to the democratization and practical deployment of
quantum technologies.

## IV. LEVELS OF SERVICE ABSTRACTIONS

This section categorizes the different levels of service
abstraction relevant to UQCC. These abstractions define the
interaction between users and quantum cloud resources, as
well as the types of services that can be deployed and
consumed [3]. Depending on the provided capabilities, UQCC
can be described under five main service models as in Table II
and in Figure 1. While quantum services inherently rely on
classical counterparts for tasks like data preprocessing and
quantum result post-processing, this paper focuses exclusively
on the quantum aspects. For a detailed discussion of classical
cloud abstraction layers, readers may refer to [3].

### A. Application-as-a-Service (AaaS)

AaaS represents the highest level of abstraction in quantum
cloud offerings and it is less popular in the classical computing
paradigm, due to the complications of quantum data encod-
ing, quantum algorithm for specific hardwares. At this level,
users submit high-level queries or domain-specific problems
to the service provider. The provider then selects and runs
appropriate quantum and classical algorithms to deliver results.
For example, an R&D department of an automotive company
might request simulations for new painting materials with
reduced CO₂ emissions and improved durability. The cloud
provider would utilize quantum optimization and chemical
modeling algorithms to return solutions, with minimal input
from the end-user and reduce partly the latency.

### B. Software-as-a-Service (SaaS)

SaaS provides end-users with access to cloud-hosted quan-
tum software applications without requiring local installation
or maintenance [25]. For instance, such as quantum machine
learning or quantum chemistry simulation tools, available
as browser-based services, exemplify SaaS in the quantum
domain.

### C. Platform-as-a-Service (PaaS)

PaaS abstracts underlying hardware and infrastructure while
providing developers with a set of tools and APIs for building,
testing, and deploying quantum applications. It acts as a bridge
between low-level qubit operations and high-level application
development. Amazon Braket is a good example which inte-
grate a few types of QPU together.

### D. Infrastructure-as-a-Service (IaaS)

IaaS involves the provisioning of raw PQUs to users, typi-
cally in the form of virtualized or time-sliced access to specific
QPUs. Users can select particular QPUs (e.g., superconducting
qubits or trapped ions) and execute custom circuits, with
control over job submission, queue handling, and backend
parameters.
<figure><img src="image_1.png" /><figcaption>Fig. 1. UQCC abstraction layers ranging from the lowest level AaaS, SaaS, PaaS, to IaaS and uIaaS and the interaction with the Classical Cloud. See Discussion in Section IV.</figcaption></figure>

<table><caption>TABLE II
COMPARISON OF QUANTUM CLOUD SERVICE ABSTRACTION LEVELS</caption><thead><tr><td>Service Model</td><td>Abstraction Level</td><td>User Control</td></tr></thead><tbody><tr><td>AaaS (Application-as-a-Service)</td><td>Highest</td><td>Minimal – user submits high-level goals</td></tr><tr><td>SaaS (Software-as-a-Service)</td><td>High</td><td>Low – predefined quantum apps via UI or API</td></tr><tr><td>PaaS (Platform-as-a-Service)</td><td>Medium</td><td>Medium – use SDKs/APIs for quantum programs</td></tr><tr><td>IaaS (Infrastructure-as-a-Service)</td><td>Low</td><td>High – job queues, backend configs</td></tr><tr><td>uIaaS (Universal-Infrastructure-as-a-Service)</td><td>Lowest (but most flexible)</td><td>High – unified access across hardware types</td></tr></tbody></table>

## E. Universal-Infrastructure-as-a-Service (uIaaS)

uIaaS (or uHaaS—Universal Hardware-as-a-Service) extends the IaaS concept by offering unified access to a heterogeneous pool of QPUs. Rather than being confined to a single technology stack, users are able to run applications across various quantum devices via a common abstraction layer. This model is foundational to the concept of UQCC and enables scalable, architecture-agnostic quantum computation, where backend selection, resource allocation, and translation are handled transparently by the cloud provider.

## V. ESSENTIAL CHARACTERISTICS OF UQCC

Because this is the first time UQCC is formally defined in the literature, we identify its core characteristics by synthesizing insights from both classical cloud computing and current quantum cloud service descriptions. To support this, we conducted a sentiment analysis of publicly available discussions and documentation related to quantum computing cloud. This analysis, in conjunction with established cloud computing principles [26], informs the key characteristics of UQCC as listed in Table III. These characteristics can be grouped into three thematic categories: service-oriented features, user-centric experience, and system-level capabilities.

## A. Service-Oriented Characteristics

1) **Universality or Interoperability:** UQCC must support a wide range of quantum computing models (e.g., superconducting, neutral atom, trapped ion, photonic) as well as classical CPU and GPU in order to allow users to run diverse quantum-classical hybrid algorithms. The orchestration layer must provide semantic equivalence across devices: a circuit designed for a superconducting backend should produce functionally consistent results when deployed on trapped-ion or photonic hardware, despite differences in gate sets, connectivity, and error models. This includes hardware abstraction, QPU-agnostic programming interfaces, and compatibility with low level standard quantum instruction sets such as OpenQASM 3.0 [27], Quil [28] and Jaqal [29]. Recently, a mid-level compiler was designed to perform this integration MLIR [30].

2) **Service Abstraction and Modularity:** Similar as the service-oriented architecture (SOA) model for classical cloud [31], UQCC aims at offering multi-level services (AaaS, SaaS, PaaS, IaaS, uIaaS) (see Section IV.) The abstraction layer has to be modular, enabling seamless workload migration between levels. For instance, a workload developed at the PaaS level using Qiskit could be escalated to AaaS for domain-specific
<table><caption>TABLE III
ESSENTIAL CHARACTERISTICS OF UQCC GROUPED BY THEME</caption><thead><tr><th>Characteristic</th><th>Definition / Features</th></tr></thead><tbody><tr><td colspan="2"><strong>Service-Oriented Characteristics</strong></td></tr><tr><td>Universality</td><td>Support for gate-based, annealing, topological, and hybrid QC models; QPU-agnostic interfaces</td></tr><tr><td>Service Abstraction</td><td>Multi-level services (AaaS, SaaS, PaaS, IaaS, uIaaS); user interaction flexibility</td></tr><tr><td>Pay-per-Use</td><td>Usage-based pricing like utilities; billing aligned with compute time or algorithm complexity</td></tr><tr><td>SLAs</td><td>Performance and reliability contracts between users and providers</td></tr><tr><td colspan="2"><strong>User-Centric Characteristics</strong></td></tr><tr><td>Ease of Use</td><td>Intuitive interfaces; accessible via web portals or SDKs</td></tr><tr><td>User Experience Design</td><td>Emphasis on usability, accessibility, and responsiveness; UI follows web 2.0 practices</td></tr><tr><td>Access Anywhere</td><td>Remote access via internet regardless of geography; browser-based or API-driven tools</td></tr><tr><td colspan="2"><strong>System-Level Characteristics</strong></td></tr><tr><td>Scalability</td><td>Ability to support many users, qubits, workloads, and distributed backends</td></tr><tr><td>Virtualization</td><td>Hardware abstraction for portability, fault tolerance, and load balancing</td></tr><tr><td>Interoperability</td><td>Cross-platform compatibility, circuit portability, support for hybrid workflows</td></tr><tr><td>Security</td><td>End-to-end encryption, isolation, access control, quantum-safe protocols</td></tr></tbody></table>

optimization or lowered to uIaaS for device-specific execution. This modularity requires middleware capable of dynamically translating circuits, handling classical-quantum orchestration, and optimizing placement of workloads across multiple QPUs.

3) *Pay-per-Use and SLAs:* UQCC services operate under dynamic, usage-based pricing models. UQCC must adopt dynamic usage-based pricing models, where billing reflects qubit time, gate depth, and error correction overheads. Unlike classical clouds, resource metering must also capture non-trivial factors such as shot counts, qubit connectivity, and compilation complexity. For example, AWS's Braket has two pricing options: per-task price and per-shot price. Infrastructure and service-level agreements (SLAs) define performance guarantees, job turnaround times, and quality-of-service commitments [3], [19] through SLAs requirement such as availability, queue latency, job completion, execution fidelity, error rate bounds, throughput, interoperability, security, customer Support.

## B. User-Centric Characteristics

1) *Ease of Use and Accessibility:* UQCC interfaces are designed for broad accessibility. Through web-based dashboards, SDKs, and APIs, users of varying expertise—from researchers to students—can access quantum resources remotely.

2) *User Experience Design:* Following best practices in web 2.0 and application usability, UQCC interfaces emphasize clarity, simplicity, and responsiveness. The design process includes user need analysis, function design, and implementation that minimizes the technical overhead for non-experts [32].

## C. System-Level Characteristics

1) *Scalability:* UQCC is built to scale in multiple dimensions: number of users, concurrent job loads, and supported hardware architectures. This includes horizontal scaling across distributed quantum infrastructure providers, quantum platform management, shot and job queue management, and adaptive workload distribution.

2) *Virtualization and Resource Optimization:* Quantum calculation and classical post-processing resources are virtualized and pooled for efficient access and load balancing. These virtualized layers abstract hardware complexity, enable on-demand provisioning, and improve system utilization.

3) *Interoperability:* UQCC is inherently interoperable with various QPU types, classical systems, and hybrid workflows. It supports backend switching, unified APIs, and platform-independent circuit formats. Standardization efforts around intermediate formats (OpenQASM 3.0, QIR) and workflow orchestration APIs are critical for interoperability.

4) *Security:* Security in UQCC includes both classical and quantum-safe measures. End-to-end encryption, job isolation, secure identity management, and compliance with regulatory standards are essential. The system's loose coupling, virtualization, and abstraction also minimize attack surfaces and protect user data [33] by unplugging parts of the systems when attacks happen.

# VI. EMERGENCE OF UQCC

To contextualize the current state of Universal Quantum Computing Cloud (UQCC), we position this emerging technology on Gartner's Hype Cycle [34]. This model is a widely accepted framework that illustrates the evolution of a technology's maturity, adoption, and public perception through cycles of overenthusiasm, disillusionment, and eventual productivity [35]. As UQCC is introduced and defined for the first time in this paper, we argue that it currently resides at the earliest stage of the cycle—the *Technology Trigger* phase—characterized by the onset of technological breakthroughs and increasing interest from both academia and industry.

To provide a comparative perspective, we trace the trajectory of quantum computing itself across the Hype Cycle, as shown in Fig. 2. Beginning with the implementation of the first experimental quantum computer in 1998 [36], [37], the technology has taken approximately 24 years to transition from the *Technology Trigger* to the *Peak of Inflated Expectations*.
<figure><img src="image_2.png" /><figcaption>Fig. 2. Positioning of Quantum Computing and UQCC on the Gartner Hype Cycle. Blue circles represent the positions of quantum computing technology of different years while the Green box represents the position of UQCC.</figcaption></figure>

This phase is marked by high visibility and overly optimistic projections regarding the technology's transformative potential, often accompanied by a surge in research funding and media attention. From 2022 onward, expectations for quantum computing have shown signs of decline, signaling a movement toward the *Trough of Disillusionment*.

However, the duration a technology spends in each stage of the Hype Cycle varies considerably. For instance, cloud computing, which entered the cycle around 2007, required only eight years to reach the *Plateau of Productivity*. The pace of progression is influenced heavily by adjacent technological developments. In the case of quantum computing, persistent challenges such as qubit stability and the scalability of quantum processors have prolonged its path.

Given its foundational dependency on quantum hardware and scalable infrastructure, UQCC is still at the nascent stage of the *Technology Trigger*. Based on historical analogies and current trajectories, we project that UQCC may reach the *Peak of Inflated Expectations* by 2030, enter the *Trough of Disillusionment* by 2035, ascend the *Slope of Enlightenment* by 2040, and ultimately achieve the *Plateau of Productivity* by 2045.

## VII. CONCLUSION AND FUTURE WORK

This paper introduced the concept of a UQCC, outlining its motivation, service abstraction layers, and essential characteristics. UQCC offers a unified, QPU-agnostic, scalable, and user-centric framework for quantum computing in the cloud.

**Key contributions:**

* Defined UQCC and its five characteristics.

* Compared classical cloud service models with quantum counterparts (AaaS, SaaS, PaaS, IaaS, uIaaS).

* Highlighted the evolution and technical diversity of QC and UQCC in the Gartner's HYPE Cycle.

**Limitations and Challenges**

Despite the potential of UQCC, its essential characteristics involve inherent limitations and trade-offs that must be carefully considered:

* Service-Oriented Constraints: Universality across heterogeneous QPUs may obscure hardware-specific optimizations, leading to performance penalties. High-level abstractions (AaaS, SaaS) simplify adoption but restrict control, whereas low-level access (uIaaS) demands advanced expertise. Pricing models are further complicated by stochastic workloads, making cost prediction uncertain.
* User-Centric Challenges: Abstraction enhances accessibility but risks reducing transparency, hindering scientific reproducibility. Automated backend selection may favor latency over fidelity, producing biased outcomes or higher costs.

* System-Level Trade-offs: True virtualization of qubits is limited by physical constraints (e.g., no-cloning theorem), so “quantum hypervisors” can only approximate abstraction via emulation and transpilation. Multi-cloud scaling introduces heterogeneity in scheduling and calibration data, complicating orchestration. Interoperability requires intermediate representations but may discard unique hardware features.

* SLA-Specific Limitations: Quantum uptime is inherently volatile due to calibration cycles and hardware fragility. Latency guarantees are difficult under variable demand. Fidelity thresholds are statistical, subject to drift, and not absolute. Finally, pay-per-use models may lead to unpredictable costs, particularly for iterative algorithms such as VQE or QAOA.

**Future directions:**

* Consider a case study of a real deployment: simulating an application run across two QPU back-ends, to showing cost-and-latency breakdown

* Develop open standards for quantum-cloud APIs and SLAs.

* Create robust middleware for hardware abstraction and backend switching.

* Explore hybrid quantum-classical orchestration and deployment pipelines.

* Evaluate usability and performance metrics across quantum cloud platforms.

UQCC lays the groundwork for scalable and accessible quantum computing, accelerating its adoption in academia and industry.

## ACKNOWLEDGMENT

We were partly supported by a grant from the Simons Foundation (916424, N.H.) in addition to the enthusiastic support of IFIRSE/ICISE staff. Dinh-V-Trung acknowledge grant.

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<figure><img src="image_3.png" /></figure>

**Rossy Nhung Nguyen** is the CEO of Quanova and the founder of the Vietnam Quantum Network (VNQuantum). She previously served as Head of Product at ORCA Computing (London UK) where she launched the second-generation photonic quantum computer, PT-2, for HPC and generative-AI workloads. Earlier, at Riverlane (Cambridge, UK), she led the production of the world's first quantum-error-correction ASIC chip. Her research focuses on software- and hardware-level integration of quantum systems into high-performance computing infras-

tructures.

<figure><img src="image_4.png" /></figure>

**Dr. Hung Q. Nguyen** completed his PhD in quantum physics at Brown University (USA) in 2010 and spent a lot of time doing in-depth research on superconducting quantum components at the world's leading research institutions such as Aalto University (Finland), Neel Institute, CNRS (France), or University of Copenhagen (Denmark). After returning to Vietnam, Dr. Hung worked at the Center for Nano and Energy, Vietnam National University, Hanoi, building a research group on semiconductor chips in energy conversion. At the same time, he carried out research on quantum computing. Dr. Hung has published more than 40 articles in prestigious international journals and is in charge of many research topics, especially in the field of quantum computing. Currently, he is the Director of the Institute of Quantum Technology, developing research directions to master quantum technology at Vietnam National University, Hanoi.

<figure><img src="image_5.png" /></figure>

**Quang Nguyen-Luong** (Member, IEEE, 2025) received the B.Sc. degree in Aerospace Engineering from Delft University of Technology (TU Delft), the Netherlands, the M.Sc. degree in Astrophysics from the University of Bonn, Germany, the M.Sc. degree in Quantum Computing from Universidad Politécnica de Madrid, Spain, and the Ph.D. degree in Physics and Astrophysics from Université Paris Diderot, France.

He has co-authored over 140 peer-reviewed papers and conference publications. His current research interests include quantum computing, cloud-based quantum infrastructure, radio wave technologies, and applications in astrophysics. He was an IBM Quantum Ambassador and Head of Research of DynaDNA, a quantum application company. He is presently the CEO of BOLTZ.AI, and serves as a visiting researcher at CEA-Saclay, France, and an investigator at the SAGI/IFIRSE Institute in Vietnam.

<figure><img src="image_6.png" /></figure>

**Dinh Văn Trung** received the B.Sc. degree in Physics from Hanoi University (formerly Hanoi General University), Vietnam, in 1994, and conducted research in astrophysics at Université Paris VI, France, from 1995 to 1998. He has authored nearly 40 peer-reviewed international journal papers. Since late 2009, he has been teaching General Physics at Hexagon, where he has contributed significantly to the success of the UEE program. He has also held visiting positions at National Taiwan University, the University of Hong Kong, and the

Institute of Physics, Hanoi.

<figure><img src="image_7.png" /></figure>

**Pham Viet Dung** received the B.Sc. degree in Space and Applications from the University of Science and Technology of Hanoi (USTH), Vietnam, in 2017, the M.Sc. degree in Satellite Technology from Université Paris Cité and USTH in 2019, and the Ph.D. degree in Physics from Université Paris Cité, France, in 2024. His research interests include antenna design, multichroic detectors for CMB polarization, microwave engineering, superconducting MKIDs, and cryogenic instrumentation. Until now, he has published 6 papers in the domain of microwave

engineering.

<figure><img src="image_8.png" /></figure>

**Dr. Asif Shariff** is a Lead Data Scientist and Senior Manager at BOLTZ.AI. His nearly eight years at IBM also include roles as a Lead Data Scientist/Manager and Senior Artificial Intelligence Developer. Previously, Asif was a Research Scientist and Blockchain Developer at Privacy Shell, the University of Toronto, Kiwi Wearables, and Ryerson University, focusing on fractal time series analysis. Asif hold a Ph.D. and M.A.Sc. in Mechanical Engineering from Ryerson University, with research on cardiovascular system fractal fluctuations and using wavelet transform modulus. Their expertise covers data science, AI, signal processing, and complex systems analysis.

multifractal correlation