Entangled Futures: Quantum Computing's Potential and Challenges in Computer Science

I. INTRODUCTION

• Quantum processors operate as co-processors: classical computers handle initialization, control, error correction, and measurement while quantum processors focus on specialized tasks.Classical systems also perform pre- and post-processing in this hybrid arrangement.Source: §I · p. 1
• Quantum computers encompass multiple physical implementations, while qubits represent a continuum of possible two-level states and can use superconducting circuits, trapped ions, or photons,.Gate-based QPUs are prominent for their flexibility and fit with general-purpose quantum algorithms [3].Source: §I · p. 1
• Quantum memory storage is typically microseconds to seconds, depending on the physical system, and extending coherence while scaling remains a fundamental challenge.Quantum error-correction codes can preserve states but require redundant qubits, adding complexity.Source: §I · pp. 1–2
• Figure 1 is identified as a schematic block diagram of quantum computing, and the text describes it as showing basic components and interactions in a quantum-classical system.Quantum networks extend quantum information across distant nodes, but repeaters relay rather than amplify quantum signals because quantum information cannot be copied.Source: Figure 1 · p. 2, §I · p. 2
• Quantum-classical interfaces encode classical inputs into quantum states, apply gate-based transformations, and measure results for interpreting the computation.Cloud APIs let users submit programs and retrieve results through high-level frameworks without directly handling low-level hardware.Source: §I · p. 2

II. OPEN ISSUES IN UTILISING QUANTUM COMPUTERS IN COMPUTER SCIENCE

• The article identifies open issues in using quantum computers in computer science as requiring further exploration.The following subsections discuss these issues, highlight gaps in current approaches, and outline potential research directions.Source: §II · p. 2
• The article organizes its discussion around gaps in current approaches to quantum computing in computer science.It presents the ensuing subsections as discussions of those issues.Source: §II · p. 2
• Potential research directions are part of the article’s treatment of open issues in computer science applications of quantum computing.The passage does not specify those directions individually.Source: §II · p. 2

A. Quantum algorithm design

• Quantum computing approaches include direct QPU computation, hybrid variational methods, and quantum-inspired classical algorithms that need no QPU.The examples given are Shor’s and Grover’s algorithms, VQE-style circuits with classical optimisation, and tensor-network methods, respectively.Source: A. Quantum algorithm design · p. 2
• Near-term hybrid methods such as VQE have yet to consistently outperform state-of-the-art classical methods.VQE executes a parameterised circuit on a QPU, then feeds measurement outcomes to a classical optimiser that updates parameters until convergence.Source: A. Quantum algorithm design · p. 2
• Variational algorithms face barren plateaus, where gradients vanish exponentially with circuit width and depth, making large-circuit training infeasible.The passage links the issue to tension between circuit expressibility and trainability, alongside hardware noise and classical overhead in the hybrid loop.Source: A. Quantum algorithm design · p. 3
• Quantum machine learning supports tasks including hardware calibration, noise characterisation, error mitigation, and adaptive control.Further research is needed to assess scalability, robustness, and practical advantage under realistic NISQ conditions.Source: A. Quantum algorithm design · p. 3
• The section calls for algorithms tailored to problem classes where quantum computers could provide substantial advantages, while addressing noise resilience and hardware constraints.It identifies optimisation and resource management as unresolved challenges in pursuing quantum speedups with classical flexibility.Source: A. Quantum algorithm design · p. 3

B. Application specification and mapping

• Classical data structures and control flows do not directly transfer to quantum systems, where superposition, probabilistic measurement, no-cloning, and state collapse require a different specification paradigm.The challenge is to represent classical applications accurately while leveraging quantum computing’s potential advantages.Source: B. Application specification and mapping · p. 3
• Table I is titled as a comparison of quantum programming tools, but the supplied passage does not state its specific comparison dimensions or outcomes.The table title alone does not establish which tool is favored.Source: Table I · p. 3
• Quantum application mapping defines input encoding, state preparation, and processing transformations while ensuring intermediate states persist until measurement.Designers must interpret outcomes in the original problem’s context and optimize qubit counts, circuit depth, connectivity, and gate fidelity.Source: B. Application specification and mapping · p. 3

C. Lack of standardised compiler/transpiler

• Platform-coupled tools limit portability: circuits compiled for one provider may need re-optimisation or restructuring for another provider’s hardware.The article identifies differing gate sets and qubit topologies as reasons for this platform dependence.Source: C. Lack of standardised… · p. 3
• OpenQASM 3.0 remains in early development, and multi-backend platforms such as Amazon Braket still rely on backend-specific compilation steps.The article describes these tools as easing integration, not eliminating platform-specific compilation.Source: C. Lack of standardised… · p. 3
• Without a universal compiler that optimises across diverse hardware, truly portable and scalable quantum applications remain out of reach.Source: C. Lack of standardised… · p. 4
• The proposed compiler approach translates programs into a hardware-independent format before platform-specific optimisation, while reducing circuit depth, gate count, and error propagation.The article also identifies hybrid compilers combining classical pre/post-processing with quantum logic as essential for broader application use.Source: C. Lack of standardised… · p. 4

D. Scheduling and context switching in hybrid quantum-classical computing

• Tasks must finish within the coherence window because pauses or interruptions risk decoherence and irreversible quantum-state loss.Resource management must also minimise crosstalk and use limited quantum hardware efficiently.Source: D. Scheduling and context switching in… · p. 4
• Current queue-based systems, compilation-aware schedulers, and hybrid orchestration frameworks aid execution, but remain largely static and do not account for real-time qubit-fidelity or noise changes.Runtime error mitigation also increases scheduling complexity.Source: D. Scheduling and context switching in… · p. 4
• Faithful classical simulation of general or highly entangled quantum states becomes infeasible at scale because state representation grows exponentially with qubit count.Simulators also struggle to model realistic noise and open-system effects at scale, limiting evaluation of NISQ error-mitigation and control strategies.Source: D. Scheduling and context switching in… · p. 4
• Quantum systems lack true context switching, limiting multitasking; future schedulers must adapt dynamically and support interleaved workflows with minimal latency.The section also identifies multi-tenancy as a fairness-efficiency challenge.Source: D. Scheduling and context switching in… · p. 4

E. Communication in quantum environment

• Noise, decoherence, and photon loss degrade transmitted quantum-state fidelity, while a lack of scalable repeaters limits long-distance communication.The section describes robust quantum-network infrastructure as absent despite promising teleportation and QKD protocols.Source: E. Communication in quantum environment · p. 4
• Measurement makes bidirectional communication non-trivial, and brief coherence windows complicate synchronisation with continuous classical systems.Existing Qiskit and Cirq frameworks face high latency, bandwidth constraints, and a lack of adaptive communication protocols.Source: E. Communication in quantum environment · p. 4
• The article calls for faster analog-to-digital conversion, dynamic synchronisation, dedicated interface hardware, and middleware for real-time workflow adaptation and error correction.It also identifies quantum routers, scalable network topologies, and multi-tenant protocols as research needs.Source: E. Communication in quantum environment · p. 4

F. Programming and Software Development

• Quantum programming remains circuit-level and hardware-conscious, while steep learning curves and scarce reusable modules limit accessibility and force teams to recreate common solutions.The section contrasts quantum development with classical high-level languages, modular libraries, and reusable APIs.Source: F. Programming and Software Development · p. 5
• Annealers encode optimisation problems as physical energy landscapes rather than universal gate sequences, offering practical utility for some combinatorial optimisation and sampling tasks.Their higher-level QUBO and Ising interfaces motivate research into gate-model wrappers, domain-specific languages, hybrid orchestration, and reusable libraries.Source: F. Programming and Software Development · p. 5

G. Monitoring and Debugging in Quantum Programs

• Measurement alters quantum systems, and probabilistic outcomes make debugging an inference task rather than direct variable inspection; simulators cannot fully reproduce real-hardware unpredictability.The limitations are especially noted as circuits grow in size and complexity.Source: G. Monitoring and Debugging in Quantum… · p. 5
• Tomography’s measurement cost scales exponentially with system size, while classical shadows and shadow overlap reduce measurement complexity but current approaches remain technically demanding and hard to scale.The section also mentions weak measurements and SIC-POVMs as alternatives that are not yet practical for routine debugging.Source: G. Monitoring and Debugging in Quantum… · p. 5
• Future debugging work includes more scalable hybrid frameworks that infer potential errors without disturbing quantum states and machine-learning methods for automated circuit-error identification.Source: G. Monitoring and Debugging in Quantum… · p. 5

H. Security in Quantum Computing

• Protecting one logical qubit may require dozens or hundreds of physical qubits, adding substantial attack surface and operational overhead.Quantum systems’ sensitivity to noise and operational inconsistencies also necessitates complex fault-tolerant architectures and quantum error-correction codes.Source: H. Security in Quantum Computing · p. 5
• Side-channel attacks often target classical control and readout layers through electromagnetic leakage, power analysis, or timing variations.Noise-resilient control protocols and shielding remain early-stage and struggle to protect consistently across hardware platforms.Source: H. Security in Quantum Computing · pp. 5–6
• Practical quantum key distribution remains vulnerable to photon-number-splitting and detector-blinding attacks that exploit physical-device imperfections.Integrating quantum networks with classical infrastructure compounds the issue because classical systems do not inherently inherit quantum protections.Source: H. Security in Quantum Computing · p. 6
• Classical safeguards such as TPM and SMC offer partial protection, while robust orchestration is needed to protect data integrity and confidentiality across quantum-classical boundaries.PQC is a transitional safeguard, not an absolute solution, because future quantum algorithms could undermine methods designed against currently known algorithms [29].Source: H. Security in Quantum Computing · p. 6

III. QUANTUM COMPUTING VS CLASSICAL COMPUTING

• Current NISQ processors’ small qubit counts, high error rates, and noise limit broad commercial utility, despite demonstrations of computational advantage in controlled settings.Classical computing has matured over decades and remains the backbone of modern computing infrastructure.Source: §III · p. 6
• Hybrid quantum-classical workflows are explored in computational chemistry, combinatorial optimisation, and financial portfolio optimisation.Quantum processors are envisioned as accelerators for specific kernels, while classical systems handle data management, orchestration, and result interpretation; demonstrated advantage remains primarily in narrowly defined benchmarks.Source: §III · p. 6
• Cloud quantum services already expose QPUs as remote accelerators through classical APIs, with jobs queued, scheduled, and postprocessed through conventional infrastructure.The passage names IBM, Google, and Amazon as providers of these services.Source: §III · p. 6
• The article describes a synergistic future in which quantum co-processors and classical computers contribute together to real-world applications.Researchers are refining quantum error correction, optimising algorithms for NISQ devices, and building quantum-classical software stacks.Source: §III · p. 6

• Devki Nandan Jha is a Lecturer at Newcastle University, UK.Source: BIOGRAPHY · p. 7
• Jha is also a visiting researcher at Oxford.Source: BIOGRAPHY · p. 7
• Jonte R. Hance is Head of the Quantum Group at Newcastle University.Source: BIOGRAPHY · p. 7