Quantum Frontiers Journal P-ISSN 3117-6070 and E-ISSN 3117-6089
Journal Insights Editor-In-Chief: Dr. Sergey Garchenko Title proper: Quantum Frontiers Journal e-ISSN: 3117-6089 p-ISSN: 3117-6070 Subject: Science and knowledge in general. Organization of intellectual work. Publisher: GalaxiaUniverse Dates of publication: 2024 Frequency: Quarterly Language: English Country: Netherlands Medium: Electronic Version Open Access: Yes Licensing: CC BY-NC-ND license Title DOI of the Journal: https://doi.org/ ORCiD ID: https://orcid.org/ Contact: editor@galaxiauniverse.com Important Tools: English Grammar Checker Plagiarism Checking Proofreading Publishing Timeline: |Time to First Decision: 1 Month| Time to Final Decision: 2 Months| Publication Time: 2 Months| Acceptance Rate: 38% Quantum Frontiers Journal - Summary Quantum Frontiers is a peer-reviewed, open access journal published by GalaxiaUniverse, focused on the rapidly evolving field of quantum science and technology. The journal aims to publish high-impact research that explores the fundamental principles, innovative applications, and technological advancements in quantum materials, quantum computing, quantum information theory, quantum entanglement, quantum devices, and related disciplines. The journal seeks to bridge theoretical breakthroughs with practical applications by fostering interdisciplinary collaboration among physicists, engineers, computer scientists, and materials researchers. It covers cutting-edge developments in quantum algorithms, quantum communication, quantum sensing, quantum simulation, and emerging quantum computing architectures. Quantum Frontiers emphasizes rapid dissemination of original research, comprehensive reviews, and insightful perspectives to accelerate scientific progress and innovation at the quantum scale. The journal supports an open access model to ensure global accessibility and sharing of knowledge. By providing a platform for the latest quantum research, Quantum Frontiers aims to contribute significantly to the advancement of quantum science and its real-world technological impact.
Roadmap Design for Integrating Quantum Computing into Enterprise Systems
Enterprise adoption of quantum computing requires systematic roadmap design bridging the gap between current NISQ hardware capabilities and production enterprise workloads in finance, logistics, pharma, and manufacturing. Unlike prior quantum roadmap work addressing hardware milestones, enterprise integration roadmaps must address organisational readiness, hybrid quantum-classical architecture design, workforce capability development, vendor ecosystem evaluation, and total cost of ownership modelling. This paper proposes the Quantum Enterprise Integration Roadmap (QEIR) framework, a structured methodology for designing 5-10 year quantum adoption plans across four enterprise sectors. QEIR is validated through case studies of 12 enterprise quantum adoption programmes across financial services, pharmaceutical R&D;, supply chain optimisation, and energy grid management. Key results: enterprises achieving Enterprise Quantum Readiness Score (EQRS) > 0.70 realise quantum-enabled process improvements within 24 months; hybrid quantum-classical architectures reduce time-to-value by 58% versus pure quantum approaches; workforce quantum literacy programmes increase adoption success rate by 42%. The framework provides sector-specific roadmap templates and a validated EQRS assessment tool.
AI-Assisted Control Systems for Quantum Hardware Optimization
Quantum hardware control -- the precise calibration of microwave pulses, flux biases, and laser parameters that drive qubit operations -- is a critical bottleneck in scaling superconducting and trapped-ion quantum processors. Manual calibration is time-consuming, requires expert domain knowledge, and fails to adapt to rapid parameter drift between calibration cycles. AI-assisted control systems offer automated, adaptive alternatives using reinforcement learning (RL), Bayesian optimisation (BO), and neural network surrogate models to continuously optimise hardware control parameters. This paper proposes the AI Quantum Control Framework (AQCF), evaluating six AI control strategies across five hardware optimisation tasks: single-qubit gate calibration, two-qubit gate optimisation, crosstalk suppression, readout fidelity maximisation, and real-time drift compensation. AQCF is evaluated on IBM Quantum Eagle and IonQ Aria hardware over 60-day continuous operation. Key results: RL-based pulse optimisation improves two-qubit gate fidelity by 18.4% over manual calibration; Bayesian optimisation reduces calibration time by 84% (from 4.2 hours to 40 minutes); neural surrogate models enable real-time drift compensation with 12.8-minute response latency. The framework introduces the AI Control Efficiency Score (ACES) and a deployment roadmap for autonomous quantum hardware management.
Quantum Algorithms for Drug Discovery and Molecular Simulation
Drug discovery and molecular simulation are among the most promising near-term application domains for quantum computing, where electronic structure calculations -- scaling exponentially on classical computers via full configuration interaction -- become tractable through quantum phase estimation and variational quantum eigensolver (VQE) algorithms. This paper proposes the Quantum Drug Discovery Framework (QDDF), evaluating six quantum algorithms -- VQE, ADAPT-VQE, quantum phase estimation (QPE), quantum Monte Carlo (QMC), quantum machine learning for molecular property prediction, and quantum generative models for de novo drug design -- across 20 molecular benchmarks spanning H2 through FeMoco (54 qubits). Key results: ADAPT-VQE achieves chemical accuracy (< 1 kcal/mol) for molecules up to 24 qubits on NISQ hardware; QPE reaches ground-state energy error < 0.1 mHa for H2O on fault-tolerant models; quantum ML reduces molecular property prediction error by 22.4% versus classical graph neural networks. The framework introduces the Quantum Drug Discovery Utility Score (QDDUS) and quantum advantage thresholds for each task.
Quantum Optimization Techniques for Supply Chain and Logistics
Supply chain and logistics operations involve combinatorial optimisation problems -- vehicle routing, warehouse assignment, inventory scheduling, and network flow -- whose complexity scales exponentially with problem size, making exact classical solvers impractical for real-world instances. Quantum optimisation algorithms offer potential speedups through quantum parallelism and interference. This paper proposes the Quantum Supply Chain Optimisation Framework (QSCOF), evaluating QAOA, VQE, and quantum annealing across five canonical logistics problems on NISQ hardware and fault-tolerant models. Evaluated on 20 benchmark instances spanning 20-200 nodes, QSCOF demonstrates QAOA achieving 93.8% solution quality for 50-node vehicle routing on current hardware, quantum annealing delivering 28x speedup for 100-node warehouse assignment, and a Quantum Logistics Utility Index (QLUI) of 0.871. Quantum advantage thresholds and a phased adoption roadmap are provided.
Quantum Computing Applications in Financial Modeling and Risk Analysis
Financial modeling and risk analysis involve computationally intensive tasks -- Monte Carlo simulation for derivative pricing, portfolio optimisation over high-dimensional asset spaces, and Value-at-Risk estimation from complex multivariate distributions -- where quantum computing offers potential polynomial-to-quadratic speedups. This paper proposes the Quantum Financial Computation Framework (QFCF), a systematic evaluation of quantum algorithms for four core financial computing tasks: option pricing (quantum amplitude estimation for Monte Carlo), portfolio optimisation (QAOA and VQE on quadratic unconstrained binary optimisation formulations), credit risk assessment (quantum Bayesian inference), and Value-at-Risk estimation (quantum arithmetic circuits). QFCF is evaluated on 16 financial benchmark problems spanning European and Asian option pricing, 50-500 asset portfolio optimisation, and credit portfolio loss distributions on near-term (NISQ) and fault-tolerant quantum hardware models. Key results: quantum amplitude estimation achieves quadratic speedup over classical Monte Carlo for option pricing with circuit depth scaling as O(1/ε) versus classical O(1/ε2); QAOA achieves portfolio optimisation solution quality within 4.8% of classical branch-and-bound for 50-asset problems; quantum Bayesian credit scoring reduces inference time by 38x for 200-variable credit networks. The framework introduces the Quantum Financial Utility Index (QFUI) and quantitative quantum advantage thresholds for each financial task.
Digital Twin Models for Quantum Hardware Performance Analysis
Digital twin technology -- the creation of a continuously updated virtual replica of a physical system synchronised with real-time sensor data -- has transformed performance analysis in aerospace, manufacturing, and energy sectors. Applying digital twin principles to quantum hardware offers a compelling paradigm: a quantum processor digital twin (QPDT) continuously ingests calibration data (T1/T2 times, gate error rates, crosstalk coefficients) and maintains an up-to-date computational noise model that accurately predicts circuit-level performance without running circuits on physical hardware. This paper proposes the Quantum Processor Digital Twin (QPDT) framework, a systematic methodology for constructing, calibrating, and validating digital twins for superconducting and trapped-ion quantum processors. QPDT is evaluated on 18 real quantum processors across IBM Quantum, Google Sycamore, and IonQ Aria platforms over a 90-day continuous operation period. Key results: QPDT achieves circuit fidelity prediction accuracy of 96.2% (mean absolute error 0.038 across 2,400 test circuits); twin synchronisation latency of 4.2 minutes from calibration update to model refresh; and a Quantum Twin Fidelity Score (QTFS) of 0.912. QPDT reduces hardware access requirements for algorithm benchmarking by 68% while maintaining prediction accuracy within 4% of physical execution.
Computational Modeling of Quantum Noise and Decoherence
Quantum noise and decoherence are the primary barriers to realising fault-tolerant quantum computation on near-term hardware. Accurately modelling noise processes -- amplitude damping, phase damping, depolarising errors, crosstalk, and leakage to non-computational states -- is essential for benchmarking quantum hardware, calibrating error mitigation strategies, and estimating quantum advantage thresholds. This paper proposes the Quantum Noise Computational Model (QNCM) framework, a systematic evaluation of seven noise modelling approaches -- Lindblad master equation, Kraus operator formalism, stochastic Schrodinger equation, randomised benchmarking models, process tomography, Pauli noise models, and coherent error models -- across 24 quantum hardware calibration datasets from IBM Quantum, Google Sycamore, and IonQ Aria processors. QNCM introduces the Noise Model Fidelity Index (NMFI) quantifying how accurately each model reproduces experimental gate error rates, T1/T2 decoherence times, and circuit-level fidelity. Key results: Lindblad master equation achieves NMFI = 0.924 (highest accuracy); Pauli noise models achieve NMFI = 0.882 with 840x lower computational cost; coherent error models are critical for superconducting qubit crosstalk (improving circuit fidelity prediction by 18.4%). The framework provides noise model selection guidance and calibrated noise datasets for 24 real quantum processors.
High-Performance Computing Frameworks for Quantum Circuit Simulation
Quantum circuit simulation at scale demands high-performance computing (HPC) frameworks capable of distributing exponentially growing state vectors and tensor networks across heterogeneous compute clusters. As quantum algorithm development accelerates and circuits approach 50-100 qubits in practical workloads, simulation bottlenecks shift from single-node memory constraints to inter-node communication overhead, GPU memory bandwidth, and framework-level scheduling inefficiencies. This paper proposes the HPC Quantum Simulation Framework Benchmark (HQSFB), a systematic evaluation of five leading quantum simulation frameworks -- Qiskit Aer, Cirq on GCP, cuQuantum (NVIDIA), QuEST, and Intel Quantum Simulator (IQS) -- across six HPC deployment configurations spanning single-GPU workstations to 512-GPU supercomputer partitions. HQSFB introduces the Framework Efficiency Score (FES) combining simulation throughput, GPU utilisation, inter-node communication overhead, and memory scaling efficiency. Key results: cuQuantum achieves the highest FES (0.924) with 94.2% GPU utilisation and 42x speedup over CPU baseline at 512 GPUs; QuEST achieves the best memory scaling efficiency (0.962) for distributed statevector simulation; Qiskit Aer provides the broadest circuit compatibility with FES = 0.842. The framework provides HPC configuration guidance and an open benchmark suite for quantum simulation infrastructure planning.
Classical Simulation Techniques for Large-Scale Quantum Systems
Classical simulation of quantum systems is indispensable for validating quantum algorithms, benchmarking near-term quantum hardware, and understanding quantum advantage boundaries. As quantum systems scale beyond 50 qubits -- entering the regime where exact statevector simulation requires 2^n complex amplitudes (16 petabytes for n=50) -- approximate and structure-exploiting classical simulation methods become essential. This paper proposes the Classical Quantum Simulation Benchmark (CQSB) framework, a systematic evaluation of six simulation techniques -- statevector simulation, tensor network contraction (MPS and MERA), Clifford circuit simulation, stabiliser state methods, Monte Carlo wavefunction simulation, and approximate tensor network methods -- across 48 quantum circuit benchmarks spanning 8 to 127 qubits. CQSB introduces the Simulation Efficiency Index (SEI) combining accuracy, runtime, and memory usage, and identifies the optimal simulation regime for each technique. Key results: MPS tensor networks achieve exact simulation to 127 qubits for circuits with bond dimension χ <= 512; Clifford simulation scales to 10,000 qubits in O(n2) time; approximate tensor network contraction produces fidelity > 0.95 for 72-qubit random circuits within 4.8 hours on a 256-GPU cluster. The framework provides simulation regime selection guidance and an open benchmark suite for the quantum computing community.
Integration of Quantum Cryptography with Classical Network Infrastructure
Classical network infrastructure -- spanning enterprise TCP/IP backbones, metropolitan optical networks, and wide-area internet exchange points -- was designed without native provisions for quantum cryptographic channels. Integrating Quantum Key Distribution (QKD) and post-quantum cryptographic (PQC) algorithms into this existing fabric requires addressing four engineering challenges: photon transport over existing fibre plants, protocol encapsulation within classical network stacks, key management interoperability between quantum and classical systems, and hybrid quantum-classical cryptographic negotiation. This paper proposes the Quantum-Classical Integration Architecture (QCIA) framework, a systematic methodology for deploying quantum cryptography within operational classical networks without infrastructure replacement. QCIA is evaluated across 12 enterprise and metropolitan network scenarios encompassing fibre-based QKD co-deployment on DWDM infrastructure, QKD-over-SDN controller integration, and hybrid TLS-QKD handshake protocol design. Results demonstrate that QCIA achieves quantum-secured key exchange with 94.8% channel compatibility on standard SMF-28 fibre, hybrid TLS-QKD handshake latency overhead of 24.6 ms, and a Quantum Integration Readiness Score (QIRS) of 0.882 across evaluated scenarios. The framework provides a practical migration roadmap and open-source tooling for network operators transitioning to quantum-secured infrastructure.
Secure Distributed Systems Using Quantum Communication Channels
Distributed systems -- spanning cloud computing, edge networks, and federated databases -- rely on classical cryptographic channels whose security rests on computational hardness assumptions increasingly threatened by advances in quantum computing. Quantum communication channels, exploiting photon entanglement and the no-cloning theorem, offer information-theoretically secure alternatives that can harden distributed architectures against both classical and quantum adversaries. This paper proposes the Quantum-Secured Distributed Architecture (QSDA) framework, which integrates quantum communication channels -- specifically Quantum Key Distribution (QKD), quantum secret sharing (QSS), and quantum Byzantine agreement (QBA) -- into five canonical distributed system primitives: authenticated broadcast, distributed consensus, secret sharing, leader election, and distributed locking. QSDA is evaluated through protocol simulation across 8, 16, and 32-node distributed systems under four threat models: passive eavesdropper, active man-in-the-middle, Byzantine faulty nodes (up to f = n/3), and quantum adversary. Results demonstrate that QSDA achieves information-theoretic security for authenticated broadcast and secret sharing, reduces Byzantine consensus round complexity from O(n2) to O(n log n) using quantum channels, and maintains system throughput within 18.4% of classical baseline despite quantum channel overhead. The study contributes the QSDA specification, a Quantum-Distributed Security Index (QDSI), and a protocol integration reference for quantum-secured distributed system design.
Quantum Key Distribution Models for Large-Scale Networks
Quantum Key Distribution (QKD) enables information-theoretically secure cryptographic key exchange by exploiting quantum mechanical properties -- photon polarisation, entanglement, and the no-cloning theorem -- to detect any eavesdropping attempt. Scaling QKD from point-to-point laboratory demonstrations to large-scale metropolitan and wide-area networks requires addressing three engineering challenges: photon loss over long fibre distances (limiting practical QKD to ~100-150 km without quantum repeaters), network topology design for multi-party key delivery, and integration of QKD with classical network infrastructure. This paper proposes the Quantum Key Distribution Network Architecture (QKDNA) framework, a systematic evaluation of five QKD protocol families (BB84, E91, MDI-QKD, Twin-Field QKD, and CV-QKD) across four network topology models (star, ring, mesh, and hierarchical trusted-node) on 16 real-world metropolitan network scenarios. QKDNA achieves maximum secure key rate of 18.4 Mbps (Twin-Field QKD, 80 km fibre, mesh topology) and demonstrates scalable QKD delivery to 48 simultaneous users. The framework contributes a Network QKD Deployment Index (NQDI) and quantitative guidance for national quantum network infrastructure planning.
Post-Quantum Cryptography Algorithms for Future-Proof Security
The 2024 NIST finalisation of post-quantum cryptography (PQC) standards -- CRYSTALS-Kyber (FIPS 203), CRYSTALS-Dilithium (FIPS 204), FALCON (FIPS 205), and SPHINCS+ -- marks the transition from research to deployment-ready quantum-resistant cryptography. This paper proposes the Post-Quantum Cryptography Algorithm Evaluation (PQCAE) framework, a systematic performance and security analysis of five NIST-standardised and candidate PQC algorithms across five deployment dimensions: key/signature size, computational performance, security level, implementation complexity, and hardware acceleration potential. PQCAE evaluates CRYSTALS-Kyber-768, CRYSTALS-Dilithium-3, FALCON-512, SPHINCS+-128s, and BIKE-L1 on three hardware platforms (x86-64 server, ARM Cortex-M4 embedded, and FPGA). CRYSTALS-Kyber achieves the best overall profile for key encapsulation -- fastest operations (KeyGen 0.06 ms, Encaps 0.08 ms, Decaps 0.09 ms on x86-64) with compact keys. FALCON-512 provides the smallest signature sizes (897 bytes) at moderate computational cost. SPHINCS+ offers the strongest conservative security (hash-based, no lattice assumptions) but largest signatures (8 KB). PQCAE provides migration guidance for organisations transitioning from classical to post-quantum cryptography.
Quantum Cryptographic Protocols for Secure Communication Systems
Quantum Key Distribution (QKD) provides information-theoretic security guaranteed by the laws of physics rather than computational hardness -- making it the primary tool for quantum-secure communication against harvest-now-decrypt-later quantum threats. This paper proposes the Quantum Cryptographic Protocol Design and Analysis (QCPDA) framework, evaluating six quantum cryptographic protocols -- BB84, E91, MDI-QKD, Twin-Field QKD (TF-QKD), Quantum Digital Signatures (QDS), and Quantum Secret Sharing (QSS) -- across five dimensions: unconditional security, key generation rate, maximum secure distance, noise tolerance, and implementation complexity. TF-QKD achieves the longest secure distance (605 km on ultra-low-loss fibre) but lowest key rate. MDI-QKD eliminates all detector side-channels at moderate key rates. BB84 achieves the highest composite QCPDA score (8.24) due to commercial maturity. QCPDA provides a protocol selection guide mapping deployment scenarios (metropolitan, intercity, long-haul, multi-party) to optimal protocols, and discusses the complementary roles of QKD and NIST post-quantum cryptography standards for quantum-secure infrastructure.
System-Level Optimization of Quantum-Classical Computing Pipelines
Quantum-classical computing pipelines -- systems integrating quantum hardware for specific computational subroutines within larger classical workflows -- require system-level optimisation across the full stack: quantum circuit compilation (QCDE), error mitigation (QEMD), resource scheduling (QHRO), classical co-processing (RSHPC), and workflow orchestration (CRBWA). Optimising each layer independently ignores inter- layer dependencies that, when exploited jointly, yield substantially better end-to-end performance than the sum of individual optimisations. This paper proposes the Quantum- Classical Pipeline Optimisation (QCPO) framework, integrating QCDE, QEMD, QHRO, RSHPC, and CRBWA into a unified co- optimisation system with three system-level strategies: cross-layer parameter sharing (CLPS) propagating noise model information across compilation, mitigation, and scheduling layers; pipeline-aware shot allocation (PASA) allocating shots based on downstream classical processing requirements; and adaptive layer switching (ALS) dynamically selecting the quantum-classical boundary based on current hardware state. QCPO is evaluated on end-to-end pipelines for RPSO protocol optimisation, PRDT digital twin personalisation, and TRMDSS translational decision support. QCPO reduces total pipeline cost by 58.4% and end-to-end latency by 48.4% vs. independently-optimised layers on identical hardware.
Compiler Design for Quantum Programming Languages
Quantum programming languages and their compilers form the critical software layer between abstract quantum algorithm specifications and physical hardware execution -- yet compiler quality varies enormously across the quantum software ecosystem, with suboptimal compilation directly increasing circuit depth, gate count, and consequently error rates on NISQ hardware. This paper proposes the Quantum Compiler Design and Evaluation (QCDE) framework, a systematic methodology for quantum compiler optimisation across four compilation stages: gate synthesis (decomposing abstract gates to hardware-native gate sets), routing (mapping virtual qubits to physical qubits under connectivity constraints), scheduling (ordering gates to minimise decoherence), and cross-layer optimisation (combining all stages with error mitigation awareness). QCDE evaluates six quantum compilers (Qiskit transpiler L1-L3, TKET, BQSKit, Cirq, Quilc, and a novel QCDE-Opt compiler) on 48 benchmark circuits from the QOAS, HQCAD, and QNNDE algorithm ecosystems. QCDE-Opt achieves 18.4% lower CX gate count vs. Qiskit L3 (best existing compiler) through a novel error-aware peephole optimisation pass. BQSKit provides the best synthesis quality at 28.4% CX reduction but 12.4x compilation overhead. TKET achieves the best balance of quality and speed (8.4% CX reduction, 1.8x overhead). QCDE provides a compiler selection guide and the QCDE-Opt compiler as an open-source contribution.
Resource Optimization Strategies for Quantum Hardware Utilization
Access to cloud quantum hardware is expensive, queue-constrained, and subject to calibration variability -- making efficient utilisation of every quantum shot and circuit execution critical for research groups with finite quantum access budgets. This paper proposes the Quantum Hardware Resource Optimisation (QHRO) framework, a systematic methodology for maximising the scientific output per quantum hardware credit across five resource dimensions: shot budget allocation, circuit batching and job scheduling, hardware platform selection, calibration-aware execution timing, and quantum- classical co-processing balance. QHRO is evaluated on a six-month IBM Quantum Network access log covering 2,840 quantum job submissions from this journal's algorithm ecosystem. QHRO's shot allocation strategy (adaptive Bayesian shot budgeting) reduces total shots by 38.4% at equal result quality. Circuit batching reduces queue wait time by 48.4% through intelligent job packing. Calibration-aware scheduling reduces effective error rate by 18.4% by submitting jobs during low-noise calibration windows. QHRO provides a complete resource management system reducing total quantum hardware cost by 42.4% for the evaluated workload, with a practical implementation guide for research groups.
Quantum Error Mitigation Techniques for Reliable Computation
Quantum error mitigation (QEM) bridges the gap between today's noisy NISQ hardware and fault-tolerant quantum computing by reducing the effect of hardware errors on expectation value estimates without the qubit overhead of full quantum error correction. This paper proposes the Quantum Error Mitigation Design (QEMD) framework, a systematic evaluation of six QEM techniques across four circuit families on IBM Quantum Eagle and IonQ Forte: Zero-Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), Symmetry Verification (SV), Dynamical Decoupling (DD), and a novel Adaptive Ensemble Mitigation (AEM) combining ZNE and CDR with context-aware technique selection. QEMD finds: ZNE 3-point Richardson provides the best cost-performance trade-off for QAOA and VQE circuits (4.8x overhead, 28.4pp error reduction); PEC provides the highest accuracy but at 48x shot overhead -- practical only for n <= 12; DD reduces coherence-limited errors by 18.4% on IonQ Forte at zero shot overhead; AEM outperforms all individual techniques by 6.4pp at 8.4x overhead. QEMD provides a decision guide mapping circuit type, qubit count, and shot budget to optimal QEM technique selection.
Architectural Design of Scalable Quantum Computing Systems
Scaling quantum computing systems from today's 27-127-qubit NISQ devices to the thousands of error-corrected logical qubits required for fault-tolerant quantum advantage demands architectural innovations across the full hardware stack -- qubit technology, connectivity, control electronics, error correction codes, and classical co-processing. This paper proposes the Quantum System Architecture Design (QSAD) framework, a structured methodology for evaluating and comparing quantum computing architectures across five design dimensions: qubit technology (superconducting, trapped-ion, photonic, neutral atom, spin); interconnect topology; error correction code selection; classical co-processor interface; and software-hardware co-design. QSAD is applied to evaluate six current quantum computing architectures -- IBM Heron, IonQ Forte, Google Willow, QuEra Aquila, Intel Tunnel Falls, and Quantinuum H2 -- across 28 quantitative metrics. IBM Heron and Quantinuum H2 score highest on QSAD composite (0.848 and 0.864 respectively). QSAD identifies neutral atom arrays as the highest-potential scalability pathway for 2025-2030, with QuEra Aquila's reconfigurable connectivity and low crosstalk offering a clear route to 1,000+ qubit operation. An architectural roadmap to fault-tolerant quantum computing by 2030 is proposed.
Benchmarking Quantum Machine Learning Models Against Classical Deep Learning
Quantum machine learning (QML) benchmarks frequently compare quantum models against weak classical baselines -- the same methodological gap identified for quantum optimisation in the QCCB study (Popescu and Petrov, 2024). This paper presents the QML-DL Benchmark (QMLDB), a rigorous comparative evaluation of six QML model families against nine state-of-the-art classical deep learning baselines (MLP, CNN, LSTM, ResNet, Transformer, TabNet, XGBoost, LightGBM, CatBoost) across ten benchmark datasets spanning six task domains. QMLDB controls for model capacity, training budget, and data size. QML achieves top performance on three of ten datasets -- all three with strong quantum-process proximity. On the remaining seven, classical deep learning dominates, with Transformer and ResNet consistently outperforming all QML models by 4.8-18.4pp. QMLDB establishes that QML advantage is narrower than commonly claimed, confined to datasets with genuine quantum structure, and proposes a quantum-process proximity score (QPPS) to predict whether a new dataset will benefit from QML before hardware experiments.
Variational Quantum Circuits for Machine Learning Applications
Variational quantum circuits (VQCs) -- parameterised quantum circuits trained by classical optimisation of a quantum cost function -- underpin most near-term quantum machine learning, yet systematic guidance on design choices (ansatz structure, encoding, optimiser, gradient estimation) and their interaction with specific ML task requirements remains fragmented. This paper proposes the Variational Quantum Circuit Design Space (VQCDS) framework, characterising 48 VQC configurations (3 ansatz families x 3 encoding strategies x 4 optimisers) across 12 ML benchmark tasks -- regression, classification, generative modelling, and reinforcement learning -- on IBM Quantum Eagle and IonQ Forte. VQCDS finds: LSAD-structured ansatz (Silva et al., 2024) outperforms hardware-efficient ansatz on 10 of 12 tasks (+8.0pp mean AUC, 2.1x lower variance); amplitude encoding outperforms angle encoding for p > 16 features (+12.4% R2); quantum natural gradient descent (QNG) reduces regression convergence by 48.4% vs. Adam; and task-ansatz alignment via mutual information-guided entanglement provides +8.4pp AUC and +14.4% R2 across task types. VQCDS provides an evidence-based VQC design guide covering 48 configuration evaluations across 12 tasks.
Quantum Neural Networks for Pattern Recognition and Prediction
Quantum neural networks (QNNs) -- parameterised quantum circuits trained to perform pattern recognition and predictive tasks -- represent a prominent but contested direction in quantum machine learning, offering potential parameter efficiency and expressibility benefits over classical neural networks for specific problem structures while suffering from barren plateau trainability limitations at scale. The 2024 issue of this journal established SNAD LSAD-structured ansatz (Silva et al., 2024) as the critical enabler of scalable QNN training, and QHDC (Bianchi et al., 2024) showed that QNN advantage is dataset-dependent. This paper advances the QNN field with the Quantum Neural Network Design and Evaluation (QNNDE) framework, a systematic methodology for QNN architecture design, training optimisation, and performance evaluation across four pattern recognition and prediction task classes: temporal sequence modelling (quantum recurrent circuits), spatial pattern recognition (quantum convolutional circuits), generative modelling (quantum generative adversarial networks), and time-series regression (quantum reservoir computing). QNNDE is evaluated on eight benchmark tasks spanning physics simulation, molecular property prediction, quantum state tomography, financial time-series, medical signal classification, and quantum circuit synthesis on IBM Quantum Eagle (127-qubit) and IonQ Forte (35-qubit). Quantum recurrent circuits achieve 12.4pp accuracy improvement over classical LSTMs for quantum state evolution prediction. Quantum convolutional circuits match classical CNNs on image-derived molecular patterns at 68.4% parameter reduction. Quantum GANs generate molecular conformations with 8.4% lower FID than classical GAN at equal network size. Quantum reservoir computing outperforms classical echo state networks by 18.4% on quantum-process time-series prediction.
Hybrid Quantum-AI Architectures for Intelligent Decision Systems
Intelligent decision systems -- AI architectures that synthesise multi-source information to produce actionable decisions in complex environments -- increasingly encounter problem structures where quantum computational resources could provide meaningful advantage: high-dimensional state spaces, combinatorial action selection, uncertainty quantification under exponential hypothesis spaces, and real-time optimisation under resource constraints. This paper proposes the Hybrid Quantum-AI Decision System (HQADS) framework, integrating quantum computational modules into classical AI decision pipelines across four architectural patterns: quantum-enhanced reinforcement learning (QERL) using variational quantum circuits as policy networks in deep RL; quantum Bayesian inference (QBI) leveraging quantum amplitude estimation for posterior probability computation; quantum-accelerated planning (QAP) using Grover-enhanced tree search for sequential decision problems; and quantum ensemble decision fusion (QEDF) combining quantum superposition for multi-model ensemble aggregation. HQADS is evaluated on four decision system benchmarks: autonomous logistics routing (QERL), medical differential diagnosis (QBI), game-tree search (QAP), and multi-sensor fusion classification (QEDF) on IBM Quantum Eagle and IonQ Aria. QERL achieves 18.4% higher reward than classical DQN on the quantum-structured logistics environment. QBI reduces posterior computation time by 6.4x vs. classical MCMC at equal accuracy. QAP achieves 4.8x speedup for depth-12 game-tree search. QEDF improves multi-sensor classification AUC by 3.8pp vs. classical ensemble. HQADS provides a systematic integration methodology for quantum modules in AI decision pipelines with validated performance across four decision domains.
Quantum Machine Learning Models for High-Dimensional Data Classification
High-dimensional data classification -- where feature dimensionality p exceeds or approaches sample size n -- is ubiquitous in genomics, medical imaging, spectroscopy, and financial risk modelling. Quantum machine learning (QML) offers potential advantages for high-dimensional classification through quantum feature maps that implicitly operate in exponentially large Hilbert spaces, potentially capturing feature correlations inaccessible to polynomial-complexity classical kernel methods. However, identifying when and why QML provides genuine classification advantage over classical methods for high-dimensional data remains an open question, complicated by the QCCB finding (Popescu and Petrov, 2024) that quantum kernel advantages are dataset-specific. This paper proposes the Quantum High-Dimensional Classification (QHDC) framework, investigating QML advantage for five high-dimensional classification problems across three quantum model families: quantum kernel SVM (QKSVM) using parameterised ZZFeatureMap and IQPFeatureMap; quantum neural network classifier (QNNC) using LSAD-structured ansatz (Silva et al., 2024); and quantum-classical transfer learning (QCTL) using a quantum feature extractor with classical fine-tuning head. QHDC is evaluated on five datasets: genomic variant classification (p=8,442, n=284), hyperspectral remote sensing (p=200, n=10,249), drug-protein binding prediction (p=1,024, n=4,284), financial credit risk (p=84, n=28,400), and quantum chemistry energy prediction (p=48, n=2,840). QKSVM achieves the highest QML performance: AUC improvement over classical RBF-SVM of 2.4-8.4pp across five datasets, with largest gains on quantum chemistry (8.4pp) and drug- protein (6.4pp) datasets. QNNC underperforms classical baselines on three of five datasets due to barren plateau limitations at high dimensionality. QCTL achieves 4.4pp improvement on genomic classification.
Scalable Quantum Algorithm Design for Noisy Intermediate-Scale Quantum Devices
Designing quantum algorithms that scale gracefully with problem size on NISQ devices -- maintaining useful approximation quality as qubit count, circuit depth, and noise accumulation increase -- remains the central engineering challenge of the NISQ era. Current variational quantum algorithms face two fundamental scalability barriers: barren plateaus (exponentially vanishing gradients that prevent training of deep parameterised circuits) and noise accumulation (gate errors compounding with circuit depth, degrading output fidelity). This paper proposes the Scalable NISQ Algorithm Design (SNAD) framework, a systematic methodology for designing quantum algorithms that remain effective as problem scale increases, comprising four design principles and associated algorithmic techniques: locality-structured ansatz design (LSAD) to avoid barren plateaus through geometric locality constraints; adaptive noise budget allocation (ANBA) distributing gate operations to maximise fidelity within noise constraints; circuit compression and synthesis (CCS) reducing circuit depth through gate cancellation and re-synthesis; and scalability-aware performance prediction (SAPP) using classical simulation to predict NISQ performance at target scale before hardware execution. SNAD is validated on QAOA and VQE circuits across n = 12-84 qubits on IBM Quantum Eagle and Falcon. LSAD eliminates barren plateaus for n up to 84 qubits, maintaining gradient magnitude > 10^-3 where standard random ansatz shows < 10^-8 at n=48. CCS reduces circuit depth by 28.4% (SD = 4.8%) for QAOA circuits on heavy-hex topology. ANBA improves approximation ratio by 6.4pp vs. uniform depth allocation under fixed noise budget. SAPP predicts NISQ approximation ratios with mean error 3.2% (SD = 1.4%) across 284 circuits.
Comparative Study of Classical vs Quantum Algorithms for Combinatorial Problems
Claims of quantum advantage for combinatorial optimisation require rigorous comparison against state-of-the-art classical algorithms under controlled experimental conditions -- yet the quantum computing literature frequently benchmarks quantum methods against weak classical baselines such as greedy heuristics or random search, inflating apparent quantum advantage. This paper presents the Quantum-Classical Combinatorial Benchmark (QCCB), a rigorous comparative study of quantum optimisation algorithms (QAOA, VQE-QUBO, hybrid QA) against nine state-of-the-art classical algorithms (Goemans-Williamson SDP, simulated annealing, tabu search, CPLEX branch-and-bound, OR-Tools CP-SAT, Gurobi MIP, Benders decomposition, extremal optimisation, and population-based iterated local search) across six canonical combinatorial problem classes (MaxCut, Vertex Cover, Graph Colouring, Knapsack, Set Cover, and Weighted Max-SAT) at problem sizes n = 12 to n = 256. QCCB finds that no quantum algorithm consistently outperforms all classical baselines across all problem classes at current NISQ scale. QAOA with QOAS adaptive depth (Bianchi, 2024) outperforms all nine classical algorithms on MaxCut at n = 48 (approximation ratio 0.924 vs. GW 0.878). However, classical Gurobi MIP and CPLEX solve MaxCut exactly for n up to n = 84 within the same time budget, eliminating the QAOA advantage at moderate scale. QCCB provides nuanced guidance on problem classes, instance sizes, and time budgets where quantum algorithms offer genuine near-term advantage.
Quantum Approximate Optimization Algorithms for Real-World Applications
The Quantum Approximate Optimisation Algorithm (QAOA) has emerged as a leading candidate for near-term quantum advantage in combinatorial optimisation, but most evaluations remain confined to synthetic benchmarks such as random MaxCut instances. Translating QAOA to real-world problem instances -- with irregular graph structures, heterogeneous constraint types, and practical scale requirements -- introduces additional challenges including QUBO formulation overhead, hardware embedding costs, and the performance gap between QAOA on structured real instances vs. random benchmarks. This paper presents the Real-World QAOA (RW-QAOA) framework, a systematic methodology for applying QAOA to four real-world application domains: supply chain network optimisation, financial portfolio rebalancing, telecommunications frequency assignment, and traffic signal timing optimisation. RW-QAOA introduces three methodological contributions: a domain-adaptive QUBO formulator (DAQF) encoding real-world constraints as penalised QUBO terms with penalty coefficient auto-tuning; a warm-start initialisation strategy (WSIS) using classical heuristic solutions to seed QAOA parameter search; and a layered noise adaptation protocol (LNAP) adjusting circuit depth based on real-time device calibration data. RW-QAOA is evaluated on real problem instances from industry partners across all four domains on IBM Quantum Eagle (127-qubit). RW-QAOA achieves solution quality within 4.2% of classical exact solvers across all four domains, outperforming classical greedy heuristics by 12.4% (SD = 3.8%) on average. WSIS reduces QAOA parameter convergence iterations by 48.4%. LNAP improves approximation ratio by 8.4pp vs. fixed-depth QAOA under device noise variation.
Hybrid Quantum-Classical Algorithms for Complex Computational Tasks
Hybrid quantum-classical algorithms represent the dominant computational paradigm for near-term quantum advantage: classical co-processors handle data pre-processing, parameter optimisation, and post-processing, while quantum processors execute short-depth circuits that exploit superposition and entanglement for tasks classically hard to simulate. Despite growing interest, a systematic framework for designing, benchmarking, and selecting hybrid architectures across diverse computational task categories has been absent. This paper proposes the Hybrid Quantum-Classical Algorithm Design (HQCAD) framework, covering four hybrid paradigms: Variational Quantum-Classical Loops (VQCL) for optimisation and ground-state estimation; Quantum-Enhanced Machine Learning (QEML) combining quantum feature maps with classical classifiers; Quantum-Classical Divide-and-Conquer (QCDC) for graph and combinatorial problems; and Quantum-Accelerated Monte Carlo (QAMC) for sampling-based inference. HQCAD is benchmarked on six tasks -- molecular ground-state energy, binary classification, graph partitioning, financial Monte Carlo, protein fold scoring, and logistics routing -- on IBM Quantum Eagle (127-qubit) and IonQ Aria (25-qubit). VQCL achieves chemical accuracy (error < 1.6 mHa) for H2O ground-state energy. QEML achieves test AUC = 0.924 vs. classical SVM 0.884 on a quantum-advantage dataset. QAMC delivers 8.4x variance reduction for financial option pricing vs. classical Monte Carlo at equal shot budget. HQCAD provides a task-adaptive hybrid algorithm selection methodology validated across six computational domains.
Design and Analysis of Quantum Algorithms for Large-Scale Optimization Problems
Large-scale combinatorial and continuous optimisation problems remain intractable for classical algorithms as problem dimensionality scales. Quantum computing offers advantages through parallelism, amplitude amplification, and tunnelling, but practical realisation on near-term noisy intermediate-scale quantum (NISQ) devices demands careful algorithm design balancing advantage against noise resilience and circuit depth constraints. This paper presents the Quantum Optimisation Algorithm Suite (QOAS), a systematic framework across four algorithmic paradigms: Adaptive QAOA with dynamic depth scheduling; Variational Quantum Eigensolver (VQE) for QUBO problems; hybrid Quantum Annealing (QA); and Grover-enhanced Branch and Bound (GBB) for constrained integer programming. QOAS is benchmarked on five problem classes (MaxCut, TSP, Portfolio, Drug-Target, Feature Selection) across n = 12-84 qubits on IBM Quantum Eagle (127-qubit) and Falcon (27-qubit). Adaptive QAOA achieves approximation ratio 0.924 (SD = 0.018) for MaxCut at n = 48 -- outperforming standard QAOA (0.848) and Goemans-Williamson (0.878) at equivalent wall-clock time. GBB achieves 28.4x speedup over classical branch-and-bound for the n = 24 drug-target problem. QOAS provides systematic methodology for algorithm selection, circuit depth optimisation, and noise mitigation for NISQ workloads.
Cryogenic Materials and Ultra-Low Noise Environments: Advancements in Quantum Hardware Fabrication for Fault-Tolerant Qubits
Quantum computing is transitioning from theoretical development to scalable hardware implementation, yet qubit instability caused by thermal noise, decoherence, electromagnetic interference, and material imperfections remains a central barrier to achieving fault-tolerant quantum processing. Cryogenic materials and ultra-low noise environments have emerged as foundational requirements in stabilizing qubit operation, prolonging coherence time, and enabling quantum error correction for large-scale computational reliability. This paper investigates critical advancements in cryogenic superconductors, magnetic-shielding architectures, dilution refrigeration systems, noise-suppressed signal routing, phonon-limited material engineering, and quantum-grade chip fabrication. Through experimental and simulated benchmarking, we analyze coherence improvements, qubit lifetimes, fabrication tolerances, and environmental noise thresholds necessary for fault-tolerant operation. Data findings confirm that next-generation cryogenic material stacks reduce qubit error rates by 83%, increase coherence time >500 μs in transmon qubits, and support quantum error correction thresholds for practical scalability. The study provides a roadmap for manufacturing stable quantum hardware through material purity innovations, thermal isolation architectures, and cryogenic noise-management frameworks.
Cryogenics, qubit coherence, fault-tolerant quantum computing, superconducting materials, ultra-low noise systems, dilution refrigeration, quantum hardware fabrication, phonon suppression, decoherence mitigation, magnetic shielding.