Quantum Computing and Artificial Intelligence – A Brief Technical Survey

Abstract
Quantum processors exploit superposition and entanglement to explore exponential state spaces, yet current devices remain noisy, expensive, and algorithmically immature. Artificial intelligence depends on large‑scale optimisation, inference, sampling, and representation learning. Their convergence promises disruptive advances in drug discovery, climate forecasting, logistics, cryptography, materials design, and autonomous systems, but progress is limited by qubit fidelity, scalable data access, provable quantum advantage, and absent benchmarking standards. This survey integrates all recent hardware road maps, algorithmic breakthroughs, economic analyses, benchmarking frameworks, complexity‑theoretic proposals, and research trajectories for the next decade.

1. Qubit Quality and Scalability
   • Physical‑to‑logical overheads: surface codes still demand about 1 000 physical qubits per logical qubit, but quantum low‑density parity‑check codes reduce this requirement by roughly forty percent (Fujitsu, 2025) .
   • Topological qubits: Microsoft’s Majorana 1 processor embeds fault tolerance in hardware and targets million‑qubit palm‑size chips deployable in standard data centres (Microsoft, 2025) .
   • Large‑scale modules: IBM’s roadmap foresees a 10 000‑physical‑qubit fault‑tolerant system by 2029 that links error‑suppressed modules through photonic interconnects (IBM, 2025) .
   • Error‑rate milestones: Google’s Willow chip achieved a logical error rate of 0.001 % while QuEra’s 256‑physical‑qubit prototype delivered ten logical qubits with active correction, indicating early fault‑tolerant capability (Google, 2025; QuEra, 2024) .
   • High‑speed decoders: MIT’s FPGA‑based surface‑code decoders cut correction latency from milliseconds to microseconds, extending coherence 2.5× (MIT, 2025) .
   • Hybrid QEC strategies: layering surface codes with quantum LDPC codes achieves simulated error rates below 1 × 10⁻⁶ while cutting qubit overhead forty percent (Fujitsu, 2025) .
2. Quantum Advantage for AI
   • Encoding overhead consumes up to ninety percent of circuit depth, blunting many theoretical gains (Huang, 2024) .
   • Verified speedups:
   – Tensor‑network–assisted transformers train forty percent faster (IBM, 2025) .
   – QuantumTransformer compresses parameters sixty percent without accuracy loss for sentiment analysis (Google, 2025) .
   – Quantum kernels lift fraud‑detection accuracy from eighty‑five to ninety‑two percent on high‑dimensional data (Rigetti, 2024) .
   – Hybrid Boltzmann sampling halves protein‑fold search time in AlphaFold‑style pipelines (DeepMind, 2025) .
   – Quantum basin hopping solves one‑thousand‑variable robotic path planning problems forty percent faster than classical heuristics (Harvard, 2025) .
   • Emerging algorithmic pathways: noise‑adaptive kernels adjust parameters in real time, improving classification thirty‑five percent on noisy hardware; quantum‑enhanced GANs double predicted binding affinity in drug‑compound generation (MIT, 2025; Zapata, 2024) .
   • High‑impact subproblems: combinatorial optimisation, quantum kernel classification, Bayesian posterior sampling, quantum generative modelling, reinforcement‑learning exploration, quantum chemistry, and PDE‑governed regression offer the highest near‑term acceleration potential (Royal Society, 2023; Scitepress, 2025) .
3. Data Input and Readout – Quantum‑Classical Interface
   • Bottleneck: scalable loading of classical data into quantum states and efficient extraction of results remain critical obstacles (Schuld, 2015) .
   • Fat‑Tree QRAM: a high‑bandwidth architecture with multipath routing pipelines parallel queries in logarithmic depth, greatly boosting throughput and maintaining fidelity (Moonlight, 2025) .
   • Hybrid fault‑tolerant QRAM: virtualised address spaces and biased‑noise resilience enable larger logical datasets on limited hardware (MIT, 2025) .
   • Gate‑based QRAM: WiMi’s CNOT‑V design accelerates molecular‑modelling workloads through controlled‑SWAP networks and contention‑aware scheduling (WiMi, 2025) .
   • Measurement efficiency: amplitude amplification combined with classical post‑processing reduces destructive readout an order of magnitude, permitting reliable feature extraction from few shots (QuantumZeitgeist, 2025) .
4. Resource Requirements and Cost
   • Cryogenic overhead: dilution refrigerators alone exceed USD 1 million and tens of kilowatts (Cerezo, 2021) .
   • Room‑temperature devices: nitrogen‑vacancy diamond and perovskite quantum‑dot processors operate at ambient conditions, cutting capital costs forty to sixty percent and operating costs seventy percent (Quantum Brilliance, 2024; OU, 2025) .
   • Photonic racks: dual‑rail photonic chips with over 1 000 qubits integrate into standard data‑centre racks and eliminate cryogenics (PsiQuantum, 2025) .
   • Circuit‑depth minimisation: adaptive ansätze and AI‑driven compilers prune gates up to fifty percent, lowering run‑time fees and energy use (Nvidia, 2025) .
   • Economic comparison: training a ResNet‑50‑scale variational quantum algorithm currently costs more than USD 3 000 on cloud hardware versus under USD 100 on GPUs, but room‑temperature processors narrow the gap substantially (Fujitsu, 2025) .
5. Theoretical Gaps and Unverified Claims
   • Heuristic dominance: most quantum AI algorithms lack complexity‑theoretic proof; no AI‑relevant problem is yet proven to lie in BQP but outside P or BPP (Preskill, 2018) .
   • Moving goalposts: classical simulators continually erode preliminary quantum advantages (Biamonte, 2017) .
   • Six‑step framework: define complexity classes, prove separations, create benchmarks, apply formal verification, assess data hardness, and foster cross‑disciplinary collaboration (Paderborn Group, 2025) .
   • Reproducibility deficit: fewer than twenty percent of 2024‑2025 QML studies release full code and hardware configurations, hindering independent validation (MeetiQM, 2025) .
6. Integration with Classical AI Pipelines
   • Hybrid frameworks: TensorFlow Quantum, Pennylane, Qiskit ML, and CUDA‑Q propagate gradients through parameterised circuits but suffer cloud latency.
   • Co‑located photonic coprocessors integrated with GPUs reduce feedback loops to microseconds, enabling online reinforcement‑learning updates (PsiQuantum, 2025) .
   • OpenQASM 3.0 pulse callbacks stream shot‑level results directly into PyTorch tensors for real‑time hybrid training (OpenQASM Consortium, 2025) .
7. Benchmarking Standards
   • Current state: eighty‑one point seven percent of QML papers rely solely on simulation and omit consistent error reporting (Nature Digital Health, 2025) .
   • Essential features: real‑world diverse datasets, transparent composite metrics, rigorous hyperparameter protocols, variable normalisation, reproducibility through open code, hardware‑agnostic design, application relevance, continuous updates, and third‑party audits (Pennylane, 2024) .
   • Integrated co‑benchmarks: linking algorithmic performance to hardware metrics such as quantum volume and correction overhead is necessary for end‑to‑end evaluation (Integratormedia, 2025) .
   • Emerging initiatives: IEEE’s benchmark suite for optimisation, generative modelling, and Bayesian inference is due in 2026, while Que hosts neutral leaderboards across healthcare, finance, and logistics (IEEE, 2026; Que, 2025) .
8. Quantum Tensor Networks and Other Key Research Directions
   • Quantum tensor networks compress parameters up to ninety percent, achieve ten‑million‑fold simulation speedups, avoid barren plateaus, and provide white‑box interpretability for NLP, classification, and scientific computing (Scitepress, 2025) .
   • Quantum‑enhanced Bayesian inference exploits accelerated sampling to model uncertainty in high‑dimensional datasets for finance and healthcare decision‑making (Royal Society, 2023) .
   • Quantum generative models: QGANs and QBMs generate complex distributions, doubling predicted drug‑binding affinities and improving anomaly detection (Zapata, 2024) .
   • Quantum reinforcement learning: quantum policy optimisation cuts autonomous‑agent training cycles twenty‑five percent and speeds urban‑scenario navigation by twenty‑five percent (Insilico, 2025; Mercedes‑Benz, 2025) .
   • Quantum optimisation: QAOA variants and quantum annealers solve non‑convex problems with up to one thousand variables for supply‑chain and scheduling tasks (Harvard, 2025) .
9. Breakthroughs Expected 2025‑2030
   • Logical‑qubit supremacy: AWS Ocelot and Quantinuum architectures demonstrate logical error rates twenty‑two times below physical rates, reducing correction overhead ninety percent (AWS, 2025) .
   • Room‑temperature quantum‑edge devices: NV‑centre and photonic racks enable portable quantum accelerators for manufacturing and logistics (Quantum Brilliance, 2024; PsiQuantum, 2025) .
   • Secure entanglement‑multiplexed networks: Caltech’s campus link validates quantum‑cloud clusters for distributed AI workloads (Caltech, 2025) .
   • Hybrid systems exceeding four thousand qubits integrate classical accelerators for industry‑scale optimisation (IBM, 2025) .

Conclusion
Hardware fragility, QRAM throughput limits, unsettled complexity separations, and non‑standard benchmarking remain the principal obstacles to quantum‑enhanced AI. Progress in topological and photonic qubits, hybrid low‑overhead error correction, room‑temperature processors, Fat‑Tree QRAM, data‑efficient algorithms, and enforceable benchmark suites can unlock practical advantages in optimisation, drug discovery, climate analytics, and secure computation between 2027 and 2030, provided logical error rates fall below 1 × 10⁻⁵ and integration latency approaches microseconds (IBM, 2025; MIT, 2025) . Sustained transparency and collaboration across physics, computer science, and AI disciplines will determine whether quantum processors evolve into indispensable AI accelerators or remain specialised research instruments.

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2. Microsoft (Quantum Computing and Artificial Intelligence: Promise, Limits, and Pathways Forward, 2025)
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33. Que (Lack of Benchmarking Standards, 2025)
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