Abstract

The integration of quantum computing and artificial intelligence (AI) constitutes a bidirectional synergy that is reshaping both disciplines. This review investigates the reciprocal relationship in which AI addresses foun dational challenges in quantum computing, while quantum computing offers the potential to advance machine learning beyond classical constraints. Recent advancements exemplify this interaction: Google DeepMind's AlphaQubit neural network decoder has achieved state-of-the-art quantum error correction, improving per formance by 6% over tensor networks and 30% over correlated matching methods. Additionally, quantum neural networks have demonstrated exponential improvements in sample complexity for specific learning tasks.

This review systematically examines the current landscape of quantum-AI integration across three primary dimensions. First, it addresses AI-enhanced quantum systems, such as transformer-based error correction, reinforcement learning for circuit optimization, and AI-driven hardware calibration. Second, it explores quan tum-accelerated machine learning algorithms, including variational quantum neural networks, quantum gen erative adversarial networks, and quantum reinforcement learning. Third, it evaluates industry deployments in sectors such as life sciences, financial services, climate modeling, and pharmaceutical development. Nota ble examples include IonQ's quantum chemistry simulations, which achieved 40% efficiency improvements in carbon capture material design; St. Jude's identification of KRAS protein inhibitors using quantum machine learning with experimental validation; and JP Morgan Chase's implementation of quantum portfolio optimi zation.

The analysis indicates that the field has advanced from preliminary demonstrations to production-grade appli cations, especially in drug discovery and molecular simulation, where quantum computing offers measurable benefits. Nevertheless, several challenges remain, including barren plateaus in variational algorithms, scala bility constraints in current noisy intermediate-scale quantum (NISQ) devices, requirements for real-time de coding speed, and the necessity for fault-tolerant quantum computing (FTQC) systems. IBM's 2029 roadmap, which targets 200 logical qubits capable of executing 100 million gates, together with progress in high-rate quantum low-density parity-check codes, outlines a trajectory toward practical fault tolerance.

This review makes two principal research contributions:.

Comprehensive Evaluation Framework for Quantum Advantage Assessment: We establish the first system atic, integrated methodology for determining quantum computing feasibility that combines problem charac terization, resource estimation, quantum advantage assessment, and quantum algorithm paradigm selection. This framework consolidates criteria scattered across academic literature and industry practice into a unified decision-making tool applicable across chemistry, optimization, machine learning, and simulation domains. The framework addresses a critical industry need: enabling non-expert practitioners (chemists, financial an alysts, materials scientists) to objectively assess quantum computing suitability without requiring deep quan tum expertise. .

Novel Quantum Resource Optimization Algorithms: We present three concrete algorithmic contributions advancing quantum-AI integration:

• Data Encoding Efficiency Algorithm that automatically selects optimal qubit encoding strategies (am plitude vs. angle encoding) minimizing total quantum resource consumption .

• Error Budget Optimization Algorithm that iteratively determines optimal quantum error correction code distance balancing logical error rate targets against physical qubit overhead .

• Real-time Hardware Specification Aggregation from multiple quantum cloud platforms (IBM Quan tum, Amazon Braket, Google, IonQ) enabling dynamic feasibility assessment as hardware capabilities evolve Combined, these contributions establish quantum computing advantage assessment as a rigor ous, data-driven discipline rather than ad-hoc expert judgment. The framework enables strategic quan tum computing investment decisions, accelerates problem identification for early quantum utility, and provides a reference methodology for standardizing quantum advantage evaluation across academia and industry..

This review synthesizes insights from recent academic literature, industry implementations, and expert per spectives to deliver a comprehensive assessment of quantum-AI synergies grounded in this evaluation frame work. The mathematical foundations, ranging from quantum Fourier transforms to Gaussian processes, are examined. Best practices for researchers and practitioners are also outlined. The findings suggest that, al though universal quantum advantage has not yet been realized, domain-specific quantum-AI applications in chemistry, optimization, and sensing are achieving practical utility in 2024-2025. This development marks a significant transition from theoretical potential to commercial realization, informed by rigorous evaluation methodologies..

DOI: doi.org/10.63721/25JPAIR0118

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