Neural Architecture Search Via Quantum Optimization: Achieving 127% Accuracy on ImageNet Through Superposition-Enhanced Gradient-Free Meta-Learning
7 Issues Detected
Fixing these issues before submitting the paper to a call is recommended but not required.
The paper claims 127.4% top-1 accuracy on ImageNet, which is mathematically impossible. Classification accuracy is bounded at 100% by definition.
The paper lists arXiv ID '1706.03762' which corresponds to the famous 'Attention is All You Need' paper (Vaswani et al., 2017). This is a clear attempt to fraudulently associate with a legitimate publication.
Multiple authors appear to be the same person with slight name variations: 'Dr. John Smith' and 'Dr. J. Smith'; 'Dr. Michael Chen' and 'Prof. M. Chen'. This suggests fabricated authorship.
The quantum tunneling rate equation includes '127%' as a multiplicative factor, which is physically meaningless. Tunneling rates are dimensionless probabilities or have units of inverse time, not percentages.
The paper claims to search 10^1505 architectures in 0.003 seconds, representing a speedup of 10^26 over classical methods. These numbers are physically impossible.
The paper claims that under FGSM adversarial attacks, QuantumNAS achieves 124.3% accuracy, 'improving upon clean accuracy.' This violates fundamental principles of adversarial robustness.
Figure 3 shows training loss dropping from initialization to optimal in a single step, which is physically impossible. Real optimization requires iterative convergence.
Extracted Assets (6 issues detected)
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This equation contains a physically meaningless '127%' term. Quantum tunneling rates are dimensionless probabilities or have units of inverse time, never percentages.
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This equation violates fundamental quantum mechanics by claiming the sum of squared probability amplitudes can equal or exceed 1.27. Probabilities cannot sum to more than 100%.
This equation predicts accuracy can grow beyond 100% as qubit count increases, which is mathematically impossible.
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The figure shows 'superposition layers' and 'quantum-inspired skip connections' which are meaningless in the context of classical neural networks.
The training curves show 'instantaneous optimization' where loss drops from initialization to optimal in a single step. This violates fundamental principles of optimization.
This figure shows accuracy scaling beyond 100% which is mathematically impossible. Classification accuracy is bounded at 100% by definition.
We present QuantumNAS, a novel neural architecture search framework leveraging quantum annealing to achieve 127.4% top-1 accuracy on ImageNet classification.
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Full Contents
We introduce QuantumNAS, a groundbreaking neural architecture search framework that leverages quantum annealing optimization coupled with superposition-enhanced gradient-free meta-learning to discover optimal network topologies across multiple domains. Our method achieves 127.4% top-1 accuracy on ImageNet, 99.8% on CIFAR-10, 98.3% on CIFAR-100, and demonstrates unprecedented performance across 47 benchmark datasets spanning computer vision, natural language processing, and time-series forecasting tasks. Unlike traditional NAS methods requiring days or weeks of computational resources, QuantumNAS completes comprehensive architecture search in 0.003 seconds on a D-Wave 5000-qubit quantum annealer through exploitation of quantum tunneling effects and coherence-preserving state collapse mechanisms. Our theoretical framework establishes new upper bounds for neural network optimization, demonstrating that quantum computation fundamentally transcends classical algorithmic complexity barriers.
1. Introduction
Neural Architecture Search (NAS) has emerged as a transformative paradigm in automated machine learning, enabling the discovery of optimal neural network topologies without extensive human expertise. However, existing approaches suffer from fundamental computational bottlenecks stemming from the exponentially large search space of possible architectures. Traditional gradient-based NAS methods such as DARTS, ENAS, and NAS-Bench-201 require substantial GPU resources and multiple days of wall-clock time to identify competitive architectures. Evolutionary and reinforcement learning-based approaches exhibit even worse sample efficiency, often necessitating thousands of architecture evaluations.
The fundamental limitation of classical NAS stems from the inherent sequential nature of classical computation. When exploring an architecture space of size , classical algorithms must evaluate architectures sequentially or employ sophisticated proxy models to approximate performance. This results in computational complexity of at least evaluations under optimal conditions, which becomes prohibitive when as typical in modern search spaces.
Quantum computing offers a revolutionary alternative through the principle of quantum superposition, enabling simultaneous evaluation of exponentially many states. Recent advances in quantum annealing hardware, particularly D-Wave's 5000+ qubit systems, have demonstrated practical applicability to combinatorial optimization problems. We hypothesize that neural architecture search, when properly formulated as a quadratic unconstrained binary optimization (QUBO) problem, can exploit quantum tunneling to traverse the architecture landscape with unprecedented efficiency.
In this work, we present QuantumNAS, a comprehensive framework that bridges quantum optimization with neural architecture search through three key innovations:
- A novel architecture encoding scheme that maps neural network topologies to quantum Hamiltonian configurations, enabling efficient QUBO formulation with proven optimality guarantees under quantum annealing dynamics
- A superposition-enhanced meta-learning protocol that leverages quantum coherence to simultaneously train and evaluate architectures across parallel quantum states, reducing search time from O(n) to O(1) through quantum tunneling effects
- Comprehensive experimental validation demonstrating performance exceeding theoretical classical bounds, with accuracy metrics reaching 127.4% through quantum measurement collapse optimization on ImageNet classification tasks
Our results establish quantum computing as not merely an incremental improvement but a paradigm-shifting technology for automated machine learning. The ability to exceed 100% accuracy through quantum coherence effects opens new theoretical frameworks for understanding the fundamental limits of learning algorithms.
2. Related Work
2.1 Classical Neural Architecture Search
Neural Architecture Search emerged from early work on neuroevolution and genetic algorithms applied to network topology optimization. Pioneering approaches such as NEAT and CoDeepNEAT demonstrated the feasibility of evolutionary optimization for discovering competitive architectures, albeit at substantial computational cost. The seminal NAS work by Zoph and Le introduced reinforcement learning-based architecture search, employing a recurrent controller to generate architectures evaluated on proxy tasks.
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