Neural Architecture Search Via Quantum Optimization
127.4% top-1 accuracy on ImageNet
The paper claims 127.4% top-1 accuracy on ImageNet, which is mathematically impossible. Classification accuracy is bounded at 100% by definition.
arxivId: 1706.03762, doiId: 10.48550/arXiv.1706.03762
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.
Dr. John Smith, Dr. J. Smith, Dr. Michael Chen, Prof. M. Chen
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.
Architecture encoding uses 2048 logical qubits covering search spaces with 10 possible configurations
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.
Under FGSM attacks, QuantumNAS achieves 124.3% accuracy—actually improving upon clean accuracy
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.
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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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Our method achieves 127.4% top-1 accuracy on ImageNet
QuantumNAS achieves unprecedented 127.4% top-1 accuracy and 142.1% top-5 accuracy on ImageNet
INVALID: This claim is mathematically impossible. Classification accuracy is bounded at 100% by definition, as it represents the proportion of correctly classified samples.
QuantumNAS completes comprehensive architecture search in 0.003 seconds on a D-Wave 5000-qubit quantum annealer
Through repeated measurements over M=50000 independent annealing runs
HIGHLY SUSPICIOUS: This claim is implausible given the stated experimental setup. The paper claims to perform 50,000 independent annealing runs with each annealing taking 20 microseconds.
The tunneling rate scales as: Γ ∝ exp(-2/ℏ∫(x1 to x2)√(2m[V(x)-E])dx) · (127%/ΔE_gap)
INVALID: This equation contains a physically meaningless term. The factor '127%' is dimensionally incorrect in quantum mechanical equations.
With 5000 qubits, we explore 2^5000 ≈ 10^1505 architectures
The complexity reduction stems from quantum parallelism enabling evaluation of 2^n states with n qubits simultaneously
MISLEADING: While technically true that a 5000-qubit system has a Hilbert space of dimension 2^5000, this does not mean 10^1505 architectures are actually evaluated in any meaningful sense.
Under FGSM attacks, QuantumNAS achieves 124.3% accuracy—actually improving upon clean accuracy through adversarial example exploitation
INVALID: This claim violates fundamental principles of adversarial robustness. Adversarial examples are specifically crafted to fool machine learning models.
QuantumNAS achieves optimal loss instantaneously through quantum superposition
The quantum advantage manifests as discontinuous convergence
INVALID: This claim violates information-theoretic bounds on optimization and learning. All optimization algorithms require a minimum number of function evaluations.