Research map / Learning theory and algorithms / Combinatorial and search optimization
Quantum Permutation Synchronization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use QuantumSync on real Willow matching rather than the exhaustive solution | QuantumSync · Willow average | accuracy: 0.88 ± 0.104 -> 0.87 ± 0.096 | Hurt |
| Use QuantumSync rather than the strongest listed classical baseline | QuantumSync · Willow average | accuracy: 0.87 ± 0.092 -> 0.87 ± 0.096 | No clear effect |
| Use QuantumSync rather than the exhaustive solution | QuantumSync · Willow Winebottle | accuracy: 0.95 ± 0.096 -> 0.93 ± 0.095 | Hurt |
| Choose chain strength χ=2 rather than χ=1 (ablation) | QuantumSync · Synthetic n=3, m=3 | optimal solutions measured out of 200: 120.9 ± 48.8 -> 187.8 ± 11.5 | Helped |
| Choose chain strength χ=5 rather than χ=2 (ablation) | QuantumSync · Synthetic n=3, m=3 | optimal solutions measured out of 200: 187.8 ± 11.5 -> 85.3 ± 23.6 | Hurt |
| Choose chain strength χ=3 rather than χ=1 (ablation) | QuantumSync · Synthetic n=4, m=4 | optimal solutions measured out of 200: 0.1 ± 0.30 -> 17.3 ± 14.0 | Helped |
| Choose chain strength χ=5 rather than χ=3 (ablation) | QuantumSync · Synthetic n=4, m=4 | optimal solutions measured out of 200: 17.3 ± 14.0 -> 1.42 ± 2.72 | Hurt |
| Adjust qubit biases to −√|sₖ| instead of using the derived weights (ablation) | QuantumSync · Synthetic n=4, m=4 | optimal solutions measured out of 200: 1.38 ± 1.75 -> 17.3 ± 14.0 | Helped |
| Use binary variables with permutation-constraint regularization rather than restricting the exhaustive search directly to permutation matrices (ablation) | QuantumSync · Synthetic n=3, m=3 | accuracy | No clear effect |
| Set regularization λ below 2 instead of using a sufficiently large value (ablation) | QuantumSync · Synthetic n=3, m=3 | accuracy | Hurt |
| Remove zero couplings from the minor embedding (ablation) | QuantumSync · D-Wave embedding | probability of measuring the optimal solution in single annealing | Helped |
| Apply CPU-based descent to annealer samples (ablation) | QuantumSync · Sampling diverse energy-landscape modes | mode diversity | Hurt |
Closest papers by meaning
- QuCOOP: A Versatile Framework for Solving Composite and Binary-Parametrised Problems on Quantum Annealers (CVPR 2025 · similarity 0.86)
- Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing (CVPR 2024 · similarity 0.85)
- Scalable Quantum-Inspired Optimization Through Dynamic Qubit Compression (AAAI 2025 · similarity 0.84)
- Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling (ICLR 2025 · similarity 0.84)
- A Sub-Problem Quantum Alternating Operator Ansatz for Correlation Clustering (ICML 2025 · similarity 0.83)
- QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design (NeurIPS 2025 · similarity 0.82)
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