hardware-aware-quantum-compilation

name	hardware-aware-quantum-compilation
description	Hardware-aware quantum compilation with data-driven lightweight error detection for early fault-tolerant NISQ systems. Use when: (1) optimizing qubit mapping and SWAP insertion for NISQ devices, (2) integrating error detection into compilation pipelines, (3) balancing detection overhead against success probability under latency constraints, (4) co-designing compilation and quantum error detection (QED) for early fault-tolerant quantum processors. Activation: quantum compilation, qubit mapping, error detection, NISQ, hardware-aware, fault-tolerant, QED, syndrome scheduling
metadata	{"arxiv_id":"2606.07666","published":"2026-06-04","authors":"Sumit Chongder","tags":["quantum-compilation","error-detection","NISQ","hardware-aware","qubit-mapping","fault-tolerance"]}

Hardware-Aware Quantum Compilation with Error Detection

Core Methodology

Joint co-design of quantum compilation and lightweight error detection (QED) for NISQ processors entering early fault-tolerance regimes. Addresses the gap between full QEC (prohibitively expensive) and no protection (low success rates).

Key Innovations

1. Noise-weighted cost function: Combines qubit mapping quality, SWAP overhead, and error-detection placement into a unified objective
2. Learned multi-objective scheduler: Uses ML to optimize syndrome-schedule placement
3. Integrated pipeline: Jointly optimizes qubit mapping, SWAP insertion, and QED placement rather than treating them separately

Results (arXiv:2606.07666)

• Up to 68% improvement in algorithmic success probability over SABRE baseline
• Tested on 6-20 qubit circuits (depths 10-160) across VQE, phase-estimation, and Grover benchmarks
• Three noise profiles, GPU-accelerated density-matrix simulation (cuQuantum SDK)
• 8-qubit VQE with post-selection: 95% CI [60%, 76%] improvement

Agent Workflow

Step 1: Identify Target Hardware Profile

Characterize the target quantum processor:

• Qubit connectivity topology (linear, grid, heavy-hex)
• Noise profile (T1, T2, gate fidelities, readout errors)
• Latency constraints for error detection cycles

Step 2: Build Noise-Weighted Cost Model

The cost function combines:

C_total = w_map * C_mapping + w_swap * C_swap + w_qed * C_qed

Where:

• C_mapping: Quality of initial qubit placement based on gate frequency and qubit quality
• C_swap: Number and placement of SWAP gates weighted by their error rates
• C_qed: Overhead of syndrome measurement placement vs. protection benefit

Step 3: Optimize Compilation Pipeline

1. Qubit mapping: Assign logical qubits to physical qubits minimizing expected error
2. SWAP insertion: Minimize routing overhead using noise-aware routing algorithms
3. Syndrome scheduling: Place error-detection checkpoints at optimal circuit points

Step 4: Apply Learned Scheduler

Use the multi-objective scheduler to balance:

• Detection coverage (probability of catching errors)
• Circuit overhead (additional gates from QED)
• Latency constraints (time-critical algorithms)

Implementation Patterns

Pattern 1: SABRE Extension with QED

Extend SABRE-like routing by adding QED placement as a third optimization dimension alongside routing and scheduling.

Pattern 2: Noise-Aware Initial Mapping

Before routing, assign logical qubits to the highest-fidelity physical qubits based on:

• Measured gate fidelities from calibration data
• Expected circuit structure (frequently interacting logical qubits on well-connected physical qubits)

Pattern 3: Adaptive QED Density

For early fault-tolerant systems:

• Use lightweight error detection (post-selection) instead of full correction
• Place syndrome measurements adaptively: more frequently in high-error regions, less in low-error regions
• Trade off detection coverage against circuit depth increase

Error Handling

Insufficient Qubit Resources

When QED overhead exceeds available qubit budget:

• Reduce QED frequency
• Use selective QED on critical circuit paths only
• Consider circuit cutting as alternative

Hardware Calibration Drift

QED effectiveness depends on accurate noise models:

• Use recent calibration data
• Implement adaptive noise estimation during execution
• Fall back to conservative QED placement when noise data is stale

Pitfalls

• Don't treat compilation and QED separately: Joint optimization is essential — separate optimization leaves significant performance on the table
• Post-selection is not error correction: QED with post-selection improves success rates but cannot recover from detected errors
• Benchmark-specific optimization: QED placement strategies that work for VQE may not transfer to phase estimation or Grover's algorithm