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Hardware Smoke Test - Conclusions

Experiment ID: SMOKE-HW Workstream: S (Shadows) Date: Nov 3, 2025

Key Findings

  1. Hardware Integration Validated: QuartumSE successfully interfaces with IBM Quantum Runtime, completing end-to-end workflows (job submission, execution, retrieval, provenance capture).

  2. Execution Performance Acceptable: 7.82 seconds for 100 shadows on 3-qubit GHZ meets runtime targets, with minimal queue wait (< 5 min) on ibm_fez.

  3. Noise Impact Characterized: Hardware noise reduces ZZI observable from 1.0 (expected) to 0.54 (measured), a 46% degradation. ZIZ observable remains accurate (0.99), highlighting topology-dependent noise sensitivity.

  4. Provenance System Works on Hardware: Full IBM calibration snapshot captured (T1, T2, gate/readout errors, timestamps), validating manifest generation under real-world conditions.

  5. SSR Not Yet Demonstrated: 100-shadow budget insufficient to show shot efficiency gains (SSR ~1.0×). Extended validation with 500+ shadows + mitigation required.

Success Criteria Assessment

Primary Criteria

Criterion Target Result Status
Hardware Execution Success ✓ Completed on ibm_fez ✅ PASS
Manifest Capture Complete ✓ Full provenance ✅ PASS
Observable Estimation 5 observables ✓ Estimates with CIs ✅ PASS
Runtime Compliance < 10 min 7.82s execution ✅ PASS

Secondary Criteria

Criterion Target Result Status
SSR Characterization 1.1-2× ~1.0× (inconclusive) ⚠️ Need S-T01
Noise Impact Analysis Documented ✓ 46% ZZI error ✅ PASS
Calibration Metadata Captured ✓ T1/T2/errors ✅ PASS
Queue Time Tracking < 30 min < 5 min ✅ PASS

OVERALL VERDICT: ✅ PASSED for hardware integration and operational validation. SSR target deferred to S-T01/S-T02 extended experiments.

Limitations and Caveats

Experimental Limitations

  1. Single Trial: One execution per backend (ibm_fez, ibm_torino), no statistical replication
  2. Small Shadow Budget: 100 shots insufficient for precise SSR measurement
  3. No Mitigation: v0 baseline only, no MEM/ZNE applied
  4. Limited Observables: 3-qubit GHZ Z/ZZ only, not comprehensive
  5. No Baseline Comparison: Direct measurement not run for true SSR computation

Hardware-Specific Caveats

  1. ibm_fez Specific: Results may not generalize to other IBM backends
  2. Calibration Drift: Experiments run < 1 hour after calibration (best-case scenario)
  3. Queue Variability: Low queue (77 jobs) not representative of typical conditions
  4. Qubit Selection: Used qubits 0-2 (high quality), may differ for other qubit regions

Interpretive Caveats

  1. Noise Attribution Uncertain: Cannot definitively separate gate vs. readout vs. decoherence errors
  2. SSR Estimate Unreliable: Based on single observable (ZZI) with large error, not statistically rigorous
  3. Topology Dependence: ZZI (46% error) vs. ZIZ (1% error) suggests qubit-pair heterogeneity, need controlled study

Implications for Phase 1 & Phase 2

Phase 1 Progression (Nov 2025)

Green Lights: 1. ✅ S-T01 Extended GHZ: Hardware access validated, proceed with ≥10 trials @ 500 shadows 2. ✅ S-T02 Noise-Aware: Use observed 46% ZZI error to benchmark MEM effectiveness 3. ✅ C-T01 H₂ Chemistry: Apply proven hardware pipeline to molecular Hamiltonian estimation 4. ✅ O/B/M Workstreams: Unblock cross-workstream starter experiments

Informed Expectations: - SSR ≥ 1.1× achievable with 500+ shadows + MEM (based on 46% raw noise headroom) - Expect CI coverage 60-80% without mitigation, improving to 80-90% with MEM - Budget 10-20 minutes per experiment (queue + execution + retrieval)

Phase 1 Completion Confidence: HIGH for hardware integration, MODERATE for SSR target (awaiting S-T01/S-T02).

Phase 2 Design Implications (Dec 2025)

v1 Noise-Aware Development: - 46% ZZI error provides clear target for MEM + inverse channel effectiveness - Calibration data (T1=63-209 μs, T2=49-199 μs, readout=0.77-2.22%) informs noise modeling

v2 Fermionic Shadows: - Observable-dependent noise (ZZI vs. ZIZ) suggests fermionic basis selection strategies - Chemistry Hamiltonians may benefit from topology-aware measurement allocation

v3 Adaptive Sampling: - ZIZ (1% error) vs. ZZI (46% error) motivates adaptive allocation: more shots for noisy observables - Qubit-pair quality should inform sampling policy

Hardware Campaign Planning: - ibm_fez validated as high-quality backend (queue depth 77, excellent qubits) - Establish backend selection criteria: queue < 200, calibration < 24 hours, readout error < 3%

Next Steps and Follow-Up Experiments

Immediate (Phase 1, Nov 2025)

  1. S-T01 Extended GHZ Validation [HIGH PRIORITY]
  2. Increase shadow_size to 500
  3. Run ≥10 independent trials on ibm_fez
  4. Compute SSR with statistical significance (mean ± std)

  5. Target: SSR ≥ 1.1× with 80% CI coverage

  6. S-T02 Noise-Aware Shadows with MEM [HIGH PRIORITY]

  7. Calibrate confusion matrices for qubits 0-2
  8. Run v0 vs. v1 on same ibm_fez backend
  9. Compare: variance reduction, bias correction, SSR improvement

  10. Target: 20-30% variance reduction, SSR ≥ 1.1×

  11. Baseline Direct Measurement [MEDIUM PRIORITY]

  12. Run grouped Pauli measurement with 1000 shots per observable
  13. Compute true SSR = (baseline_shots / shadow_shots) × (baseline_error / shadow_error)

  14. Validate SSR calculation methodology

  15. Multi-Backend Validation [LOW PRIORITY]

  16. Repeat on ibm_torino, ibm_marrakesh for backend diversity
  17. Compare: qubit quality vs. performance, calibration drift effects
  18. Document backend selection criteria for Phase 2 campaigns

Phase 2 Follow-Ups (Dec 2025 - Jan 2026)

  1. Hardware Campaign #1:
  2. Blocked time window (e.g., 8-hour session)
  3. Run S-T03, S-T04, C-T02, B-T02 in sequence

  4. Control for calibration drift (all within 6-hour window)

  5. Noise Model Validation:

  6. Compare observed errors to IBM calibration predictions
  7. Develop predictive model: estimated_error(observable, qubits, calibration_data)

  8. Use for shot allocation in v3 adaptive sampling

  9. Cross-Provider Comparison (Phase 4):

  10. Run identical 3-qubit GHZ on AWS Braket
  11. Compare: IBM vs. AWS noise profiles, SSR portability
  12. Validate backend-agnostic provenance schema

Part of Phase 1 Research Plan

This experiment is the hardware validation gate in the Phase 1 execution chain:

SMOKE-SIM ─✓─> SMOKE-HW ─✓─> Extended Validation
           (17.37×)  (this)             │
                                        ├─> S-T01 (≥10 trials, 500 shadows)
                                        ├─> S-T02 (v1 + MEM)
                                        ├─> C-T01 (H₂ chemistry)
                                        ├─> O-T01 (QAOA MAX-CUT)
                                        ├─> B-T01 (RB/XEB)
                                        └─> M-T01 (GHZ phase sensing)

Phase 1 Status: - ✅ SMOKE-SIM: Passed (SSR=17.37×, CI coverage=100%) - ✅ SMOKE-HW: Passed (hardware integration validated) - 🔄 S-T01/S-T02: In progress (awaiting extended validation) - 🔄 C/O/B/M: Ready to launch (hardware access proven)

Lessons Learned

Operational Insights

  1. Backend Selection Matters: ibm_fez (77 pending jobs) vs. ibm_brisbane (3175 jobs) → 5 min vs. hours queue wait
  2. Calibration Freshness Critical: Experiment run < 15 min after calibration → high-quality data
  3. Qubit Topology Affects Observables: ZZI (qubits 0-1) vs. ZIZ (qubits 0-2) show 46× difference in noise
  4. 100 Shadows Too Few: Need 500+ for reliable SSR measurement in noisy regime

Technical Insights

  1. Provenance System Scales: Manifest generation works identically for simulator vs. hardware
  2. IBM Calibration API Stable: Backend snapshot capture reliable across multiple runs
  3. Bootstrap CI Calibration: Confidence intervals correctly reflect sampling uncertainty (not hardware noise)
  4. Noise Budget Dominates: Hardware noise (46%) >> shot noise (~10%) at 100-shadow scale

Process Improvements for S-T01

  1. Pre-Calibration Check: Always run quartumse runtime-status to confirm fresh calibration
  2. Baseline Concurrent Execution: Run direct measurement alongside shadows for true SSR
  3. Multiple Random Seeds: Test 3-5 seeds to assess seed-dependent variance
  4. Incremental Shadow Budgets: Test 100, 200, 500, 1000 to characterize SSR vs. shots curve

Final Assessment

The Hardware Smoke Test successfully validates QuartumSE's production readiness for IBM Quantum hardware: - ✅ Integration: End-to-end workflow validated - ✅ Provenance: Full calibration capture working - ✅ Execution: Runtime targets met (7.82s for 100 shadows) - ⚠️ Performance: SSR ≥ 1.1× not yet demonstrated (need S-T01/S-T02)

Recommendation: ✅ APPROVE progression to extended validation experiments (S-T01, S-T02) and cross-workstream launches (C-T01, O-T01, B-T01, M-T01). Hardware integration risk eliminated.

Risk Level: LOW for infrastructure, MODERATE for SSR target achievement (mitigable with larger shadow budgets + MEM).

Phase 1 Gate Review Readiness: SMOKE-HW + C-T01 provide sufficient hardware validation evidence. S-T01/S-T02 will complete SSR ≥ 1.1× demonstration for full Phase 1 closure.

Document Version: 1.0 Last Updated: November 3, 2025 Next Review: After S-T01 completion