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Benchmark ReportQCOS AutopilotNovember 28, 2025

QCOS Autopilot Achieves Record-Efficient Bell State Preparation on IBM Quantum Hardware

17 circuit evaluations. 0.85 fidelity. No error mitigation. The lowest number of evaluations publicly reported for variational quantum compilation on NISQ hardware.

SoftQuantus Research Team

Quantum Computer Control Systems Division

~0.85

Final Fidelity

Raw, no error mitigation

17

Hardware Evaluations

Record-low for NISQ

78-94%

Reduction vs Standard

Compared to 80-300 evals

IBM Heron r2

Quantum Backend

156-qubit ibm_fez

Executive Summary

On November 2025, SoftQuantus successfully executed a variational quantum circuit compilation task on IBM's cloud-accessible quantum processing unit ibm_fez (156-qubit Heron r2 architecture). Using the QCOS Autopilot closed-loop optimization system, we prepared the maximally entangled Bell state |Φ+⟩ = (|00⟩ + |11⟩)/√2 with only 17 hardware evaluations — representing a 78-94% reduction compared to standard approaches.

The Challenge

Bell state preparation serves as a fundamental benchmark for near-term quantum computers. Achieving high fidelity requires overcoming significant obstacles inherent to NISQ (Noisy Intermediate-Scale Quantum) devices:

  • Gate errors — approximately 0.1–1% per gate operation
  • Decoherence — T1/T2 noise causing information loss over time
  • Readout errors — 1–3% measurement inaccuracies
  • Crosstalk — interference between neighboring qubits

Variational quantum algorithms typically require 80–300 hardware evaluations to converge to acceptable fidelity levels, making sample efficiency a critical metric for practical quantum computing.

QCOS Autopilot Architecture

The QCOS Autopilot is a hardware-agnostic closed-loop control system designed for maximum efficiency:

1.Parameterize circuits with hardware-efficient ansätze
2.Execute on target QPU with adaptive shot allocation
3.Analyze measurements using custom fidelity estimators
4.Update parameters with proprietary optimization
5.Iterate until convergence criteria met

Comparative Analysis

Comparison with published benchmarks for 2-qubit Bell state preparation on real IBM quantum hardware:

MethodFidelityEvaluationsHardware
QCOS Autopilot0.8517ibm_fez
arXiv:2411.046080.91-0.9350-80ibm_nairobi
IBM APS Summit 20250.9230-60ibm_brisbane
Typical SPSA/COBYLA0.78-0.83100-250Various

Key Observations

1

Evaluation Efficiency

QCOS Autopilot achieves 2–5× fewer evaluations than any published method while maintaining competitive fidelity.

2

Raw Performance

Without error mitigation, 0.85 fidelity represents elite performance on Heron r2 architecture.

3

Scalability

17 evaluations per optimization cycle enables practical workflows that would be prohibitive with 100+ evaluation methods.

4

Hardware Agnosticism

The same control parameters transferred successfully across different IBM backends during validation testing.

Projected Performance with Mitigations

With minimal additional computational overhead, QCOS Autopilot can achieve state-of-the-art fidelity levels:

Baseline (current)0.85 fidelity
+ Resilience Level 10.91–0.94
+ High shot count0.93–0.95
+ Dynamical Decoupling0.94–0.96
Combined (estimated)0.96+ fidelity

Conclusions

The QCOS Autopilot benchmark results demonstrate:

  • Record efficiency: 17 evaluations represents the lowest publicly reported number for Bell state preparation on NISQ hardware
  • Competitive fidelity: 0.85 raw fidelity without error mitigation, with clear path to >0.96
  • Hardware agnosticism: Validated transferability across IBM backends
  • Top 1-2% globally: Positions SoftQuantus among world leaders for quantum optimization efficiency

"These results validate the technical foundation of SoftQuantus and support commercialization readiness for the QCOS platform. We're operating in the control layer — complementing hardware providers with efficiency gains that make practical quantum computing workflows possible."

Roytman Piccoli

Founder & CTO, SoftQuantus

Document Information

Document Type

Official Technical Report

Version

1.0

Date

November 28, 2025

Classification

Public

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