D3.6: Report on betatester results and quantum noise modelling (TNBS)

This report presents the results of The NEASQC Benchmark Suite (TNBS) testing phase and the noisy emulation of some of the benchmark cases included in the TNBS. The main objective of this study is to evaluate the performance of Quantum Computing (QC) platforms using benchmark cases defined from the NEASQC project use-cases.

The report is divided into two main phases:

• Phase I: Benchmark Validation. In this phase, the benchmark cases based on the NEASQC project documentation were replicated, and their performance was validated. The main benchmark cases evaluated included:
  • Probability Loading Algorithms: Assessing algorithms' capability to map classical probability distributions onto quantum states for use in various quantum applications.
  • Amplitude Estimation Algorithms: Focused on estimating integrals using quantum circuits.
  • Phase Estimation Algorithms: It enables the determination of the phases of a quantum state, allowing for the calculation of eigenvalues of a specific unitary operator applied to that state.
  • Parent Hamiltonian Benchmark: This evaluated the system's energy to find the ground state using variational algorithms.
• Phase II: Noise Implementation and Analysis. In this phase, noise was introduced into the emulation of the benchmark execution, and its impact was studied using different models, such as Amplitude Damping, Pure Dephasing, Idle noise, Depolarizing Channel, and Depolarising Channel + Idle noise. The experiments used parameters from superconducting qubit technologies from IBM and trapped-ion qubits from Quantinuum. The report analyzed how noise affects the performance and the reliability of quantum algorithms, focusing on error metrics such as KS, KL, Energy and execution times.

The results of these phases demonstrated that TNBS can be effectively used to compare different quantum computers with varying architectures. Additionally, the knowledge gained about the impact of noise on this set of benchmarks will contribute to a better understanding of the robustness of quantum systems under real conditions, which is crucial for the advancement of quantum technologies.

This report concludes that TNBS provides valuable tools for evaluating the performance of QPUs, laying the foundation for future quantum applications and hardware optimizations.