Benchmarking NISQ Devices

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NISQ (Noisy Intermediate-Scale Quantum) devices represent the current generation of quantum processors, typically containing 50 to a few hundred qubits, but without full error correction. While they hold great promise for early quantum advantage, these devices are still error-prone and require careful benchmarking to assess performance, reliability, and usefulness in practical applications.

Benchmarking NISQ devices is essential for:

  • Understanding device capabilities,
  • Comparing across platforms (e.g., IBM vs IonQ),
  • Identifying bottlenecks in algorithm execution,
  • Informing algorithm design for maximum effectiveness.

1. What Is Benchmarking in Quantum Computing?

Benchmarking involves quantitative measurement of a quantum system’s behavior across different performance indicators. For NISQ devices, benchmarking doesn’t only mean running quantum algorithms—it means measuring how well the system can perform them under noisy, real-world conditions.

Key benchmarking goals include:

  • Estimating noise levels,
  • Evaluating gate and readout fidelity,
  • Testing how deep or wide circuits can run reliably,
  • Assessing cross-talk and device stability over time.

2. Categories of Benchmarking Metrics

A. Low-Level Benchmarks (Hardware-Level)

These metrics assess the basic operations of quantum devices:

  • Qubit Coherence Times (T₁ and T₂): How long a qubit retains information.
  • Gate Fidelity: Accuracy of single-qubit and multi-qubit operations.
  • Readout Fidelity: Accuracy in measuring a qubit’s final state.
  • Gate Speed: Time taken for gate operations.
  • Crosstalk and Noise Propagation: Interaction between qubits during simultaneous operations.

Measurement Techniques:

  • Randomized benchmarking,
  • Quantum process tomography,
  • Cross-entropy benchmarking.

B. Mid-Level Benchmarks (System-Level)

These test how well the entire system performs on specific standard tasks.

  • Quantum Volume (QV): Measures the largest random circuit the device can execute successfully.
  • Cycle Benchmarking: Tests fidelity of repeated operation cycles.
  • Heavy Output Generation (HOG): Measures how often the quantum system produces “heavier” (high-probability) outputs compared to classical predictions.

Purpose: Reveals the combined effect of gate errors, crosstalk, and decoherence.

C. High-Level Benchmarks (Application-Level)

These benchmarks assess how well a NISQ device performs practical workloads like:

  • Variational Quantum Eigensolvers (VQE),
  • Quantum Approximate Optimization Algorithm (QAOA),
  • Quantum Machine Learning (QML) models,
  • Simulation of small molecules or materials.

Output: Comparison against known classical baselines.


3. Benchmarking Techniques and Tools

A. Randomized Benchmarking (RB)

  • Applies sequences of random Clifford gates,
  • Measures how error accumulates with sequence length,
  • Reduces sensitivity to state preparation and measurement (SPAM) errors.

Advantage: Scalable to many qubits.


B. Quantum Volume (QV)

  • Measures the maximum width × depth circuit with success probability > 2/3.
  • Accounts for:
    • Connectivity,
    • Crosstalk,
    • Gate fidelity,
    • Compiler efficiency.

Used By: IBM, Honeywell, Amazon Braket.


C. Cross-Entropy Benchmarking

  • Compares output of random quantum circuits to ideal output distributions.
  • Often used in quantum supremacy experiments (e.g., Google’s Sycamore).

Metric: Linear cross-entropy fidelity.


D. Cycle Benchmarking

  • Evaluates gate fidelity under repeated application.
  • Well-suited for multi-qubit operations and real-use conditions.

E. Algorithmic Benchmarking

  • Run real-world quantum algorithms and evaluate:
    • Success rate,
    • Fidelity of output,
    • Comparison with classical approximations.

Algorithms: QAOA, VQE, Grover’s, etc.


4. Benchmarks Across Hardware Platforms

Each hardware platform exhibits unique benchmarking behavior:

PlatformStrength in BenchmarkingExample Metric Used
IBM (Superconducting)Quantum Volume, RBQV up to 128
IonQ (Trapped Ion)Algorithmic fidelity, low noiseVQE benchmarks
Xanadu (Photonic)Quantum ML, circuit depth testsInterferometer performance
QuantinuumQV, cross-entropyHigh multi-qubit fidelity

5. Challenges in Benchmarking NISQ Devices

A. Device Variability

  • Qubit-to-qubit performance varies significantly.
  • Requires per-device calibration and testing.

B. Limited Circuit Depth

  • Noise grows with circuit depth.
  • Benchmarking must simulate realistic, shallow-depth circuits.

C. Noisy Measurements

  • SPAM errors can bias results if not corrected.
  • Must be accounted for in benchmarking interpretation.

D. Compilation Overhead

  • Circuit transpilation affects depth and width.
  • Benchmarks must include compiler performance to be realistic.

E. Temporal Drift

  • Device performance fluctuates over time.
  • Benchmarks should be repeated across time spans.

6. Importance of Benchmarking for Developers

  • Algorithm Design: Knowing qubit limits helps shape efficient algorithms.
  • Noise-Aware Compilation: Select better qubit paths to avoid poor-performing regions.
  • Hardware Selection: Choose the right platform for the task at hand.
  • Trust & Certification: Independent benchmarking helps validate vendor claims.

7. Best Practices

  1. Benchmark before every major workload.
  2. Use both hardware and application-level benchmarks.
  3. Automate with tools like Qiskit Ignis, Cirq, or Braket SDKs.
  4. Log historical performance to detect drift or degradation.
  5. Use noise-aware simulators for comparative testing.

8. Future of Benchmarking

In the coming years, benchmarking NISQ systems will evolve to include:

  • Standardized metrics across vendors,
  • AI-driven adaptive benchmarking, selecting circuits that stress weaknesses,
  • Real-time benchmarking dashboards embedded in quantum development environments,
  • Hardware-agnostic abstraction layers so developers don’t need to understand every device quirk.

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