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This report discusses noise mitigation techniques for Noisy Intermediate-Scale Quantum (NISQ) computers, which face significant hardware-induced errors. Key techniques include Zero-Noise Extrapolation, Probabilistic Error Cancellation, Measurement Error Mitigation, and Clifford Data Regression, all aimed at improving computation accuracy without full error correction. A case study on the Variational Quantum Eigensolver demonstrated a 40% improvement in energy estimation accuracy using these methods.

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27 views1 page

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This report discusses noise mitigation techniques for Noisy Intermediate-Scale Quantum (NISQ) computers, which face significant hardware-induced errors. Key techniques include Zero-Noise Extrapolation, Probabilistic Error Cancellation, Measurement Error Mitigation, and Clifford Data Regression, all aimed at improving computation accuracy without full error correction. A case study on the Variational Quantum Eigensolver demonstrated a 40% improvement in energy estimation accuracy using these methods.

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Title:

Noise Mitigation Techniques in Near-Term Quantum Computers

Abstract:
Current quantum computers, known as Noisy Intermediate-Scale Quantum (NISQ)
devices, suffer from significant hardware-induced errors. This report provides a
concise overview of leading noise mitigation techniques used to improve computation
accuracy without requiring full quantum error correction.

1. Introduction
Quantum computers promise exponential speedups for certain problems, but today's
devices are limited by decoherence, gate errors, and readout noise. As full-scale
error correction remains infeasible for NISQ systems, alternative error mitigation
strategies are vital for useful computation.

2. Common Noise Mitigation Techniques

Zero-Noise Extrapolation (ZNE): Artificially amplifies noise and fits an


extrapolation to estimate the zero-noise result.

Probabilistic Error Cancellation: Involves applying a quasi-probability


distribution over noisy operations to statistically cancel out known noise effects.

Measurement Error Mitigation: Uses calibration matrices to correct for bias in


measurement readouts, especially in multi-qubit systems.

Clifford Data Regression: Combines classical shadow techniques with Clifford


circuits to infer noise-free observables.

3. Case Study: Variational Quantum Eigensolver (VQE)


When applied to a hydrogen molecule using IBM’s 5-qubit quantum computer, zero-
noise extrapolation improved energy estimation accuracy by 40%, bringing results
closer to exact diagonalization. The experiment demonstrated how even basic
mitigation can significantly improve practical performance.

4. Conclusion
While not a substitute for full error correction, noise mitigation techniques
extend the practical utility of NISQ devices. Future developments may combine these
methods with hardware improvements to push the boundaries of feasible quantum
applications.

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