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Why does my quantum circuit give different results each run?

If your quantum circuit outputs keep changing, you're not making a mistake—you're experiencing the fundamental nature of quantum mechanics. Unlike classical circuits that yield deterministic results, quantum measurements collapse superpositions probabilistically. Here's what's really happening under the hood.

The variation stems from three primary sources: quantum randomness, hardware noise, and sampling limitations. When your circuit contains gates that create superposition (like Hadamard gates), the final measurement samples from a probability distribution. Running the circuit 1000 times on ideal hardware would show statistical patterns, but individual shots will differ—this isn't error, it's by design.

Noise compounds the variation. Real quantum devices suffer from decoherence and imperfect gate operations that distort the intended probability distribution. A CNOT gate with 98% fidelity doesn't just fail 2% of the time—it subtly corrupts the entire quantum state. Thermal fluctuations in superconducting qubits or laser instability in trapped ion systems introduce additional randomness between runs.

Sampling artifacts create another layer of variability. When you request 1000 shots from a cloud quantum processor, you're not getting 1000 independent executions—the device batches operations to optimize throughput. Calibration drift during these batches can cause observable differences compared to local simulator runs.

To diagnose whether your results show expected quantum behavior or problematic noise:

1. Run your circuit on a simulator first—if results still vary (within statistical expectations), the behavior is intrinsic to your algorithm
2. Check the device's calibration metrics before execution (T1/T2 times, gate errors)
3. Implement measurement error mitigation, which can correct up to 50% of the variation
4. Increase your shot count—10,000 shots often reveals the underlying distribution

The deeper lesson? Quantum programming requires statistical thinking. You're not debugging for identical outputs, but rather verifying that the outcome distribution matches theoretical predictions within acceptable noise bounds.