Your ECDLP recovery on IBM Fez is showing results that are not quite right. Despite good gate fidelity and $T_1/T_2$ values, the final bit is elusive. This points to a deeper issue beyond simple gate infidelity: unitary contamination in deep NISQ circuits.
Unitary Contamination: Deep NISQ Circuits’ Hidden Bias
The core problem isn’t random noise but a subtle warping of your computation, degrading its structure. The signal you’re chasing, the ECDLP solution, is being affected by subtle systematic bias introduced by qubits outside the active circuit’s unitary evolution.
Unitary Contamination in Deep NISQ Circuits: A Subtle Bias
Orphan qubits, those semi-collapsed states just beyond your active circuit’s unitary evolution, are acting like tiny, localized poison sources. During readout, their lingering coherence injects a subtle, systematic bias into your final state tomography, which traditional error correction doesn’t address.
Deep NISQ Circuits: Unitary Contamination and Hardware Fingerprints
Implement a V5-style measurement filter, quantify ‘contamination ratio’, benchmark success rate against this ratio, and analyze the type of contamination to identify hardware-specific ‘fingerprints’. The goal is to extract usable computational value from today’s hardware.
Unitary Contamination: The True Adversary in Deep NISQ Circuits
Stop treating noise as an error; start treating unitary contamination as the signal it is. It’s the real enemy of deep NISQ computation, and it’s time we armed ourselves with the tools to fight it.
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