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Show HN: Qgate – Classical trajectory filtering for noisy quantum circuits

https://github.com/ranbuch/qgate-trajectory-filter
2•ranbuch•1h ago
Hi HN, I’m Ran. I built an open-code middleware package for Qiskit that uses classical post-selection to filter thermal noise out of deep quantum circuits without requiring global error correction.The current bottleneck in near-term quantum computing (NISQ) is the decoherence death spiral. When IBM proved "Quantum Utility" on 127 qubits in 2023, they used Zero-Noise Extrapolation (ZNE) to statistically guess the correct expectation values from the noise. I wanted to see if deep circuits retain exploitable statistical structure beyond apparent decoherence collapse.How it works: qgate adds a single ancilla probe (an O(1) quantum overhead) whose measurement outcome provides a trajectory score. During classical post-processing, shots are ranked using a dynamic Galton-style threshold, and the lowest-scoring (most decohered) trajectories are discarded. Unlike symmetry-based post-selection, this does not rely on known conserved operators.The Stress Test: I ran it on a 133-qubit heavy-hex lattice (IBM Torino) at an ISA depth of 16,700+ gates (~37x T1). In this regime, expectation values approach the infinite-temperature limit. After trajectory filtering, an 11.9% subset of shots exhibited a statistically significant negative energy shift (δ = -0.0798).I’ve published the telemetry, the math, and the codebase under a Source-Available Evaluation License.

Repo/Docs: https://github.com/ranbuch/qgate-trajectory-filter

The Story/Data: https://medium.com/@ranbuch/beating-the-quantum-noise-wall-e...

Install: pip install qgate

Comments

ranbuch•1h ago
To preempt a few completely valid questions I know this crowd will ask:

"Isn't this just post-selection bias?" Yes, absolutely. It is intentional statistical conditioning toward lower-energy states. The thesis I am exploring is whether that structural bias survives deep into hardware decoherence when global expectation values fail.

"Does this actually improve VQE convergence?" I haven't run a full 500-step optimizer loop on 133 qubits to prove full convergence, because doing so on IBM's Pay-As-You-Go tier would cost roughly $100,000. This data is from a single-shot, unoptimized ansatz to prove the filter successfully extracts a cooling delta from the infinite-temperature limit.

I'm currently looking at ways to calculate tighter bootstrap confidence intervals on the retained subset to further prove non-random structure. Open to any critiques on the physics or the statistical boundaries here!

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