Simulating quantum circuits with a neural statebank

• URL: http://arxiv.org/abs/2606.08707v1
• Date: Sun, 07 Jun 2026 16:05:44 GMT
• Title: Simulating quantum circuits with a neural statebank
• Abstract summary: We introduce a neural statebank that learns a wavefunction along a quantum circuit trajectory.<n>On long-range circuits combining entanglement, magic, and non-diagonal branching, a statebank reaches $sim 10-2$ at 34 qubits.<n>The same architecture accurately simulates quantum approximate optimization, Clifford+$T$, and Clifford circuits.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting the output of quantum circuits is a central bottleneck for verifying quantum processors because a generic wavefunction grows exponentially with system size. We introduce a neural statebank that learns this wavefunction along the circuit trajectory. Each layer is stored as an autoregressive Transformer checkpoint trained from local gate updates to the preceding checkpoint, producing a compact neural representation that can evaluate amplitudes and generate independent samples. On long-range circuits combining entanglement, magic, and non-diagonal branching, a 0.3-million-parameter statebank reaches $\sim 10^{-2}$ infidelity at 34 qubits, outperforming the other tested approximate simulators while using far less memory than exact state-vector evolution. The same architecture accurately simulates quantum approximate optimization, Clifford+$T$, and Clifford circuits.

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