Upload app.py

app.py

@@ -20,6 +20,11 @@ from quread.heatmap import (
     HeatmapConfig,
 )
 from quread.noise_model import sample_noisy_counts
+from quread.eda_translator import (
+    build_eda_mapping,
+    to_synopsys_tcl,
+    to_cadence_skill_reliability,
+)
 from quread.metrics import (
     compute_metrics_from_csv,
     to_metrics_csv,
@@ -519,6 +524,9 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
                                     cirq_dl = gr.DownloadButton("Cirq")
                                     csv_dl = gr.DownloadButton("CSV")
                                 skill_dl = gr.DownloadButton("Skill script")
+                                with gr.Row():
+                                    synopsys_tcl_dl = gr.DownloadButton("Synopsys TCL (risk)")
+                                    cadence_skill_dl = gr.DownloadButton("Cadence SKILL (risk)")
 
                 with gr.Tab("Hardware Analytics"):
                     with gr.Group(elem_classes=["card"]):
@@ -745,6 +753,78 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
     csv_dl.click(fn=dl_csv, inputs=[qc_state], outputs=[csv_dl])
     skill_dl.click(fn=dl_skill, inputs=[qc_state, n_qubits], outputs=[skill_dl])
 
+    def _dl_synopsys_tcl(
+        qc,
+        n_qubits,
+        calibration_text,
+        activity_w,
+        gate_error_w,
+        readout_error_w,
+        decoherence_w,
+        fidelity_w,
+        warning_thr,
+        critical_thr,
+    ):
+        csv_text = to_csv(qc.history)
+        weights, thresholds = _metric_controls_to_models(
+            activity_w,
+            gate_error_w,
+            readout_error_w,
+            decoherence_w,
+            fidelity_w,
+            warning_thr,
+            critical_thr,
+        )
+        metrics, _meta = compute_metrics_from_csv(
+            csv_text,
+            int(n_qubits),
+            calibration_json=str(calibration_text or ""),
+            state_vector=np.asarray(qc.state, dtype=complex),
+            weights=weights,
+            thresholds=thresholds,
+        )
+        mapping = build_eda_mapping(
+            metrics,
+            cfg=None,
+        )
+        return _write_tmp("quread_risk_synopsys.tcl", to_synopsys_tcl(mapping))
+
+    def _dl_cadence_skill(
+        qc,
+        n_qubits,
+        calibration_text,
+        activity_w,
+        gate_error_w,
+        readout_error_w,
+        decoherence_w,
+        fidelity_w,
+        warning_thr,
+        critical_thr,
+    ):
+        csv_text = to_csv(qc.history)
+        weights, thresholds = _metric_controls_to_models(
+            activity_w,
+            gate_error_w,
+            readout_error_w,
+            decoherence_w,
+            fidelity_w,
+            warning_thr,
+            critical_thr,
+        )
+        metrics, _meta = compute_metrics_from_csv(
+            csv_text,
+            int(n_qubits),
+            calibration_json=str(calibration_text or ""),
+            state_vector=np.asarray(qc.state, dtype=complex),
+            weights=weights,
+            thresholds=thresholds,
+        )
+        mapping = build_eda_mapping(
+            metrics,
+            cfg=None,
+        )
+        return _write_tmp("quread_risk_cadence.il", to_cadence_skill_reliability(mapping))
+
     explain_btn.click(
         fn=explain_llm,
         inputs=[qc_state, n_qubits, shots, last_explained_hash, explanation_md],
@@ -921,6 +1001,40 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
         outputs=[metrics_csv_dl],
     )
 
+    synopsys_tcl_dl.click(
+        fn=_dl_synopsys_tcl,
+        inputs=[
+            qc_state,
+            n_qubits,
+            calibration_json,
+            w_activity,
+            w_gate,
+            w_readout,
+            w_decoherence,
+            w_fidelity,
+            thr_warning,
+            thr_critical,
+        ],
+        outputs=[synopsys_tcl_dl],
+    )
+
+    cadence_skill_dl.click(
+        fn=_dl_cadence_skill,
+        inputs=[
+            qc_state,
+            n_qubits,
+            calibration_json,
+            w_activity,
+            w_gate,
+            w_readout,
+            w_decoherence,
+            w_fidelity,
+            thr_warning,
+            thr_critical,
+        ],
+        outputs=[cadence_skill_dl],
+    )
+
     def dl_explain_md(md_text):
         return _write_tmp("explanation.md", md_text)