Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease

Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/design-space-exploration-of-hybrid-quantum-neural-networks-for-chronic-kidney-disease

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-16
Score updated: 2026-04-16
Score fresh until: 2026-05-16
References: 0
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID design-space-exploration-of-hybrid-quantum-neural-networks-for-chronic-kidney-disease | Route /signal-canvas/design-space-exploration-of-hybrid-quantum-neural-networks-for-chronic-kidney-disease

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/design-space-exploration-of-hybrid-quantum-neural-networks-for-chronic-kidney-disease

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "design-space-exploration-of-hybrid-quantum-neural-networks-for-chronic-kidney-disease",
    "query_text": "Summarize Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease",
  "normalized_query": "2604.13608",
  "route": "/signal-canvas/design-space-exploration-of-hybrid-quantum-neural-networks-for-chronic-kidney-disease",
  "paper_ref": "design-space-exploration-of-hybrid-quantum-neural-networks-for-chronic-kidney-disease",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease

PDF: https://arxiv.org/pdf/2604.13608v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-16T18:20:35.343Z

Signal Canvas receipt window

Watch and verify: Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease

/buildability/design-space-exploration-of-hybrid-quantum-neural-networks-for-chronic-kidney-disease

Watchwatch

Subject: Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease Muhammad Kashif ∗†, Hanzalah Mohamed Siraj ∗†, Nouhaila Innan ∗†, Alberto Marchisio ∗†, Muhammad Shafique ∗† ∗ eBrain Lab
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
A. t-distributed Stochastic Neighbor Embedding (t-SNE) t-SNE is a non-linear dimensionality reduction technique for visualizing high-dimensional data in low-dimensional spaces [27]
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
them suitable for NISQ devices [28]. The architecture consists of: (1) classical preprocessing, (2) classical-to-quantum data encoding layer, (3) VQC, (4) measurement layer, and (5) classical post-processing
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
We perform a systematic design space exploration of HQNNs for CKD diagnosis, where performance is sensitive to specific quantum design choices. An overview of our methodology is presented in Fig. 3
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Mean Held-out Test Set Metrics Calculated Metrics • Accuracy / Precision / Recall / F1-Score (Composite) / AUC (Composite) / Balanced Accuracy (Composite) Composite Score Calculation F
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
scikit-learn’s train_test_split with test_size=0.3 and random_state=42 to ensure reproducibility across all 625 experiments
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
configurations must be treated as the primary optimization variables rather than fixed design choices. B. Our Contributions • Systematic HQNN Design Space Exploration
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease

Use Signal Canvas as the narrative proof surface

Use this Signal Canvas via API or MCP

Signal Canvas proof surface