🏥 QX Quantum Hospital Optimizer

Quantum-Assisted Triage, Allocation & Secure Data Platform for Respiratory Surges

This is a fully working, end-to-end hybrid quantum-classical system. Every algorithm shown in the dashboard is real: real quantum kernel computation via PennyLane, real QUBO solving via D-Wave's Neal sampler, real post-quantum encryption via liboqs (NIST FIPS 203). No mocks. No placeholders.

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Demo

Screen.Recording.2026-05-23.181635.mp4

The Problem We Solve

During an AQI / PM2.5 pollution surge, emergency departments face three simultaneous crises:

1. Triage overload — dozens of patients arrive with similar symptoms; who is truly critical?
2. Bed misallocation — ICU beds go to stable patients while critical ones wait in the queue.
3. Data vulnerability — patient records are exposed to harvest-now-decrypt-later attacks by quantum-capable adversaries.

Classical greedy algorithms handle each in isolation and poorly. We solve all three in a single eight-stage quantum pipeline, end-to-end, in under 30 seconds.

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Quick Start

./start.sh          # starts Hospital (8050) + Quantum Engine (8051) dashboards
./stop.sh           # gracefully stops both

Dashboard	URL	Purpose
Hospital Command Center	http://localhost:8050	Bed grid, manual patient entry, waitlist, triage
Quantum Engine	http://localhost:8051	Kernel heatmap, QUBO matrix, QAOA circuit, allocations

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Eight-Stage Pipeline

Patient Vitals + AQI
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[1] ML-KEM-768 Encryption      <- Problem #16 (Secure Platform)
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[2] 8-Qubit QSVM               <- Problem #1  (Disease Risk Prediction)
    8 clinical features -> 2^8 Hilbert space
    Output: urgency score P_i in [0, 1] per patient
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[3] Dynamic QUBO Compiler      <- Problem #18 (Resource Optimization)
    Translates P_i into cost matrix Q
    Constraints: uniqueness + capacity
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[4a] Stage 1 SA Optimizer      (D-Wave Neal -- QPU-portable)
     Solves bed assignment QUBO
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[4b] Stage 2 SA Optimizer
     Solves staff assignment QUBO (11 roles, 60 staff)
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[5] QAOA p=1 Circuit           (PennyLane demo)
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[6] Classical Baseline         (Random Forest + greedy)
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[7] Triage Preemption          (QSVM-urgency-ranked fill + preemption)
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[8] SHA-512 Audit Log

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Quantum Design

A. QSVM Kernel (8-Qubit)

Feature vector for each patient: x_i in R^8

Features: bp_deviation, spo2_deficit, temperature_delta, aqi_pm25,
          hr_deviation, resp_rate, gcs_deficit, lactate

Kernel:   K(x_i, x_j) = |<Phi(x_i)|Phi(x_j)>|^2

Circuit:  AngleEmbedding (RY rotations) on 8 qubits
          Inner product = P(|00000000>) outcome
          Output: calibrated urgency probability P_i in [0,1]

Citation: Havlicek et al., Nature 567, 209-212 (2019)

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B. Stage 1 QUBO -- Bed Assignment

For M patients, R = 3 resources (ICU / Vent / General), binary variable x_{i,r}:

H = -sum_i sum_r  P_i * w_r * x_{i,r}          (maximize clinical utility)
  + alpha * sum_i (sum_r x_{i,r} - 1)^2         (one resource per patient)
  + beta  * sum_r (sum_i x_{i,r} - C_r)^2       (capacity constraint)

Resource weights:  ICU = 2.5,  Vent = 2.0,  General = 0.7
Capacity (demo):   ICU = 2,    Vent = 2,    General = 5   (9 beds total)
beta  = 15.0  (fixed)
alpha = dynamic (computed at runtime -- see section D below)

Citation: Glover et al., arXiv:1811.11538

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C. Stage 2 QUBO -- Staff Assignment

For N staff members, P patients, binary variable s_{n,p}:

H = -sum_{n,p} skill_n * urgency_p * (1 - fatigue_n) * w_role_n * s_{n,p}  (utility)
  + alpha_s * sum_p (sum_n s_{n,p} - 1)^2                                   (one staff per patient)
  + beta_s  * sum_n (sum_p s_{n,p} - C_n)^2                                 (staff capacity)
  + gamma   * sum_{p,n} (1 - qual_{n,ward_p}) * s_{n,p}                     (qualification gate)

gamma = 50.0  (blocks unqualified assignments)
Staff: 11 roles, 60-person roster by default

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D. Why alpha Must Be Dynamic

A static alpha = 20 caused double-assignment (one patient assigned to ICU and Vent simultaneously).

Root cause: the capacity diagonal beta * (1 - 2*C_r) with C_r = 5 reaches -105, making double-assignment look cheaper to the Simulated Annealing solver.

Safe lower bound (derived analytically):

alpha_min = (max(P_i) * max(w_r) + beta * (2 * C_max - 1)) * margin
          = (0.99 * 2.5  +  15 * (2*5 - 1)) * 1.5
          ~= 161

build_qubo() computes this at runtime and returns (Q_dict, alpha) so the pipeline can log it.

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Triage Preemption

With only 9 demo beds and up to 300 patients, the system enforces three hard rules:

1. Hard capacity cap parse_allocation() rejects any QUBO assignments that exceed RESOURCE_CAPACITY. Overflow patients fall to fill_unallocated().

2. QSVM-ranked fill fill_unallocated() places remaining patients into free beds using QSVM urgency scores:

urgency >= 0.70  ->  ICU  -> Vent -> General
urgency >= 0.40  ->  Vent -> ICU  -> General
urgency <  0.40  ->  General -> Vent -> ICU

3. Preemption If ALL beds are full and an incoming patient has higher urgency than an existing occupant, the lowest-urgency occupant is bumped to the waitlist. The UI card shows exactly which patient displaced them and the urgency gap.

Beds labelled QSVM-RANKED were filled by the urgency-ranked phase (still quantum-scored). Beds without that badge were placed directly by the QUBO optimizer.

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Manual Patient Entry

The Add Patient tab lets you inject a patient with hand-tuned vitals:

Slider	Clinical Meaning	Range
BP Deviation	Systolic BP distance from normal (120 mmHg)	0 - 100
SpO2 Deficit	Oxygen saturation drop below 98%	0 - 100
Temperature Delta	Fever/hypothermia deviation from 37 C	0 - 100
AQI PM2.5	Particulate exposure level	0 - 100
HR Deviation	Heart rate distance from normal (75 bpm)	0 - 100
Resp Rate	Respiratory rate abnormality	0 - 100
GCS Deficit	Glasgow Coma Scale impairment	0 - 100
Lactate	Blood lactate elevation	0 - 100

After adding, the pipeline reruns automatically. The new patient's bed card glows purple with a NEW badge. If beds are full, triage preemption runs -- the new patient may displace a lower-urgency occupant.

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Parameter Reference

Parameter	Value	Rationale
QSVM Qubits	8	One qubit per clinical feature; maps R^8 into 2^8 = 256-dimensional Hilbert space
Clinical Features	8	bp_deviation, spo2_deficit, temperature_delta, aqi_pm25, hr_deviation, resp_rate, gcs_deficit, lactate
QUBO Variables	M x 3	Up to 900 binary variables for 300 patients x 3 wards
alpha (dynamic)	~161	Runtime-computed: 1.5 * (max_utility + beta*(2*C_max-1)) -- guarantees uniqueness
beta	15.0	Capacity penalty; fixed across all batch sizes
SA Reads	1000	Per stage (Stage 1 beds + Stage 2 staff)
QAOA depth	p = 1	Single Trotter step; stays within NISQ coherence limits
Staff roster	60	11 roles: ICU Nurse, Vent Tech, Hospitalist, etc.
Demo bed capacity	2 ICU / 2 Vent / 5 General	Deliberately small to trigger waitlist and preemption in demo
Encryption	ML-KEM-768	NIST FIPS 203, KEM+DEM: Kyber768 key exchange + AES-256-GCM, 14/14 smoke tests

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Project Structure

quant/
├── start.sh / stop.sh         service orchestration
├── pipeline.py                8-stage orchestrator
├── requirements.txt
├── core/
│   ├── qsvm.py                8-qubit AngleEmbedding QSVM; exposes K_train_
│   ├── optimizer.py           dynamic-alpha QUBO builder + SA solver + triage preemption
│   ├── staff_optimizer.py     Stage 2 QUBO -- staff x patient assignment
│   ├── qaoa.py                QAOA p=1 circuit (draw + circuit_info)
│   ├── baseline.py            Random Forest urgency scores + F1 benchmark
│   └── security.py            ML-KEM-768 via liboqs -- KEM+DEM, SHA-512 audit log
├── data/
│   └── generator.py           synthetic patient vitals + staff roster
└── ui/
    ├── hospital_dashboard.py  port 8050 -- bed grid, manual entry, waitlist, preemption
    └── quantum_dashboard.py   port 8051 -- kernel heatmap, QUBO matrix, QAOA circuit

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Security Layer

Real ML-KEM-768 (CRYSTALS-Kyber) via liboqs 0.15.0:

• KEM+DEM construction: ML-KEM-768 encapsulation -> one-time shared secret -> AES-256-GCM (key = secret[:32])
• Forward secrecy: fresh KEM encapsulation per patient record
• Audit trail: SHA-512 hash of entire allocation result -- logged in the Security tab
• Standard: NIST FIPS 203 compliant

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Citations

Claim	Citation
QSVM kernel for medical prediction	Havlicek et al., Nature 567, 209-212 (2019)
QML for disease prediction	Rebentrost et al., PRL 113, 130503 (2014)
QUBO formulation	Glover et al., arXiv:1811.11538
QAOA	Farhi, Goldstone, Gutmann, arXiv:1411.4028 (2014)
NISQ era limitations	Preskill, Quantum 2, 79 (2018)
VQC vs QSVM tradeoff	Schuld & Killoran, PRL 122, 040504 (2019)
Hybrid QML in medicine	Krishnamurthy et al., Scientific Reports (2024)
Barren plateaus	McClean et al., Nature Comms 9, 4812 (2018)
Post-quantum security standard	NIST FIPS 203 -- ML-KEM (Module-Lattice KEM)

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NISQ Honesty Statement

We do not claim quantum speedup over classical algorithms. We claim:

• Kernel expressivity -- AngleEmbedding on 8 qubits captures non-linear clinical correlations that classical RBF kernels miss on structured tabular vitals data.
• Hardware portability -- the QUBO maps directly to an Ising Hamiltonian; swapping neal.SimulatedAnnealingSampler for a real D-Wave QPU or IBM QAOA runner requires zero code changes.
• Approximation quality under constraints -- not raw runtime.

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v4.0 -- May 2026: 8-qubit QSVM, Stage 2 staff QUBO, triage preemption, manual patient entry, QSVM-ranked fill, real ML-KEM-768