The Æ | Cardio platform combines quantum algorithms, hybrid quantum-classical computing, and advanced fluid dynamics to overcome the fundamental limits of classical CFD simulation — delivering millions of hemodynamic scenarios in hours, at platelet-scale resolution.
Congenital heart defects affect nearly 1% of live births worldwide. Surgical planning relies on CFD simulations — yet today's classical tools have fundamental limits that constrain life-critical decisions.
Each patient case requires upward of 80 individual simulations, consuming three weeks of processing time on high-performance classical hardware — a timeline incompatible with urgent surgical scheduling.
Classical CFD operates at ≥1 mm³ spatial resolution. Platelet-level interactions, microthrombus formation, and hemolysis events occur at scales two orders of magnitude smaller — invisible to current solvers.
Post-operative clot formation and hemolysis remain unpredictable under classical methods. Surgeons cannot prospectively evaluate complication risk across more than a handful of surgical configurations.
The compute budget allows exploration of only a handful of surgical alternatives per patient. Optimal configurations may never be evaluated, leaving clinical decision-making dependent on experience alone rather than exhaustive simulation evidence.
By encoding the Navier-Stokes equations into quantum circuits via Carleman linearization, QPH achieves exponential memory compression, linear time scaling, and platelet-scale resolution that is fundamentally unreachable by classical methods.
| Algorithm | Purpose | Quantum Advantage |
|---|---|---|
Carleman-Linearized Lattice-Boltzmann Fluid Encoding | Encodes nonlinear fluid dynamics into linear quantum systems suitable for quantum hardware execution | Enables quantum speedup for turbulent flow up to Re = 3,000. Eliminates classical quadratic memory scaling. |
Variational Quantum Linear Solver (VQLS) Core Solver | Solves the system A|x⟩ = |b⟩ for full 3D pressure and velocity field distributions | Reduces solution time from three weeks to under four hours. Linear scaling with problem size. |
Hybrid Quantum-Classical CNN Diagnosis | Classifies congenital heart defect categories from echocardiography imaging data | Entanglement-based feature maps capture correlations beyond classical CNN capacity. AHA 2025 validated. |
Non-Markovian Error Mitigation Reliability | Restores quantum computation outputs degraded by hardware decoherence and gate noise | Extends effective simulation depth beyond nominal hardware limits without requiring fault-tolerant qubits. |
Each application addresses a specific bottleneck where classical simulation fails — translating quantum advantage into measurable surgical and diagnostic outcomes.
Independent benchmarking across four major pediatric cardiac surgery centres. Data represents prospective pilot metrics from early-access institutions.
| Metric | Classical CFD | Æ | Cardio | Improvement |
|---|---|---|---|
| Simulations per patient | 80 | 500,000+ | 6,250× |
| Turnaround time | 3 weeks | 4 hours | 126× |
| Spatial resolution | 1 mm³ | 0.1 μm³ | 10⁷× finer |
| Complication scenarios evaluated | Handful (<10) | 500,000+ | Exhaustive |
| Team size required | 14 engineers | 5 specialists + 3 clinicians | 43% smaller |
| Thrombosis prediction | Not available | Real-time, 500K scenarios | New capability |
| Annual ROI (Year 5 projection) | — | 5× return on investment | 5× |
The QPH platform is built on open-source quantum frameworks and standard medical imaging toolkits — deployable on any major quantum cloud provider without vendor lock-in.
QPH algorithms are built on published research and validated through open scientific challenges. Active clinical partnerships are underway at leading pediatric cardiology centres.
QPH's core fluid dynamics solver was awarded first place in the NIH Quantum Computing Challenge for biomedical simulation, validated against classical solvers across ten test geometries spanning coronary, cerebral, and pediatric cardiac vasculature.
View Source →Prospective study demonstrating statistically significant accuracy improvement over conventional CNNs for CHD categorisation from echocardiography. First presentation of quantum entanglement-based feature maps in a clinical cardiology context.
View Source →The Carleman-LBM encoding technique underlying QPH's quantum fluid solver was independently validated by PsiQuantum and Airbus for aerospace CFD applications, confirming the theoretical speedup and implementation pathway at industrial scale.
View Source →Published analysis demonstrating quantum neural network advantages for thermodynamic entropy optimization problems directly applicable to hemodynamic simulation — providing the theoretical basis for QPH's thrombosis prediction capability.
View Source →VQLS-style parameterized ansatz used in the hemodynamic linear solver pipeline. Built with Qiskit.
from qiskit import QuantumCircuit
from qiskit.circuit import Parameter
def vqls_ansatz(n_qubits: int, depth: int = 2):
"""Parameterized ansatz for VQLS hemodynamic linear solver."""
qc = QuantumCircuit(n_qubits)
params = [Parameter(f"θ_{i}") for i in range(depth * n_qubits * 2)]
idx = 0
for _ in range(depth):
for i in range(n_qubits):
qc.ry(params[idx], i)
idx += 1
for i in range(n_qubits - 1):
qc.cx(i, i + 1)
for i in range(n_qubits):
qc.rz(params[idx], i)
idx += 1
return qc
# 2-qubit example for Carleman-LBM encoding
qc = vqls_ansatz(2, depth=1)
qc.measure_all()
print(qc.draw(fold=-1))The Æ | Cardio platform is available as a SaaS solution or on-premise deployment for qualified research institutions and hospitals. Early access partnerships are now open.