One-line: Love-QPU replaces “fight-the-noise” (X-axis) control with engineered synchronization (Y-axis). It measures and steers collective phase $\hat{\rho}^{(0)}$ across bosonic or topological primitives, using passive protection and autonomous quantum error correction (AQEC) to reduce active control overhead and decoherence back-action.
The Status Quo: Today’s leading QPUs achieve superb single-device physics but rely on heavy, active error correction (syndrome measurement → feedforward) that injects control noise and demands massive cryo-wiring and electronics. This is the X-axis approach: fighting noise with brute force and high resistance ($R$).
Recent progress shows we can shift protection into the hardware itself:
The Paradigm Shift: The field is pivoting to Y-axis shaping (phase/topology)—exactly the Love-OS stance of $R \to 0$ (minimize resistive intervention) and phase synchronization.
PSF-Zero measures zero-lag phase synchronization across many nodes:
\[\hat{\rho}^{(0)}(t) = \frac{2}{K(K-1)} \sum_{i < j} \langle \cos(\phi_i(\tau) - \phi_j(\tau)) \rangle\]In Love-QPU, nodes are oscillator modes/cavities or protected degrees of freedom. Target behavior:
To transition from emulation to physical hardware, the following checklist defines the requirements for a baseline 4-mode Bosonic (Cat) tile.
This configuration defines the thresholds for the autonomous scheduler.
love_qpu:
targets:
rho_in_manifold: ">= 0.85"
rho_cross_coupling: "<= 0.10"
alerts:
ewma_lambda: 0.2
cusum:
kappa: 0.001
eta: 0.5
interventions:
duty_max: 0.01
random_phase_kick:
amplitude_rad: [0.01, 0.05]
isi_ms: [200, 800]
jitter_ms: [5, 10]
This module ingests phase streams from the 4-mode tile, calculates $\hat{\rho}^{(0)}$, tracks drift using EWMA/CUSUM, and triggers low-resistance ($R \to 0$) interventions only when necessary.
import numpy as np
class LoveQPUScheduler:
def __init__(self, num_modes=4, config=None):
self.num_modes = num_modes
self.config = config or self.default_config()
# State variables
self.rho_history = []
self.ewma_val = 0.0
self.cusum_pos = 0.0
def default_config(self):
return {
"ewma_lambda": 0.2,
"cusum_kappa": 0.001,
"cusum_eta": 0.5,
"threshold_low": 0.85
}
def calculate_psf_zero(self, phase_array):
"""
Calculates the zero-lag phase synchronization (rho^(0)) across modes.
phase_array: 1D numpy array of phases (in radians) for the K modes.
"""
K = len(phase_array)
if K < 2: return 1.0
cos_sum = 0.0
for i in range(K):
for j in range(i + 1, K):
cos_sum += np.cos(phase_array[i] - phase_array[j])
rho_0 = (2.0 / (K * (K - 1))) * cos_sum
return rho_0
def update_monitors(self, rho_current):
"""Updates EWMA and CUSUM statistics to detect phase drift."""
lam = self.config["ewma_lambda"]
kappa = self.config["cusum_kappa"]
# EWMA Update
if not self.rho_history:
self.ewma_val = rho_current
else:
self.ewma_val = lam * rho_current + (1 - lam) * self.ewma_val
# CUSUM Update (detecting negative drift / loss of synchronization)
# We want to detect if rho drops below our expected high baseline.
baseline = 1.0 # Ideal synchronization
deviation = baseline - rho_current
self.cusum_pos = max(0, self.cusum_pos + deviation - kappa)
self.rho_history.append(rho_current)
return self.ewma_val, self.cusum_pos
def scheduler_step(self, phase_measurements):
"""
Main control loop execution.
SENSE -> DECIDE -> ACT (Minimal intervention)
"""
# 1. Sense: Calculate phase synchronization
rho_current = self.calculate_psf_zero(phase_measurements)
ewma, cusum = self.update_monitors(rho_current)
# 2. Decide: Is intervention needed?
intervention_triggered = False
action_log = "Passive stabilization active."
if cusum > self.config["cusum_eta"] or ewma < self.config["threshold_low"]:
# 3. Act: Trigger minimal, randomized phase-kick (desynchronize cross-coupling)
intervention_triggered = True
action_log = "Drift detected. Scheduling randomized micro-desync phase kick (Duty < 1%)."
# Reset CUSUM after intervention
self.cusum_pos = 0.0
return {
"rho_0": round(rho_current, 4),
"ewma": round(ewma, 4),
"cusum": round(cusum, 4),
"intervention": intervention_triggered,
"log": action_log
}
# --- Emulation / Bench Testing ---
if __name__ == "__main__":
scheduler = LoveQPUScheduler(num_modes=4)
print("Simulating Love-QPU Control Loop...\n")
# Simulate 5 clock cycles
for step in range(1, 6):
# Emulate phase measurements from readout (with slight noise)
# In a perfect state, phases are tightly clustered
if step < 4:
simulated_phases = np.random.normal(loc=0.0, scale=0.1, size=4)
else:
# Inject a phase drift (decoherence/noise) at step 4
simulated_phases = np.array([0.0, 0.1, 1.5, -1.2])
result = scheduler.scheduler_step(simulated_phases)
print(f"Cycle {step}: Phases = {np.round(simulated_phases, 2)}")
print(f" -> rho^(0): {result['rho_0']}")
print(f" -> CUSUM : {result['cusum']}")
print(f" -> Action : {result['log']}\n")