qlif-cast-weather-forecasting

name: qlif-cast-weather-forecasting description: "QLIF-CAST: Quantum Leaky-Integrate-and-Fire methodology for time-series regression (weather forecasting). Hybrid quantum-classical recurrent architecture using single-qubit superpositions for neuron states. Demonstrates 15.4% MSE reduction over classical LIF and 94% faster convergence vs QLSTM/QNN." tags: ["quantum-spiking", "time-series-forecasting", "quantum-machine-learning", "weather-forecasting", "hybrid-quantum-classical"] category: ai_collection

QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Forecasting

arXiv: 2605.18333 (May 18, 2026) Authors: Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan, Muhammad Kashif, Muhammad Shafique Category: Quantum Physics (quant-ph); Machine Learning (cs.LG)

Overview

QLIF-CAST extends the Quantum Leaky-Integrate-and-Fire (QLIF) spiking neural network from classification to time-series regression, specifically short-term multivariate weather forecasting. It encodes neuron excitation states as single-qubit quantum superpositions within a hybrid quantum-classical recurrent architecture.

Core Innovation

Quantum Neuron Model

Each QLIF neuron encodes its excitation state as a single-qubit state:

|ψ⟩ = cos(θ/2)|0⟩ + sin(θ/2)|1⟩

• Rx rotation gates: Drive neuron excitation based on input current
• T1 relaxation decay: Models natural decay (leaky behavior)
• Threshold firing: Measurement-based spike generation

Hybrid Architecture

Input → Classical Feature Encoder → QLIF Recurrent Layer → Classical Decoder → Output
         (multivariate features)     (quantum neuron states)  (readout layer)

The QLIF layer sits within a recurrent structure, processing temporal sequences with quantum-enhanced dynamics.

Key Results

Evaluation 1: Weather Forecasting (vs Classical LIF)

Demonstrates quantum neuronal dynamics reduce prediction error over classical equivalents with matched parameters.

Evaluation 2: Cross-Domain Comparison (vs QLSTM, QNN)

Hardware Verification

• Executed on IBM Marrakesh (156-qubit QPU)
• Only 1.2% average deviation from simulation
• Confirms reliable circuit execution on real quantum hardware

Methodology

QLIF Neuron Dynamics

1. Input Encoding: Multivariate features → rotation angles for Rx gates
2. Excitation Update: |ψ(t)⟩ = Rx(input)|ψ(t-1)⟩
3. T1 Decay: Natural relaxation toward ground state
4. Spike Detection: Measure qubit; spike if |1⟩ probability exceeds threshold
5. State Reset: Post-spike reset to ground state

Training

• Hybrid quantum-classical optimization
• Classical parameters: encoder/decoder weights
• Quantum parameters: rotation angles, thresholds
• Loss: MSE for regression tasks

Applications

• Weather forecasting: Short-term multivariate prediction
• Air quality prediction: Cross-domain validation
• Wind speed forecasting: Time-series regression
• General time-series: Any continuous-valued prediction task

Implementation Considerations

# Conceptual QLIF-CAST architecture
class QLIF_CAST:
    def __init__(self, n_qubits, n_features):
        self.encoder = ClassicalEncoder(n_features, n_qubits)
        self.qlif = QLIFRecurrentLayer(n_qubits)  # Quantum circuit
        self.decoder = ClassicalDecoder(n_qubits)
    
    def forward(self, x):
        encoded = self.encoder(x)
        quantum_states = self.qlif(encoded)
        return self.decoder(quantum_states)

Hardware Requirements

• Quantum backend: IBM Q (156+ qubits recommended)
• Classical co-processor for encoding/decoding
• Hybrid optimization framework (PennyLane, Qiskit)

Comparison with Related Approaches

Limitations

• NISQ-era constraints: Limited qubit count and coherence time
• Simulation vs Hardware: 1.2% deviation may grow with larger circuits
• Domain scope: Primarily validated on environmental forecasting
• Scalability: Quantum advantage may diminish with classical hardware improvements

Activation Keywords

• qlif-cast
• quantum leaky integrate fire
• quantum spiking neural network
• quantum time series forecasting
• hybrid quantum classical recurrent
• single qubit neuron
• quantum weather forecasting
• T1 relaxation neuron
• quantum regression
• Rx rotation neuron

Citation

@article{marchisio2026qlifcast,
  title={QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting},
  author={Marchisio, Alberto and Ebrahim, Aayan and Innan, Nouhaila and Kashif, Muhammad and Shafique, Muhammad},
  journal={arXiv preprint arXiv:2605.18333},
  year={2026}
}