qlif-cast-quantum-spiking-forecasting

name: qlif-cast-quantum-spiking-forecasting description: Quantum Leaky-Integrate-and-Fire (QLIF-CAST) methodology for time-series forecasting. Adapts QLIF spiking neural networks for multivariate regression, achieving 15.4% lower MSE than classical LIF and 94% faster convergence than QLSTM/QNN. Activated by: quantum spiking forecasting, QLIF, time-series quantum, quantum regression.

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

Description

QLIF-CAST methodology adapts the Quantum Leaky Integrate-and-Fire (QLIF) spiking neural network for time-series regression tasks, specifically multivariate weather and environmental forecasting. It encodes neuron excitation states as single-qubit quantum superpositions driven by Rx rotation gates and T1 relaxation decay, within a hybrid quantum-classical recurrent architecture.

Key results:

• 15.4% lower MSE vs parameter-matched classical LIF baseline
• 4.4% lower MAE vs classical LIF
• 94% less training time vs QLSTM and QNN on air quality/wind speed benchmarks
• 1.2% average deviation from simulation on IBM Marrakesh 156-qubit QPU
• Occupies distinct position in speed-error trade-off space

Activation Keywords

• quantum spiking forecasting
• QLIF-CAST
• quantum leaky integrate and fire
• quantum time-series regression
• quantum weather forecasting
• quantum spiking neural network forecasting
• 量子脉冲预测
• QLIF

Core Architecture

QLIF Neuron Model

• State encoding: Single-qubit quantum superposition $|\psi\rangle = \cos(\theta/2)|0\rangle + e^{i\phi}\sin(\theta/2)|1\rangle$
• Excitation dynamics: Rx rotation gates drive state evolution based on input
• Leak mechanism: T1 relaxation decay provides natural forgetting
• Firing threshold: Measurement probability determines spike emission
• Recurrent connectivity: Hybrid quantum-classical feedback loop

Hybrid Architecture

Input Time-Series → Quantum Encoding → QLIF Layer → Classical Readout → Output
                        ↑                                          |
                        └──────────── Recurrent Feedback ─────────┘

Usage Patterns

Pattern 1: Weather/Environmental Forecasting

Use QLIF-CAST for multivariate time-series forecasting where:

• Data has temporal dependencies and multiple correlated features
• Classical LIF/RNN models show convergence bottlenecks
• Quantum speedup in training is valuable
• Applications: weather, air quality, wind speed, climate

Pattern 2: Resource-Constrained Training

Use QLIF-CAST when:

• Training time is a critical constraint
• Need favorable speed-accuracy trade-off
• Classical LSTM/GRU models are too slow for the dataset size

Pattern 3: NISQ-Era Deployment

Use QLIF-CAST for:

• Hybrid quantum-classical pipeline on current hardware
• Shallow quantum circuits with classical pre/post-processing
• Hardware verification confirms <2% simulation-to-hardware gap

Instructions for Agents

Step 1: Problem Assessment

Determine if QLIF-CAST is appropriate:

• Is it time-series regression? QLIF-CAST extends QLIF beyond classification to continuous prediction
• Is data multivariate? The model handles multiple correlated input features
• Is speed important? QLIF-CAST shows significant training speed advantages

Step 2: Data Encoding

• Encode time-series features as rotation angles for Rx gates
• Use amplitude encoding for normalized input values
• Map temporal sequences to sequential quantum circuit applications

Step 3: Architecture Design

# Conceptual architecture
class QLIF_CAST:
    def __init__(self, n_qubits, n_classical_features):
        # QLIF neurons as qubits
        self.quantum_neurons = n_qubits
        # Rx rotation for input excitation
        self.rotation_gate = 'Rx'
        # T1 relaxation for leak
        self.t1_decay = parameter
        # Hybrid readout
        self.classical_readout = Linear(n_qubits, output_dim)
    
    def forward(self, x_t, h_prev):
        # Quantum state update
        psi = Rx(x_t) @ T1_decay(h_prev)
        # Measurement → classical
        spike = measure(psi)
        # Classical readout
        output = self.classical_readout(spike)
        return output, psi  # For recurrence

Step 4: Training Protocol

• Use hybrid quantum-classical gradient descent
• Quantum circuit evaluation for forward pass
• Classical backpropagation for readout layer
• Parameter-shift rule for quantum gate gradients

Step 5: Hardware Deployment

• Verify on simulator first
• Deploy on IBM QPU or similar NISQ device
• Expect ~1.2% deviation from simulation (as reported)
• Use error mitigation for noise resilience

Error Handling

Barren Plateau Problem

• QLIF-CAST's shallow circuit depth mitigates this vs deep QNNs
• Use layer-wise training if needed

Noise Sensitivity

• T1 relaxation is physically motivated but can accumulate errors
• Apply measurement error mitigation on hardware
• Use the 1.2% hardware deviation as tolerance bound

Classical Baseline Comparison

• Always compare against parameter-matched classical LIF
• Use MSE and MAE as primary metrics
• Track training time as secondary advantage

Related Skills

• spiking-neural-network-analysis - SNN analysis methodology
• quantum-neural-architecture - QNN design patterns
• hybrid-quantum-classical-systems - Hybrid system engineering
• qlif-quantized-burst-neurons-v2 - Related QLIF neuron models

Reference

• Paper: "QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting"
• Authors: Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan
• arXiv: 2605.18333
• Published: 2026-05-18