Interference: When Learning Signals Conflict
How quantum interference handles conflicting gradients in reinforcement learning
Understanding Quantum Interference
In quantum mechanics, waves can interfere with each other. When two waves meet:
- Constructive interference: Waves in phase amplify each other (peaks align with peaks)
- Destructive interference: Waves out of phase cancel each other (peaks align with troughs)
The famous double-slit experiment demonstrates this: particles create an interference pattern on a screen, with bright bands (constructive) and dark bands (destructive). This is a purely quantum phenomenon—classical particles would just create two bright spots.
The Problem in Reinforcement Learning
In RL, we often receive conflicting learning signals. Consider a gridworld where:
- In region A, action "go right" leads to high rewards
- In region B, the same action "go right" leads to penalties
Classical RL averages these signals: if region A appears 60% of the time and region B appears 40%, the agent learns to go right with some probability. But this misses the structure: the action is good in A and bad in B. Averaging destroys this information.
Example: Multi-Armed Bandit with Context - A bandit where pulling arm 1 gives reward +10 in context A but -10 in context B. Classical algorithms average to 0, missing that the arm should be pulled in A and avoided in B. Quantum interference preserves this phase information.
Where Standard Learning Fails
Averaging conflicting gradients
The Problematic Approach
In a standard RL setup, all experiences are thrown into a single replay buffer. Gradients from “self-service great” segments and “self-service terrible” segments are mixed together. Over time, the agent converges to an average behaviour that is mediocre for everyone.
Critical Issues:
Our Interference-Based QiRL Design
Constructive and destructive combination of experts
The Triangulo Solution
We decompose the policy into multiple expert components – for example, a “self-service-first” expert and an “escalate-early” expert. Each expert specialises on the segments where it performs well.
Self-service / Escalation
Decomposition
Policy is expressed as a sum of expert contributions.
Constructive Interference
Experts reinforce each other in contexts where they agree with reward.
Destructive Interference
Misaligned experts cancel out in contexts where they perform poorly.
The result is a single QiRL agent that can behave very differently across segments, without baking hard segment rules into the product. The structure emerges from data.
Production View: Interfering Experts
How we combine and train expert components
Expert Mixture Implementation
Below is a simplified pseudocode sketch of an interfering expert policy: context determines mixing weights, and learning adjusts both expert parameters and their phase-like contributions.
class InterferingPolicy:
def __init__(self, experts, gating_network):
self.experts = experts # list of expert policies
self.gating = gating_network # outputs context-dependent weights
def act(self, state):
context = extract_context(state)
weights = self.gating(context) # can be positive or negative
logits = 0
for w, expert in zip(weights, self.experts):
logits += w * expert.action_logits(state)
dist = Categorical(logits=logits)
return dist.sample()def update_interfering_policy(policy, batch):
"""
Encourage experts that predict high-return behaviour in a segment
and suppress those that systematically underperform there.
"""
returns = estimate_returns(batch)
segments = batch["segment_id"]
loss = 0
for segment in unique(segments):
seg_batch = batch[segments == segment]
seg_return = returns[segments == segment].mean()
# experts that align with seg_return get constructive updates,
# misaligned experts get destructive updates
loss += segment_specific_loss(policy, seg_batch, seg_return)
loss.backward()
optimizer.step()
optimizer.zero_grad()Key Features:
- Context-based gating: different segments see different expert mixtures.
- Structured cancellation: bad experts are actively suppressed, not just ignored.
- Interpretability: we can inspect which experts dominate in which contexts.
Summary: Quantum Interference in RL
How phase relationships enable nuanced policy updates
Key Insights
Quantum interference allows RL algorithms to handle conflicting learning signals more intelligently than simple averaging. By preserving phase relationships between signals, the agent can learn that the same action is good in some contexts and bad in others, rather than converging to a mediocre average.
Phase Preservation
Interference preserves the phase (context) of learning signals, allowing constructive reinforcement in good contexts and destructive cancellation in bad ones.
Context-Aware Learning
Instead of averaging conflicting gradients, interference enables context-dependent policies that adapt to different regions of state space.
Phase-Aware Updates
Policy updates respect signal phase relationships, enabling nuanced learning that distinguishes between conflicting contexts rather than averaging them away.
Intuitive Framework
The interference framework provides an intuitive way to understand how conflicting learning signals can reinforce or cancel each other, making the learning process more interpretable.
🔮 Quantum Saga Secrets
Secret 1: Embrace Heterogeneity
👁️ Click to revealTrying to collapse all segments into one behaviour loses information; interference gives a language for keeping differences alive.
Secret 2: Debugging via Experts
👁️ Click to revealWhen something goes wrong in production, you can inspect which expert dominates and adjust or retrain it, instead of rewriting the whole agent.
Continue the Quantum Saga
Interference is the third story in the quantum saga. Next is tunnelling – how QiRL agents escape local optima by jumping through apparent metric barriers.