tutorialrecommenderhybrid

Tutorial: Building a Quantum-Enhanced Restaurant Recommender (Dining App Redux)

qqubit365

2026-02-03

9 min read

Recreate a micro dining app and add a compact quantum recommender: dataset prep, PennyLane code, hybrid inference, and Claude re-ranking.

Hook: Stop wondering if quantum helps — build a tiny quantum recommender and see it in action

Decision fatigue is real: whether it's a group chat or a micro dining app (think Where2Eat) you built over a weekend, recommending the right restaurant for the right people is a hard data problem. As a developer or IT pro in 2026, you want practical, portable examples that fit into existing stacks. This tutorial recreates a micro dining app and augments it with a compact quantum recommender. You'll get dataset preparation, code samples (classical + quantum), and a clear hybrid inference flow that uses an LLM (Claude) for natural-language re-ranking and explanations.

Executive summary (what you'll build)

By the end of this tutorial you will have:

A reproducible dataset pipeline for restaurants and user preferences.
A lightweight classical baseline recommender using embeddings and logistic regression.
A compact quantum recommender module using a quantum similarity kernel (PennyLane) and a small variational circuit to produce a personalization score.
A hybrid inference flow: embedding → quantum score → classical fusion → Claude re-ranker + natural-language response.

Why this matters in 2026

Quantum SDKs and cloud access matured through 2024–2026: hybrid algorithms (quantum kernels, small variational circuits) are now practical for micro-app experimentation. In late 2025 and early 2026 we saw better error mitigation, mid-circuit measurement support in cloud QPUs, and tighter SDK integrations (PennyLane, Qiskit, Braket) with classical stacks. This lets developers prototype quantum-enhanced features that act as differentiators in personalization pipelines without needing a full quantum advantage claim.

Architecture overview

The micro-app architecture is intentionally small and deployable on a single VM or serverless platform:

User preferences captured by the app (explicit likes, recent choices, group tags).
Classical embedding service (sentence-transformers / lightweight MLP) converts preferences and restaurant descriptions to vectors. If you plan edge deployments or tiny inference targets, review guides for deploying generative models on constrained hardware like the Raspberry Pi 5.
Quantum recommender module computes a quantum similarity score between user and restaurant embeddings (simulator in dev, cloud QPU in prod experimentation).
Classical fusion model combines the quantum score with deterministic features (rating, distance) to produce top-K candidates.
LLM (Claude) re-ranks the top-K and generates human-friendly recommendations and explanations.

Dataset: preparation and schema

Start simple. Create a small CSV representing restaurants and a CSV of simulated user interactions. The goal is reproducibility and clarity.

# restaurants.csv
# id,name,cuisine,price_level,rating,location,tags,description
1,LaPalma,Italian,2,4.5,"40.7128,-74.0060","cozy,woodfired","Wood-fired pizzas and rustic pasta"
2,SushiOh,Japanese,3,4.7,"40.7139,-74.0070","counter,seatings","Seasonal sushi and omakase"
3,TacoCasa,Mexican,1,4.2,"40.7100,-74.0050","casual,late-night","Street-style tacos and margaritas"

For users, track explicit likes or short text preferences (helps LLM and embeddings).

# users.csv
# user_id,name,profile_text,liked_restaurant_ids
u1,Alice,"I like cozy Italian and quiet places",1
u2,Bob,"Loves late-night tacos and spicy food",3

Transform restaurant fields into a combined text blob for embedding:

def make_text_blob(row):
    return f"{row['name']}. {row['cuisine']} cuisine. {row['description']}. Tags: {row['tags']}"

Compute embeddings (classical step)

Use a compact sentence-transformers model to produce 128–384D embeddings for both user profile_text and the restaurant blob. These embeddings are the inputs to the quantum module (we'll down-project to the quantum circuit size). If you plan to move embeddings or models to edge or microservices, pair this with a micro-frontends / edge pattern to keep latency low (Micro‑Frontends at the Edge).

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')  # small, fast
rest_texts = [make_text_blob(r) for r in restaurants]
rest_embs = model.encode(rest_texts)
user_emb = model.encode([user_profile_text])[0]

Tip: normalize embeddings to unit length before quantum angle encoding.

Classical baseline

Before adding quantum components, implement a baseline. A simple cosine-similarity ranking works well and provides a performance baseline.

from sklearn.metrics.pairwise import cosine_similarity
sims = cosine_similarity([user_emb], rest_embs)[0]
topk_idx = sims.argsort()[::-1][:5]

Quantum recommender module: concept

We use a quantum similarity kernel computed as the fidelity between two quantum states prepared from embeddings. Concretely:

Project 128D embeddings to a smaller dimensionality that matches the number of qubits (e.g., 6 qubits → 6 angles).
Use angle encoding: each vector component maps to a rotation gate.
Compute fidelity between user state |ψ_u⟩ and restaurant state |ψ_r⟩ — fidelity ∈ [0,1] acts as a similarity score.

This is a lightweight module that can run on a simulator in development and be tested on cloud QPUs for research. For production readiness, plan for fallback logic and incident handling so the system remains reliable when cloud QPUs are queued or fail (Public-Sector Incident Response Playbook for Major Cloud Provider Outages).

Quantum code (PennyLane)

import pennylane as qml
import numpy as np

n_qubits = 6
dev = qml.device('default.qubit', wires=n_qubits)

# angle encoding circuit
@qml.qnode(dev)
def prepare_state(angles):
    for i in range(n_qubits):
        qml.RY(angles[i], wires=i)
    return qml.state()

# fidelity-based similarity
def quantum_similarity(user_angles, item_angles):
    psi_u = prepare_state(user_angles)
    psi_r = prepare_state(item_angles)
    # fidelity between pure states = ||^2
    fid = np.abs(np.vdot(psi_u, psi_r))**2
    return float(fid)

# Example usage
user_angles = project_to_angles(user_emb, n_qubits)
item_angles = project_to_angles(rest_embs[0], n_qubits)
score = quantum_similarity(user_angles, item_angles)
print('Quantum similarity:', score)

Notes:

project_to_angles is a helper that reduces embedding dimensionality and maps values to rotation ranges, e.g., via PCA + scaling to [0, pi].
On real hardware, use a device like 'braket.aws.qubit' or 'ionq.aws' with the same qnode code, but beware queueing and noise.

Optional: parameterized variational module

If you want trainable quantum parameters, append a small hardware-efficient ansatz after angle encoding and optimize for a ranking loss. For micro-apps, starting with unparameterized fidelity is simpler and stable.

Integrating quantum scores into a hybrid fusion model

Quantum similarity is one input. Combine it with classical features to produce the final ranking score. A simple logistic regression or small MLP is practical:

import numpy as np
from sklearn.linear_model import LogisticRegression

# Construct features: [quantum_score, cosine_sim, rating, distance_norm]
X = []
y = []  # click / prefer labels (if you have them)
for i, emb in enumerate(rest_embs):
    q = quantum_similarity(user_angles, project_to_angles(emb, n_qubits))
    c = cosine_similarity([user_emb], [emb])[0][0]
    rating = restaurants[i]['rating']
    distance_norm = compute_distance_norm(user_loc, restaurants[i]['location'])
    X.append([q, c, rating, distance_norm])
    y.append(labels[i])

clf = LogisticRegression().fit(X, y)

# On inference:
X_test = [[q, c, rating, distance]]
p = clf.predict_proba(X_test)[0,1]

This hybrid approach lets you measure the incremental contribution of the quantum feature. Use ablation studies to quantify value.

Hybrid inference flow (step-by-step)

User issues a query or selects preferences in your micro-app.
Compute user embedding (sentence-transformer).
For each candidate restaurant, compute classical similarity (cosine) and the quantum similarity via the quantum module.
Fuse features in the classical fusion model to produce a top-K list.
Call Claude with the top-K and user context to re-rank and generate an explanation (e.g., "We suggest LaPalma because you said you like cozy Italian..."). For examples of automating prompts and orchestrating LLM calls, see Automating Cloud Workflows with Prompt Chains.
Return the ranked list and explanation to the user; log feedback for continuous training.

Claude integration (example)

Use Claude to improve UX: convert numerical scores into friendly sentences and re-rank with soft preferences. Here's a simple HTTP pseudocode to call Claude's API (replace with your integration code and keys):

import requests

claude_api = 'https://api.anthropic.com/v1/complete'  # pseudocode endpoint
headers = {'Authorization': f'Bearer {CLAUDE_API_KEY}'}

prompt = f"User: {user_profile_text}\nTop options:\n"
for r, score in top_candidates:
    prompt += f"- {r['name']} ({r['cuisine']}) score={score:.2f}\n"

prompt += "\nPlease rank these from best to worst for the user and provide a brief explanation."

resp = requests.post(claude_api, headers=headers, json={'prompt': prompt, 'max_tokens': 200})
print(resp.json())

Claude excels at understanding subtle context (group preferences, vibe cues) and producing concise explanations. Use it to surface why a restaurant was chosen — this increases trust and transparency. If you want a ready micro-app scaffold that wires Claude into your flow, check the starter kit: Ship a micro-app in a week: a starter kit using Claude/ChatGPT.

Evaluation and metrics

Measure the quantum module's value using:

Delta NDCG when the quantum feature is added to the fusion model vs. baseline.
A/B tests of user engagement or chosen restaurant rates between the classical-only and hybrid system. Use the micro-app starter to run quick A/B tests and iterate.
Calibration of the quantum score: ensure the quantum similarity correlates sensibly with human preferences.

Because QPUs are noisy, run multiple seeds and calibrate using error mitigation strategies (readout error correction, zero-noise extrapolation) when moving beyond simulators. Also plan for operational concerns: storage cost optimization for cached quantum scores and dataset snapshots, and design fallbacks for when hardware is unavailable.

Deployment considerations and SDK integrations

Best practices for integrating the quantum module into your micro-app:

Start with a simulator for development (PennyLane default.qubit or Qiskit's Aer).
Use containerized inference for the classical parts and a separate microservice for quantum calls to isolate latency and retries — this is aligned with composable micro-app ideas in From CRM to Micro‑Apps.
For cloud QPUs, batch quantum similarity computations where possible and cache results (restaurant vectors change slowly).
Monitor quota, latency, and failed runs. Fall back to classical similarity when the QPU is unavailable; operational runbooks and outage playbooks are helpful (Public-Sector Incident Response Playbook).

Popular SDKs in 2026: PennyLane (for hybrid pipelines), Qiskit (IBM hardware), Amazon Braket integrations, and vendor-specific SDKs. PennyLane's device-agnostic qnodes make switching between simulator and cloud device straightforward.

Practical tips and gotchas

Keep the quantum circuit shallow (1–3 layers) to reduce noise impact.
Down-project embeddings carefully — PCA works well; keep interpretability in mind.
Cache quantum scores for static restaurant data to reduce QPU usage and latency; caching and storage strategy matters for cost and speed (storage cost optimization).
If you don't have click labels, run small user studies or synthetic preference sampling to train the fusion model.
Quantify the cost of QPU calls and add a fallback path in production. Consider edge registries and reliable artifact storage for curated vectors (cloud filing & edge registries).

2026 trends and what to expect next

As of 2026, hybrid approaches are the most realistic path for application-level quantum integration. Expect these trends:

More SDK-first integrations between LLMs, embeddings, and quantum libraries — making hybrid pipelines easier.
Better error mitigation and mid-circuit measurement support that allow slightly deeper circuits for small gains in similarity tasks.
Growing standardized benchmarks for quantum-enhanced ML components to measure real-world impact beyond toy problems.

Case study: quick experiment

Run this micro-experiment to measure whether the quantum feature helps for a small user set:

Collect 200 user preference samples and their chosen restaurants (can be synthetic).
Train baseline fusion model (classical features only).
Augment with the quantum similarity feature computed on a simulator and retrain.
Compare NDCG@5 and AUC. Run statistical tests to validate improvements.

Actionable takeaways

Prototype quickly: start with a simulator and cached quantum scores for your static catalog. If you need a fast scaffold, see the micro-app starter kit (Ship a micro-app in a week).
Measure incrementally: ablate the quantum score to quantify impact before promoting to production.
Fuse, don't replace: treat quantum outputs as additional signals in a classical fusion model.
Use LLMs for UX: Claude can translate scores into trust-building explanations and re-rank using soft human preferences; automation of prompt chains improves reliability (prompt chains).

Practical quantum components are not a silver bullet — they are concise, experimental features that can add personalized signal when integrated carefully into classical pipelines.

Next steps and call-to-action

Ready to try this end-to-end? Fork a starter repo with the dataset, embedding pipeline, PennyLane quantum module, and Claude re-ranker. Run experiments locally with the simulator, then pilot the quantum module on a cloud QPU for research. If you want a jump-start, grab the micro-app scaffold, run the provided notebooks, and run the A/B test described above.

Share your results in the qubit365 community or open an issue in the repo — we’re collecting micro-app experiments to build a public benchmark of quantum-enhanced recommenders. Push the boundary: add a trainable ansatz, experiment with quantum kernels, and report whether the hybrid model improves real user engagement.

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