Built as an Authentic Quantum App • Quantum-native by Design
Qubitrade:
Autonomous Investing,
Engineered for Quantum Reality.
Qubitrade is a full-stack autonomous portfolio rebalancing app: Django + Celery + React in production, with a quantum optimization engine today and active pilot modules for IBM Quantum execution, QML, QRL, and QGAN.
No performance promises. Today’s engine runs on hybrid hardware with quantum methods and Qiskit-based prototypes.
From Brokerage Login to Quantum-Ready Autonomy
The app runs live today with Alpaca brokerage integration and autonomous rebalancing loops.
Connect Your Brokerage
Securely link your Alpaca account via API keys. Funds stay in your own brokerage; Qubitrade only sends trade instructions.
Define the Universe
Choose what the engine can touch: stocks, ETFs, or custom baskets. Configure risk settings that become constraints in the optimizer.
Enable Autonomous Mode
Turn on the Authentic Quantum App loop: scheduled Celery tasks fetch data, score assets, optimize a portfolio and send orders automatically.
Authentic Quantum App Architecture
One cohesive stack: classical where it’s mature, quantum-ready where it matters. Every critical decision is expressed as math (QUBO, kernels, policies) that can be handed to real QPUs.
1. Prediction & Signals Layer
A production ensemble of classical Support Vector Regressors (“QSVR”) predicts short-term returns using engineered features (technical indicators, wavelets, PCA). This layer is already wired to swap in quantum kernels and variational circuits as they mature.
Today: CPU/GPU execution with quantum feature engineering.
Live Pilot: Qiskit-based Quantum Kernels (QuantumKernelPilot) and QGAN
stress scenarios.
2. RL Orchestrator Layer
A PPO-based reinforcement learning agent decides when to rebalance, when to stay in cash, and how aggressively to deploy capital. It consumes live portfolio state, volatility regimes, and quantum correlation metrics.
Today: Hybrid PPO with active Quantum Policy Gradients (QPG) and QRNG exploration in
pilot mode.
Roadmap: Full quantum state encoding on distributed QPUs.
3. Quantum Optimization & Connector Layer
Portfolio rebalancing is formulated as a QUBO and solved with a QAOA engine. A dedicated Quantum Connector app isolates Qiskit integration, backend selection, batching, and fallbacks between simulators and IBM Quantum hardware.
Today: Hybrid Solver (Simulator + IBM Quantum Ready) with active
multi-tenant batching and cost controls.
Roadmap: Scaling to 100+ qubit backends.
From GitHub to Live Trades: Production Stack
The current implementation is not a slideware demo; it is a running app. Django, Celery and React/Vite orchestrate real brokerage accounts, with quantum codepaths already present in the repository.
Live Backend & Frontend
- Backend: Django + DRF with a modular
apps layout
(
broker,rl_agent,quantum_connector). - Async Engine: Celery workers with queues for ETL, ML training, RL updates, and quantum optimization tasks.
- Frontend: React/Vite dashboard that surfaces engine status, portfolio state, and autonomous decisions.
- Brokerage: Alpaca API for market data & order routing, with safeguards for market-open checks and pattern-day-trading constraints.
Quantum Connector & Batching
The quantum_connector app encapsulates all calls into
Qiskit and IBM
services. It is built around batching and cost-awareness rather than one-job-per-user.
- QUBO problems derived from portfolio constraints and risk budgets.
- Batching multiple user optimizations into a single quantum session where feasible.
- Automatic fallback to simulators if hardware queues or budgets are exceeded.
Where Qubitrade Sits in the Landscape
Most “AI traders” are fully classical. Qubitrade is architected as an Authentic Quantum App from day one.
| Feature | DIY Trading | Standard Robo-Advisors | Qubitrade Authentic Quantum App |
|---|---|---|---|
| Decision Making | Manual, emotional, time-consuming | Static rules, periodic rebalancing | Continuous ML + RL, fully automated loop |
| Strategy Model | Ad-hoc, often untested | Classical MPT & risk bands | QUBO-based optimization with quantum-ready formulation |
| Risk Handling | Manual stops, inconsistent behavior | Fixed risk profiles, slow to adapt | RL-led regime detection, strategic inaction in turbulence |
| Quantum Path | None | None | Dedicated Quantum Connector, QAOA/QML/QRL/QGAN roadmap |
| Transparency | Personal notes (if any) | High-level charts only | Planned per-trade reasons, engine status & quantum vs classical logs |
Quantum Layer: Designed for Real Qubits
Today’s engine runs on simulators and hybrid hardware, but every component is expressed in a way that can be delegated to real QPUs as reliability and access improve.
QUBO as the Core Language
Portfolio selection is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This framing is compatible with QAOA, quantum annealers, and future algorithms.
qp = build_quadratic_program(expected_returns, cov_matrix, budget)
qubo = QuadraticProgramToQubo().convert(qp)
QAOA Solver
A QAOA-style algorithm searches the QUBO landscape for good portfolios. In the live app this runs on high-fidelity simulators with strict timeouts and classical fallbacks.
qaoa = QAOA(sampler, optimizer, reps=2)
result = MinimumEigenOptimizer(qaoa).solve(qubo)
Roadmap: migrate this path to IBM Quantum via Qiskit Runtime, with batching & error mitigation.
Quantum-Ready ML & QGAN
The QSVR ensemble is structured so its kernel and feature map can be replaced with quantum versions (e.g. ZZFeatureMap). A dedicated QGAN module is active for forward-looking scenario generation and tail-risk stress testing.
feature_map = ZZFeatureMap(num_features, reps=2)
# QGAN & quantum kernels plugged here as they mature
Quantum RL & QRNG Integration
The RL environment already includes quantum features and is wired to accept true quantum signals: active QRNG-based exploration noise, small quantum policy layers, and regime encodings.
epsilon = qrng_sample() # replace classical RNG
if epsilon < threshold:
take_exploratory_action()
End-to-End Pipeline of a Rebalance
From raw prices to signed orders. The flow is built so that the optimization step can be transparently switched from classical/simulator to quantum hardware.
1. Data Ingestion & Feature Engineering
Celery ETL tasks continuously pull price/volume data from Alpaca, compute indicators, wavelets and PCA-compressed features. This feeds the QSVR models and RL state.
2. Predictions & RL Decision
The prediction layer scores candidate assets. The RL Orchestrator reviews portfolio state and decides whether it is worth triggering a rebalance or holding cash.
3. batched QUBO Optimization
When a rebalance is triggered, the system builds a QUBO for the selected universe, then passes one or more such problems to a batching task.
batched_requests = build_qubo_batch(users, universes)
quantum_batch_qaoa_task.delay(batched_requests)
Today, this uses a QAOA-style routine on simulators. The same batching API is ready to target IBM Quantum backends.
4. Trade Construction & Execution
The optimizer returns binary decisions and weights. A broker task computes deltas vs the live portfolio and creates a minimal set of orders, enforcing risk and compliance checks before sending them to Alpaca.
Engine Snapshot
Simulated log-style view of one rebalance run
[ML] QSVR ensemble updated mae=0.0062 r2=0.72 device=GPU
[RL] state=defensive regime=high_vol action=REBALANCE_TRIGGERED
[QUBO] budget=4 assets_preselected=10 lambda_risk=0.85
[BATCH] packed 3 QUBOs into batch_id=qb-2025-11-19-01
[QAOA_SIM] reps=2 depth≈70 backend=aer_simulator status=OK
[QAOA_SIM] solutions=["101001", "110001", "011100"] objective_best=-1.284
[BROKER] target_weights computed; rebalancing 7 orders (cash buffer 18%)
[BROKER] order_batch_id=alp-2025-11-19-01 sent to Alpaca
[ENGINE] classical fallback=FALSE (simulated quantum path succeeded)
[ENGINE] quantum_hardware_used=FALSE (simulator path – hardware gated by roadmap)
These logs exist in the codebase and roadmap as structured telemetry, enabling side-by-side evaluation of classical vs quantum-enhanced runs when hardware integration is switched on.
Why Qubitrade Feels Like “The Only App”
There are many AI trading tools. Qubitrade is architected from the ground up as an authentic quantum application: every major decision problem is already cast in a form that real quantum hardware can solve.
Quantum-Authentic by Design
Many products merely borrow quantum buzzwords. Qubitrade starts from the math: QUBOs, kernels, quantum feature maps, QGANs, QRL. The code is written to be executable on IBM Quantum when it’s economically and technically viable – not as a marketing afterthought.
Full Autonomy, Not Just Signals
Qubitrade doesn’t stop at alerts or dashboards. It maintains a continuous loop: sense (data), think (ML + RL + optimization), and act (broker orders) – with guardrails. The vision is a set-and-forget quantum-native allocator, not a tool that needs babysitting.
Owner-Grade Observability
The blueprint mandates exhaustive logging: engine state, QPU backend choices, queue times, cost, and classical baselines. That makes quantum advantage measurable, falsifiable and auditable – exactly what serious capital needs to trust an autonomous system.
Qubitrade does not claim guaranteed outperformance. The edge comes from a unique technical posture: a production app that is already structurally ready for genuine quantum acceleration as hardware, algorithms and access mature.
Talk to Qubitrade About the Quantum Roadmap
If you are an investor, partner, or early adopter interested in quantum-authentic finance, leave your details. We can show you live app, and explain the architecture.