In 2026 we’ve finally crossed the line where quantum-classical hybrids deliver results worth trading on. I need that edge pointed squarely at high-frequency algorithmic trading. The goal is a production-ready strategy that delegates the intensive optimisation loop to a quantum co-processor while letting a classical AI layer handle real-time signal evaluation and risk controls. Here’s what I have in mind: the quantum side tackles portfolio state-space exploration—think QAOA, VQE or amplitude-estimation—while TensorFlow / PyTorch models learn micro-structure patterns from live tick data and route only the most promising parameter sets back to the gate model. Latencies must stay sub-millisecond from signal to order, so a coherent design for GPU–FPGA–QPU orchestration is essential. Deliverables • A documented architecture diagram showing data flow between classical AI, middleware, and the chosen quantum SDK (Qiskit, Braket or similar). • Clean, modular Python code with C++/CUDA kernels where latency demands it, fully containerised for reproducibility. • Back-test and forward-test reports on at least one major FX pair and a US equity futures contract, including Sharpe, max drawdown, and execution slippage statistics. • Deployment guide for a colocation environment, covering queue management to the quantum back-end and fall-back logic when the QPU is offline. Acceptance criteria: the strategy must sustain sub-5 µs internal decision latency and demonstrate a minimum 15 % improvement in risk-adjusted return over a classical-only baseline across three months of tick data. If you’ve already experimented with quantum optimisation for finance and can speak in both qubits and FIX tags, let’s make this the first live Quantum-AI HFT desk on the street.