qufin

Quantum-Enhanced Quantitative Finance

Quantum finance is full of claims and short on honest benchmarks, which leaves unclear where quantum methods genuinely help. qufin is the framework built to settle that: every quantum algorithm ships beside the best classical solver, so the two are compared on the same problem — across 159 modules, 14 subpackages, and 11 backends.

Challenge

Quantum finance methods are hard to evaluate honestly. They are usually reported in isolation, without a strong classical baseline run on the same problem — so it stays unclear where quantum approaches genuinely help and where a classical solver remains the better choice.

Approach

qufin pairs every quantum algorithm with its strongest classical counterpart. Portfolio optimization pits QAOA, VQE, and annealing against Mean-Variance, Black-Litterman, and HRP; option pricing runs 6 QAE variants against Black-Scholes and Monte Carlo; quantum VaR is computed via HHL. Everything runs on 11 backends — Qiskit, PennyLane, Cirq, Braket, CUDA-Q, D-Wave, IonQ, and Quantinuum among them — with 8 error-mitigation strategies for the noise realities of current hardware.

Outcome

An open-source framework of 159 modules in 14 subpackages, installable with a single command — pip install qufin — giving quants and researchers common ground for judging quantum methods against classical ones.

Key Features

• 159 modules across 14 subpackages
• 11 backends: Qiskit, PennyLane, Cirq, Braket, CUDA-Q, D-Wave, IonQ, Quantinuum
• Portfolio optimization: QAOA, VQE, and annealing vs Mean-Variance, Black-Litterman, and HRP
• Option pricing: 6 QAE variants vs Black-Scholes and Monte Carlo
• Quantum VaR via HHL
• 8 error-mitigation strategies
• Install with pip install qufin

Stack & Methods

• Python — pip install qufin
• Backends: Qiskit, PennyLane, Cirq, Braket, CUDA-Q, D-Wave, IonQ, Quantinuum
• Quantum algorithms: QAOA, VQE, annealing, QAE, HHL
• Classical baselines: Mean-Variance, Black-Litterman, HRP, Black-Scholes, Monte Carlo
• 8 error-mitigation strategies
• Head-to-head quantum-vs-classical benchmarking