#QuantumPortfolioOptimizer

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Quantum Portfolio Optimizer: Global Data Quantum, IBM Qiskit

Portfolio optimisation for quantum computing

Global Data Quantum introduced the Quantum Portfolio Optimiser function in IBM Qiskit. Quantum computing optimises investment portfolios.

A detailed breakdown:

Quantum Portfolio Optimiser Goal

The Quantum Portfolio Optimiser optimises investment performance while reducing transaction costs and risks. Its dynamic portfolio optimisation goal is to find the optimum investment plan across many time periods to maximise projected return and minimise risks, often while considering budget, transaction costs, and risk aversion. Dynamic portfolio optimisation modifies assets based on asset performance, unlike traditional portfolio optimisation, which uses a single rebalancing time. The program targets analysts, investors, and portfolio managers. Portfolio optimisation allows backtesting trading approaches.

Quantum Portfolio Optimiser Access:

Discover the function in IBM Qiskit Functions Catalogue. This experimental functionality is only available to IBM Quantum Premium and Flex Plan users in preview release. Request a catalogue to access Global Data Quantum.

Quantum Computing—Why?

Traditional methods become slow and inefficient as resources or limits increase. Quantum computing's capacity to analyse several variables in parallel can solve complex problems faster and more efficiently than classical solvers like CPLEX, Gurobi, and Pyscf on HPC resources.

Quantum Portfolio Optimiser Functions?

The Quantum Portfolio Optimiser has four steps:

It receives financial asset values and user-specified investing conditions.

Quantum circuits convert classical input data into a quantum-resolution problem. This requires constructing the dynamic portfolio optimisation problem using Quadratic Unconstrained Binary optimisation (QUBO) and converting it into a quantum operator (Ising Hamiltonian).

The Variational Quantum Eigensolver (VQE) algorithm is considered. The VQE was designed to determine the optimal solution-wide investment combinations. In this hybrid quantum-classical approach, the quantum circuit estimates the cost function and Differential Evolution is used for classical optimisation.

Adjusting post-processing to eliminate quantum device noise yields an optimal, trustworthy, and realistic recommendation. For optimal output, the system uses noise-aware (SQD-based) post-processing.

Formulating Problems

Portfolio optimisation uses multi-objective Quadratic Unconstrained Binary Optimisation (QUBO). The QUBO function optimises four goals:

Max out the return function (F).

Reduce investment risk (R) and transaction costs.

Respect investment limits. The QUBO function is defined as O = -F + (γ/2)R + C + ρP, where γ is the risk aversion coefficient and ρ is the constraints reinforcement coefficient (Lagrange multiplier The minimum qubit count for a problem is the number of assets (na), time periods (nt), and bit resolution (nq) used to describe the investment.

Input

This function requires several input parameters:

A dictionary of asset prices uses dates as supplementary keys. All assets must have consistent data for the same dates.

Qubo_settings: A dictionary that configures the QUBO problem with parameters like nq resolution qubits, dt time window each step, maximum investment per asset, risk aversion coefficient, transaction charge, and restriction coefficient.

Optimizer_settings (Optional): Sets up the standard optimisation technique, including primitive settings (sampler_shots, estimator_shots) and differentiation_evolution algorithm parameters (num_generations, population_size).

ansatz_settings (Optional): Select “optimized_real_amplitudes” or “tailored” and enable multiple pass managers, dynamical decoupling, and other options to configure the quantum circuit ansatz.

Optional: QPU backend name, such as “ibm_torino.”

previous_session_id (Optional): A list of past session IDs to continue execution or retrieve data.

Apply_postprocess (Optional): True applies noise-aware SQD post-processing.

tags: An optional text list to label the experiment.

Output

Function returns two dictionaries: “result” and “metadata”.

Result: optimal optimisation outcomes, such as the optimal investment strategy over time and the lowest target cost. Investment weights are normalised by total investment.

Metadata: Metadata describes all optimisation results. It includes counts, investment pathways, objective costs, Sharpe ratios, returns, limitation violations, samples/states, and transaction costs. The session ID, asset order, QUBO matrix, and resource consumption summary are all included. Return, Sharpe ratio, restriction deviation, and least objective cost are key metadata for the best solution.

Application Function Context Qiskit

Application functions like the Quantum Portfolio Optimiser provide a comprehensive quantum pipeline by abstracting the quantum workflow. Because quantum methods use conventional classical inputs and return domain-familiar classical outputs, they can be easily integrated into present application processes without quantum computing knowledge.

Analysis of Performance and Benchmarks

The function is verified using different resolution qubit, ansatz circuit, and asset grouping configurations. Benchmarks evaluate solutions using two metrics:

Objective cost: To evaluate optimisation, the objective cost compares the cost function value to Gurobi (free version) output.

Sharpe ratio: Measures portfolio risk-adjusted return. Benchmark data shows the quantum optimiser finds viable investment plans. For a test using IBEX35 assets (Set 3, 4 time steps, 2-bit encoding, 56 qubits), the Optimised Real Amplitudes ansatz had an objective cost of -3.67 and a Sharpe ratio of 14.48, while Gurobi had 16.44 and -4.11. Comparing quantum sampling to random sampling, visual inspection shows that lower prices dominate the distribution.