The benefits of Fire Opal on Rigetti

Fire Opal improves the execution of quantum algorithms by applying advanced error-suppression, including hardware-aware circuit optimization. Fire Opal’s integration with Rigetti applies these benefits to the latest Ankaa-3 device, connecting with Rigetti’s software toolchain. You can obtain benefits including successful algorithm execution at larger scales, and lower compute time and costs, using this comprehensive error-suppression pipeline with no configuration required on your end.

To demonstrate the impact of this integration, we implemented a series of benchmarks on the Ankaa-3 82-qubit processor including our quantum optimization solver, Bernstein–Vazirani (BV), and quantum Fourier transform (QFT) tests. Across all benchmarks, Fire Opal consistently provided enhanced outputs, with the benefit increasing with the problem size and circuit depth. For instance, we saw nearly a 30X improvement in QFT execution, showing Fire Opal's impact in preserving coherence during calculation, and >100X improvement in BV executions for problems using 14 qubits or more. These high-impact outcomes across diverse algorithms are consistent with our benchmarking results across other quantum hardware platforms.

Demonstrating value in finance: Portfolio optimization

As a practical real-world example, we examine portfolio optimization to show how Fire Opal enables successful and reliable quantum solutions using Rigetti hardware. Portfolio optimization allocates capital across assets to maximize expected return while minimizing risk and satisfying constraints such as budgets and sector exposure. These constraints make the problem combinatorially complex, often pushing classical optimization methods to their limits. At market scales exceeding $12 trillion, even small improvements in solution quality or time‑to‑result can translate into enormous financial impact and reduced exposure to risk. To address these computational challenges, we tailored our hybrid quantum optimization solver for portfolio optimization and deployed it on Rigetti’s Ankaa-3 82-qubit processor using Fire Opal. For a complete technical description of our quantum optimization solver, see our technical manuscript.

Using Nasdaq index data, we implemented a 30-asset (30-qubit) portfolio optimization problem and evaluated the effectiveness of our integration by measuring both the quality of the solutions obtained and the probability of sampling them as key performance metrics. Compared to the identical quantum algorithm run on the device without our integration, and to a local solver run on a standard computer, Fire Opal increased the probability of measuring the optimal solution by >30X and >160X respectively, as illustrated in the figure. The figure plots the sample distribution as a function of candidate solution cost. Using Fire Opal, the distribution is concentrated at the optimal solution.

Using Fire Opal together with Rigetti hardware delivers high-quality results at this scale, and we continue to enable successful execution of larger and deeper quantum circuits. The methods applied here for hardware‑aligned circuit execution such as robust layout selection, and post‑measurement error mitigation are applicable across all quantum algorithms. The portfolio optimization use case serves as a demonstration of how integration between quantum software and hardware can transform theoretical algorithms into operational tools. You can realize these benefits on Rigetti QCS, delivering immediate performance improvements in your own workloads. To use Fire Opal with Rigetti devices, please contact us and integrate this capability into your quantum solutions.