Unlock the full potential of IonQ trapped ion hardware with Fire Opal on Amazon Braket

If you’re looking to push the limits of IonQ hardware and achieve the most impactful outcomes, Fire Opal is here to help. Fire Opal now supports IonQ’s quantum processors accessible through Amazon Braket. We are excited to launch this at this year's SXSW - hear from senior leaders at AWS, IonQ, and Q-CTRL at our upcoming session!

With a single command, you can boost the performance of IonQ hardware across your workloads with no need to worry about the intricacies of quantum hardware.

Fire Opal has a proven track record of optimizing quantum hardware, starting with error suppression on superconducting processors. Thanks to its hardware-agnostic design, we’re now extending that same powerful technology to IonQ’s trapped-ion QPUs.

Our AI-driven error suppression pipeline is built to maximize the performance of any gate-based quantum computing system, making Fire Opal a universal performance-management solution. Over the past few months, our team has been experimenting with IonQ’s ever-expanding fleet and demonstrating significant performance gains across their cutting-edge devices, including IonQ Forte, IonQ Aria 1, and IonQ Aria 2.

Now that capability is available to you. The launch of Fire Opal support for IonQ systems is the first step in an exciting rollout of error suppression techniques on trapped ion hardware. The impressive benchmarking improvements already achieved only represent a small fraction of the gains that Fire Opal can achieve. Over the next few months, we’ll push results even further with methods like dynamical decoupling and AI-driven gate optimization.

Last year, we collaborated with Accenture Federal Services, where they validated the performance benefits on their network anomaly detection application. Now that capability is available to you, self-serve and right out of the box through Fire Opal on Amazon Braket. We’re also offering private preview access of our noise-aware, fully abstracted optimization solver—technology that’s redefining what's possible with gate-based quantum computers—on IonQ.

This milestone unlocks new opportunities for developers and researchers to push the boundaries of quantum computing—enabling deeper, wider algorithms with higher fidelity than ever before. And these performance benefits only begin to highlight what becomes possible with larger IonQ devices coming online.

Check out the documentation to get started on IonQ.

Push IonQ devices to new heights on industry benchmarks

Fire Opal is performance management software that fully automates the application of error suppression techniques. It addresses the fundamental challenge of quantum hardware—noise. By dramatically improving hardware performance today, Fire Opal also paves the way for better algorithmic execution and more efficient quantum error correction protocols in the future.

Amazon Braket seamlessly integrates with Fire Opal, providing secure authentication, reliable job execution, and direct connectivity to IonQ devices. This combination of Fire Opal’s AI-driven error suppression with Amazon Braket’s robust infrastructure unlocks new possibilities for users running workloads on IonQ hardware.

Trapped ion quantum computers like IonQ Forte and IonQ Aria platforms offer extremely high-quality qubits and native gates, captured by metrics such as coherence times that measure in the seconds and some of the highest gate fidelities demonstrated in commercial quantum computing hardware to date. But even the best quantum hardware naturally leaves room for error reduction. By combining the benefits of IonQ hardware with Fire Opal, users can now:

• Achieve higher accuracy while running deeper and wider circuits
• Apply error reduction methods to improve performance across a broad range of use cases
• Accelerate real-world applications

We demonstrate these capabilities by running application-oriented benchmarks used to test the performance you can expect for real use cases. We have adapted these benchmarks from the QED-C repository and focused on posing the fairest and toughest tests of our technology. And historically, Fire Opal has delivered huge wins across these tests.

On IonQ systems, Fire Opal delivers impressive improvements to benchmarks, such as the Bernstein–Vazirani Algorithm, Quantum Fourier Transformation (QFT), and Quantum Phase Estimation (QPE). For example, QPE—traditionally a very challenging algorithm due to the scaling of two-qubit gates—success probability jumps from 55% to nearly 70% at 30 qubits with Fire Opal.

These benchmarks highlight how Fire Opal can help you push the boundaries of today’s quantum systems. And remember, because Fire Opal is deterministic and works with any algorithm, you can secure performance improvements right up to the intrinsic limits set by hardware with no overhead. No extra shots, no extra compute time, and no qubits wasted on encoding.

With automated tools helping you achieve the best possible performance, you can focus on developing and validating novel quantum use cases. We’re excited to see how much further these numbers improve with the full Fire Opal pipeline unleashed on IonQ.

Review all benchmarks on our documentation site.

Error suppression—an essential addition to an application developer’s  performance-management toolkit

Fire Opal's error suppression is an excellent addition to the error mitigation tools offered on the IonQ platform and available through Amazon Braket—debiasing and sharpening. Error suppression broadens the range of applications that can be enhanced and doesn't incur any quantum runtime overhead, meaning you can save on compute costs. Moreover, selecting between methodologies doesn’t require expert-level knowledge—it just works.

For background, let’s first talk about the existing error mitigation tools available for IonQ hardware. 

Debiasing is a method that corrects errors by running the same calculation in different ways—different “variants” of the physical qubit mapping, gate decomposition, and similar—and then aggregating the results in a way that cancels out certain kinds of systematic error on the device. 

Once you have all of the variants, you can use one of two aggregation methods to combine and post-process the results: “component-wise averaging” (“averaging” for short) and “plurality voting” (or “sharpening”). Knowing which aggregation method to apply requires understanding in detail the type of output distribution that you’re expecting.

Averaging works best for scenarios where most output states have non-zero probabilities. However, when output distributions are meant to have high contrast, meaning that there are high peaks and low troughs, averaging tends to “smooth out” the distribution, which makes it harder to distinguish the high-probability outputs from noise. 

Sharpening more directly increases the probabilities of the most common outputs. This method is effective for quantum algorithms with a small number of highly probable outputs (e.g., phase estimation or amplitude estimation), but for algorithms with complex or non-peaked output distributions, this can provide suboptimal or even unhelpful results, which means you have to know what kinds of outputs you’re expecting and tune the approach on a per-algorithm basis. 

Fire Opal’s error-suppression pipeline modifies the circuit instructions before the circuit is run on the device, proactively preventing errors where they’re most likely to occur, from the gate level to the circuit level. Because they don’t depend at all on the algorithm’s outputs, Fire Opal’s methods can be applied broadly to any type of algorithm. And applying error suppression requires no aggregation of results, so it comes at no additional cost.

Together across debiasing, averaging, sharpening, and now Fire Opal you have a complete suite of tools that you can leverage to counter errors and achieve meaningful results on IonQ hardware.

Abstract away quantum hardware and accelerate quantum optimization 

The next stage in maximizing value from quantum workloads requires abstracting hardware entirely so developers can focus on the problems they want to solve rather than the intricacies of executing quantum circuits. As a major step in this direction, Fire Opal offers a fully packaged hybrid Optimization Solver that enables you to solve real-world optimization challenges on quantum hardware while completely abstracting circuit-level complexity.

The Optimization Solver accepts high-level problem definitions, allowing domain experts to test quantum solutions without having to build the entire algorithm from scratch or even develop any understanding of quantum computing. The entire workflow is noise-aware and leverages Fire Opal’s core performance management under the hood, meaning that you can run problems at full device scale and achieve results far superior to any standard algorithm implementations. 

As noted earlier, Accenture Federal Services used Fire Opal’s Optimization Solver to implement a network anomaly detection application on IonQ hardware. Using a quantum optimization solution tested on a real network traffic dataset, Accenture Federal Services built a solution that achieved three times greater accuracy than a classical heuristics-based solution.

We have seen firsthand how Fire Opal enhances the performance of today’s quantum hardware, delivering measurable improvements in anomaly detection and large-scale optimization challenges. As Fire Opal expands its compatibility with devices like IonQ through Amazon Braket, it opens the door for broader adoption of Q-CTRL’s error suppression techniques. This integration would help accelerate the practical application of quantum solutions and empower researchers and developers at AFS to achieve more accurate and reliable results in critical areas like cybersecurity and optimization.

Raymond Beecham, Security Delivery Specialist at Accenture Federal Services.

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