See the light in the dark with automated performance optimization

Achieve meaningful results from quantum computers through fully automated error suppression — no hardware expertise required.

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Quantum Computing Software 2023

10X

Deeper circuits

1,000X

Reduced compute cost

1,000X

Improved accuracy

Fire Opal is the easiest way to make quantum computers useful

Today’s quantum computers are growing rapidly,
but errors hinder the ability to get useful results.

Fire Opal is an out-of-the-box cloud solution that allows you to solve your toughest quantum problems with over 100 qubits using Q‑CTRL’s proprietary automated error suppression technology.

Now anyone can boost the quality of their use cases run on real hardware while achieving massive cost savings. A single pipeline for abstracting hardware, automatically reducing error, and boosting algorithmic success on quantum computers will transform the value of your quantum applications.

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Achieve quality results at utility scales

Get meaningful results for high-value use cases on real quantum computers

Attack real-world use cases with over 100 useful qubits

Transform outputs from random to useful through automated error suppression

Gain critical insights only available from hardware

28, 30, 120 qubit Maxcut problems via QAOA algorithm

Reduce compute time and cost

Reduce costs by >1,000x by getting clearer results from each execution

Maximize your quantum compute budget with overhead-free execution

Reduce shot counts and improve signal quality

Eliminate failed executions with performance optimization and instructive warnings to preserve your budget

Build quantum solutions with ease

Run use cases at peak performance through hardware abstraction

Input your custom algorithm or run entire workflows with a single command and never see a quantum circuit

Implement a comprehensive error-suppression pipeline with no code, no settings, and no configuration

Move your applications across multiple hardware backends without reconfiguration

How it works

Fire Opal delivers AI-powered hardware optimization and abstraction so you can see real results for your quantum use cases. It connects directly to supported hardware backends and returns the best output achievable without the need for any user intervention or hardware knowledge. Focus on results and impact instead of fighting errors.

What’s included

The industry’s best fully-configured toolkit to deliver world-leading performance for quantum applications to any user.

Read the docs

Automated error suppression

Fully automated, AI-driven error suppression and mitigation, delivered with a single line of code for any algorithm, benchmarked as the best on real hardware.

The best compiler in the industry

Circuit-level error reduction and efficient transpilation integrated and validated to outperform the competition.

High-level application solvers with no quantum circuits

Hardware-optimized algorithmic modules for popular hybrid subroutines, such as efficient Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), etc.

Automated backend selection*

Flexible connection to multiple backends and programming languages through a single interface, delivering results tailored to your circuit.

*Automated backend selection coming soon

Fire Opal is directly integrated with leading quantum development platforms

Easily design and test new applications and deploy directly for performance-optimized hardware execution in a single workflow.

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Real-world use cases

Delivering quantum computing for faster commuting

Improving algorithmic success for Transport for NSW's complex transport network management and congestion problems using quantum control infrastructure software

>200X

Improvement in algorithmic success.

Read the case study

A rare opportunity for leading transport innovators and quantum computing experts to tackle complex transport network management and congestion problems.

Andrew Constance

Minister for Transport and Roads

Transport of NSW

Enabling data loading for quantum machine learning with Fire Opal

BlueQubit demonstrated groundbreaking loading of complex distribution information onto 20 qubits for a QML application by using our error suppression product.

Better performance in terms of Total Variational Distance (TVD), which measures the deviation from perfect data loading.

Read the case study

As we develop novel techniques to solve some of the quantum industry’s hardest challenges, Fire Opal is an essential tool to reduce the impact of hardware noise and demonstrate successful results with deeper and wider circuits.

Hayk Tepanyan

Chief Technology Officer

Blue Qubit

Improving Army logistics with quantum computing

With Fire Opal, the Australian Army tested and validated a quantum computing solution on real hardware that promises to outperform their existing methods.

12X

improvement in the likelihood of finding an optimal solution with Fire Opal over the default hardware execution

Read the case study

Optimally routing 120 convoys can take more than a month of classical computation. The Australian Army is evaluating the potential of quantum computing to provide improvements; however, it’s been difficult to validate the feasibility of a quantum solution due to hardware noise. With Fire Opal, an algorithmic enhancement software, we are able to achieve results on quantum computers that build confidence in our quantum roadmap.

Marcus Doherty

Australian Reserve Officer

Australian Army

Reducing quantum compute costs 2,500X with Fire Opal

With Fire Opal a financial company was able to run algorithms on more cost-effective hardware systems while achieving results comparable to more premium systems

>2,500X

Reduction of quantum compute cost

Read the case study

We wanted to challenge Fire Opal’s capabilities by running a quite complex, unoptimized circuit. The results were extremely promising. The only comparable results we’ve seen have come from hardware that is currently too expensive to run extensive tests on.

Dr. Valtteri Lahtinen

Chief Scientific Officer

Q-CTRL Partner

We find it particularly appealing that the product is both circuit agnostic and quick, which will allow for fast prototyping. This is valuable in the short term to enable us to push further with end-to-end, proof-of-concept explorations on current hardware.

Julian van Velzen

CTIO & Head of Quantum Lab

Capgemini

Fire Opal achieved 4.5x more accuracy and 2x reduction in execution cost compared to the baseline. The experience clearly indicates how much more cost-effective quantum computing can be when using the right tools.