Q-CTRL digest
Running quantum computers from any device at TiEcon 2025
May 9, 2025
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.
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.
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.
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.
Learn more
Where to find Fire Opal
Real-world use cases
Accelerating the schedule for quantum-enhanced rail
Accelerate the usefulness of quantum computing for rail scheduling through custom solution development and performance optimization utilizing Fire Opal.
6X
increase in solvable problem size and accelerated timeline to practical quantum advantage by up to 3 years, now estimated for 2028.
Read the case study
We were pleasantly surprised to see the optimal routing of 26 trains over 18 minutes of real scheduling data for the full station topology being realised on a real quantum device, which otherwise wouldn’t have been possible without using Q-CTRL’s optimisation solver.
Nadia Hoodbhoy
Principal Engineer
,
Network Rail
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.
8X
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
Higher-quality and more flexible scheduling is a challenging but critical component for future rail services. Q-CTRL’s work has shown how quantum computing can enable and potentially accelerate this capability, with promising results already on their roadmap towards a real-world quantum solver demonstration.
Sarah Sharples
Chief Scientific Advisor
,
Department for Transport
We were pleasantly surprised to see the optimal routing of 26 trains over 18 minutes of real scheduling data for the full station topology being realised on a real quantum device, which otherwise wouldn’t have been possible without using Q-CTRL’s optimisation solver.
Nadia Hoodbhoy
Principal Engineer
,
Network Rail
Using Q-CTRL's performance management software, we validated the efficacy of a novel quantum machine learning method on IBM hardware. The performance gave us confidence that in the coming years, quantum will play a crucial role in our commercial operations.
Hiroshi Yamauchi
Senior Manager, Research Institute of Advanced Technology
,
SoftBank Corp
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