Opening new opportunities for you on 127-qubit devices
December 5, 2023
The age of quantum computing is here now. Rapid developments in this transformational field are bringing within reach the opportunity to solve problems that are impossible on even today’s best supercomputers. The potential benefits span across a range of industries from finance to pharmaceuticals, and more.
However, quantum computing hardware is plagued with error, which affects the accuracy of calculations.
Errors can arise from a range of environmental factors, such as disturbances in the Earth’s magnetic field or electromagnetic interference. Or they can arise from imperfections in the way we manipulate quantum hardware.
In the current “noisy intermediate scale quantum computing” (NISQ) era, the complexity and value of quantum algorithms end users can run are limited by the relatively low number of qubits available today. But more than that, they’re limited by the errors those devices suffer. Machines are not even delivering up to their full potential.
The R&D teams building quantum computers are working to improve their hardware and address the problem of errors. Quantum Error Correction (QEC), the most popular approach studied, is a solution for the future; it cannot give benefits yet and will require enormous numbers of physical qubits in order to do so.
Given this outlook, our team has been developing ways to get useful results from the quantum computers that exist today, and accelerate the pathway to large-scale machines running Quantum Error Correction.
When running an algorithm on today’s hardware, you’re more likely to get the wrong answer than the right one. Unless you’re running it with Fire Opal! We’re here to illuminate meaningful results amidst all the errors.
Fire Opal is a Python package that applies a complete suite of error suppression techniques to vastly improve the quality of quantum algorithm results. In most cases, it totally transforms quantum computer outputs from random to useful.
It bundles our performance-enhancing technology demonstrated to achieve up to 9,000x improvement over the best publicly available, expert-configured techniques applied to popular hardware backends.
Fire Opal’s technology accounts for errors before, during, and after runtime. It allows you to run more valuable algorithms on today’s quantum computers by reducing errors automatically, expanding the complexity of circuits that can be run. And these benefits let you see meaningful answers on the first pass - there’s no expensive sampling or other overhead required.
By enabling you to run deeper circuits with more qubits, Fire Opal helps you gain critical insights only available from hardware, accelerating the path to quantum advantage.
Take advantage of Fire Opal’s advanced technology with a single line of code. You can see benefits immediately, with no learning curve or settings; we’ve eliminated any need for configuration, which often requires unnecessary time, effort, and expertise in the underlying hardware details.
Fire Opal is hardware agnostic—we handle all the special configurations behind-the-scenes for you. At release, we are offering support across IBM backends, which will soon be joined by other providers’ hardware devices.
We collaborate closely with manufacturers to optimize our technology to the nuances of differing devices. Our team was one of the first to test Amazon Braket’s pulse level control feature, which works to enhance results across their vast portfolio of backends.
From the outset we wanted Fire Opal to be easy-to-use - allowing any algorithm developer or researcher to focus on the meaningful work, which helps advance the entire industry toward real world applications of quantum computing.
In our Get Started guide, we provide a step-by-step introduction using the Bernstein-Vazirani algorithm. In this snippet, you can see how easy it is to run the circuit using Fire Opal in just one execute call.
There are no other options, settings, or configurations to worry about. Fire Opal assembles all of the relevant techniques and makes them work brilliantly on any supported backend.
Most application developers need to run their algorithms on real hardware; simulators just don’t give the full picture of reality and can’t scale - and this need is growing every day.
Fire Opal can help drive down development costs by both helping algorithm designers use their hardware access most efficiently and by making low-cost machines perform like premium hardware.
Of course, having pre-packaged tools helps save time and eliminates the need to bring in experts. But Fire Opal also helps you lower compute costs more than 100x by enabling you to get the best possible results in a single execution with no randomization, sampling overhead, or post processing.
As you prototype and iterate, Fire Opal can let you push further with lower cost hardware systems. The results delivered by Fire Opal on publicly available “pay-as-you-go” hardware are comparable to results achieved on premium hardware which can be over 100x more expensive.
As of today, anyone can download the Fire Opal package and see benefits immediately, with no learning curve.
During our private beta, dozens of customers across diverse industries used Fire Opal to achieve drastic performance gains on real circuits that can achieve their business goals. can achieve their business goals.. These early trials reflect the comprehensive results we recently published in our technical manuscript.
Be sure to join our developer community to discuss ideas, get support, and stay up-to-date with the latest news.
We’re keen to learn about your use cases and how Fire Opal can best serve your needs. Our approach is to support and enable the entire quantum ecosystem, and we will continue to build our suite of products to help you achieve your goals!
November 28, 2023