This blog has looked at some of the great technical work being done by our quantum control engineers led by Dr Michael Hush. We've seen how their team has taken experimental insights developed over the past decade and built them into powerful computational tools for the efficient characterization and suppression of noise and errors in quantum hardware.
But how do we get this awesome technology into your hands? Well, that needs the development of products that you can unleash in your lab, on your quantum system, for your quantum machines.
Enter Michael Dijkstra and Kevin Nguyen. They're working hand-in-glove with the Quantum Control Team to build an intuitive product that allows you to extract maximum performance from your quantum hardware without needing to hire a team of full-time Quantum Control Engineers.
This convergence of quantum technology and product development is building Black Opal, a cloud-based platform to help you design, analyze, and optimize drop-in replacements for your quantum gates that suppress errors and improve the performance of your hardware.
The teams work together in "modelling" sessions, where the Product Team bring problems that clients raise and the quantum engineers find feasible ways to solve them.
Lead Quantum Control Engineer Michael Hush said: "There is always a dialogue between the product and technical team with every new feature we add. Typically, the technical team will explain a complex control technique and the product team will boil it down to the essence of what's important for the end user.
"It's a process that goes both ways," he said. "It's a great collaboration."
Building an Intuitive Process
Michael Dijkstra, Head of Product, said that calculating filter functions for control characterization is a great example of how this works. Remember, "these are simple computational heuristics which help us design controls that are able to reduce errors in quantum hardware.
For a single-qubit system, we only need a couple of inputs no matter what your hardware platform is: trapped ions, superconducting circuits, and so on.
With two qubits, the process of calculating the filter function becomes more involved and needs customization depending on whether your system uses Molmer-Sorenson gates, cross-resonance gates, parametric-drive gates, etc.
Mr Dijkstra said: "We are building a nice front-end for those customers so we can easily walk them through the process.
"The quantum team has developed a new algorithm enabling them to calculate generic two-qubit-gate filter functions for the first time, but the inputs vary slightly depending on the sort of hardware system you're using. We design the front-end to make the process as intuitive as possible," he said.
Once the client has entered relevant inputs, this information is sent to the back-end where the magic happens. This process goes through the API (application programming interface), that Kevin Nguyen, Lead Software Engineer, has developed. The client's data is taken through to the Python module, which performs the relevant computations and outputs objects such as the filter function or pulse waveforms based on the system inputs.
Mr Nguyen sees himself as the bridge between the front-end interface and the custom-designed work-horse algorithms at the back end.
"My work helps link user inputs to the Python library which the quantum team is developing. Combined with the intuitive front end, that's the essence of our service. We're building a fully functional and easy-to-use platform that customers can subscribe to and in return they get access to our Python library of algorithms, our smarts, and our control solutions," he said.
Dr Hush said: "There is a virtuous cycle developed between the quantum and product teams. A lot of the design input is ultimately coming from customers through discussions with our CEO, Professor Michael Biercuk.
"With some problems, it's very clear how we move forward to a control solution. Other times we need to work more closely with the clients and perform new research to develop customized controls."
"The value of Black Opal is evident when you link a few features together," according to Mr. Dijkstra.
Using the same platform, clients can generate optimal controls allowing them to efficiently characterize noise sources in their hardware. Then they can seamlessly import this information directly into another part of the product to explore how well known control solutions will mitigate the dominant forms of noise.
"If one of the known controls from our library isn't the right solution, customers can use our machine-learning optimization package that will generate a customized control.
"This whole process ensures customers use the best control solutions to suppress the noise in their systems." This is all enabled by the architecture of our products - seamlessly integrating the various tasks encountered in developing novel quantum control solutions.
CEO and Founder of Q-CTRL, Professor Biercuk, makes clear this is an iterative process: "Once you generate the right controls to suppress your primary noise source, something new will emerge as the limit to your system's performance. Moreover, systems tend to drift and change in time requiring re-optimization.
"We help our customers every step of the way as they continually seek to reduce errors in their hardware, and extract maximum performance."
So, what's next? "We are continuing to trial these products internally and plan an alpha release ahead of the public launch of Black Opal later in the year."