Qruise and Quantum Machines Collaborate on Advanced Model Learning Algorithms to Improve Quantum Gates
In a collaborative study by Qruise and Quantum Machines, a team of researchers has applied Qruise’s advanced Model Learning algorithms to significantly improve superconducting flux-based entangling gates. By combining data from multiple experiments, the team developed a highly accurate digital twin of the quantum system to reverse engineer signal distortions.
Towards a Two-Qubit Gate Fidelity
One of the key limiting factors for large-scale quantum computing is the fidelity of two-qubit gates, where the fidelity is the accuracy and reliability of the gate executing the desired task to produce the desired outcome. To obtain high gate fidelities, it is very important to accurately know the transfer function (the distortion of the intended signal due to the control electronics). Current approaches to determine the transfer function rely on running a particular experiment called Cryoscope [1]. The input signal to the gates can be reconstructed through careful analysis of the data, and experimentalists design an inverse filter to correct particular aspects of the signal.
The problem with this approach is that it requires a specific implementation of the Cryoscope experiment and a specific analysis technique in order to obtain only the transfer function. While this is not an issue if one wishes to focus only on this particular flux distortion issue, the need to design and implement different experiments for each parameter and physics phenomenon required is onerous. Much of this load can be alleviated with Model Learning.
Optimizing Quantum Gates with Model Learning
A highly adaptable feature of the Qruise ML, Model Learning can determine any number of system parameters from any relevant experimental dataset by optimizing the model to best fit the data. In this work, the team combined data from multiple experiments to develop a single model describing in detail the control electronics and quantum device and generate a highly accurate digital twin of the system. Focusing on the flux line transfer function, they automatically tuned the model to resolve flux distortion effects, among others, and generated inverse filters to correct them.
Using this advanced Model Learning algorithm, the team learned 83 parameters from a single chevron plot of a square pulse of varying duration and amplitude. Specifically, the qubit-qubit coupling, flux periodicity, and DC bias were extracted, as well as 80 parameters describing the combination of FIR and IIR transfer functions of the flux line.
“Applied widely, Model Learning can help obtain a predictive detailed model of quantum hardware, allowing researchers to not only obtain the best fidelities possible for a given system but also determine a detailed error budget,”
— Shai Machnes, CEO of Qruise
The learned model was validated three times: first, by performing optimal control on the updated model and inverting the transfer function, thereby enhancing the contrast by 1.5 times; second, by showing the model correctly predicts uncorrected bipolar flux pulses [2]; and third, by correcting the flux-line distortions by correcting the phase shifts and enhancing contrast by 10%.
A perfectly designed control pulse is necessary as high-fidelity quantum gates require precise interaction between qubits. Due to the extremely high accuracy of their model, the team was able to design an optimal filter that corrected for the system distortions, significantly improving upon the Cryscope method.
Maximizing Qubit Performance: Two-Qubit Gates and Beyond
These results from Qruise and Quantum Machines are highly significant and will likely be of great use in the development of high-fidelity two-qubit gates and beyond. Part of the technique’s beauty is how versatile it could be, allowing the determination of an arbitrary set of model parameters from any set of relevant experimental data.
Although this particular study focused on flux-based two-qubit superconducting gates, the method could easily be generalized to different types of gates and, indeed, different qubit systems. “Applied widely, Model Learning can help obtain a predictive detailed model of quantum hardware, allowing researchers to not only obtain the best fidelities possible for a given system but also determine a detailed error budget,” says Shai Machnes, CEO of Qruise. “This error budget provides researchers with actionable information as to which physical factors are limiting gate fidelities, allowing for maximum performance enhancement between successive device iterations.”
References
[1] Rol, M. A., Ciorciaro, L., Malinowski, F. K., Tarasinski, B. M., Sagastizabal, R. E., Bultink, C. C., … & DiCarlo, L. (2020). Time-domain characterization and correction of on-chip distortion of control pulses in a quantum processor. Applied Physics Letters, 116(5).
[2] Negîrneac, V., Ali, H., Muthusubramanian, N., Battistel, F., Sagastizabal, R., Moreira, M. S., … & DiCarlo, L. (2021). High-fidelity controlled-z gate with maximal intermediate leakage operating at the speed limit in a superconducting quantum processor. Physical Review Letters, 126(22), 220502.
About the Companies
Qruise, founded in 2021, is creating a unique machine-learning software to debug and reverse-engineer physical systems for R&D labs developing new devices. Their mission is to revolutionize physics-centric R&D by providing virtual physicists to work alongside human physicists and engineers in labs developing cutting-edge technology, starting with quantum computers and quantum sensors.
Quantum Machines (QM) accelerates the realization of practical quantum computing that will disrupt all industries. Our comprehensive portfolio includes state-of-the-art control systems and cryogenic electronic solutions that support multiple quantum processing unit technologies. QM’s OPX family of quantum controllers leverages unique Pulse Processing Unit (PPU) technology to deliver unprecedented performance, scalability, and productivity. Easily programmable at the pulse level or gate level (standard de facto OQASM3), OPX runs even the most complex quantum algorithms right out of the box – including quantum error correction, multi-qubit calibration, mid-circuit frequency tracking, and more. With hundreds of deployments, Quantum Machines’ products and solutions have been widely adopted by national and academic research labs, HPC centers, quantum computer manufacturers, and cloud service providers.
