Keysight Quantum Circuit Simulation with Google Quantum AI Flux Quantization

Quantum Circuit Simulation (Quantum Ckt Sim), a new innovative circuit design environment from Keysight Technologies. The new circuit design environment is said to accelerate the creation of complex quantum circuits. Furthermore, the solution integrates advanced flux quantization—an industry first—by collaborating with Google Quantum AI.

Precisely flux quantization modeling is crucial in the field of superconducting quantum circuits. This basic characteristic guarantees discrete quantization of the magnetic flux across a superconducting loop, which is essential for quantum circuit functionality.

Improving Quantum Circuit Simulations

In order to solve this problem and improve quantum circuit simulations, Google Quantum AI and Keysight have partnered to incorporate frequency-domain flux quantization into circuit solvers. The new approach helps researchers to develop more dependable and effective superconducting circuits by accurately predicting flux quantization.

With its superior flux quantization in the frequency domain, the new method streamlines the quantum workflow and offers unmatched precision and analytical power for large-scale, highly nonlinear quantum circuits.

This improves the accuracy with which researchers can modelcomplex quantum circuits, lowering computational errors and raising the general reliability of simulations.

Key Features of the Quantum Circuit Simulation Design and Simulation Solution Include:

• Quantum Devices Library – Incorporates a library of quantum devices in Keysight ADS encompassing those most frequently used such as RF/DC SQUIDs, SNAILs, FLUXONIUMs, and SNAKEs.

• Comprehensive Design Environment – Features a range of nonlinear circuit simulators such as harmonic balance, transient / convolution for time domain, circuit envelope for modulation domain, and x-parameters nonlinear model generator.

• Enhanced Quantum Control – Enables the driving of superconducting circuits with external flux, allowing for more precise control and manipulation of quantum circuits in advanced quantum computing applications.
• Streamlined design – Simplifies the design of parametric quantum circuits including quantum amplifiers, the critical blocks on the output chains of quantum systems that improve readout fidelity.

With its establishment as a major force in the field of quantum computing, Google Quantum AI has made significant milestones in the field.

Google has accomplished significant milestones with its cutting-edge quantum processors and innovative research, such as demonstrating beyond-classical computation and actively participating in the development of sophisticated quantum amplifiers—which are crucial for improving the performance of quantum systems.

Ofer Naaman, Research Scientist, Google Quantum AI, said: “Using Quantum Ckt Sim, it is now possible to enforce flux quantization conditions in ADS frequency-domain simulations of superconducting devices. This is a critical capability whose absence thus far limited the usability of modern EDA tools in microwave superconducting circuit design.”

Mohamed Hassan, Quantum Solutions Planning Lead, Keysight EDA, said: “It’s thrilling to witness the accurate modeling of frequency domain flux quantization of superconducting circuits using an EDA tool for the first time. This significant milestone leverages EDA capabilities to streamline the design of superconducting microwave circuits for quantum applications and beyond. We anticipate this advancement will empower quantum engineers to enhance the performance of parametric quantum circuits, particularly in terms of power handling and bandwidth, which are crucial for the readout of qubits in quantum computers.”

The collaboration’s success is detailed in a recently posted technical paper, titled “Modeling flux-quantizing Josephson junction circuits in Keysight ADS”. This paper demonstrates the innovative approach to flux-quantization and its significant impact on the field of quantum computing.  The success sets a new standard for accuracy and efficiency of modeling superconducting circuits.