Optimizing SWAP Networks for Quantum Computing: Experiment Demonstrates How Software-Optimized Circuits Run Less Error-Prone Quantum Algorithms – Science Daily | Region & Cash

A research partnership at Lawrence Berkeley National Laboratory (Berkeley Lab) Advanced Quantum Testbed (AQT) and Chicago-based Super.tech (acquired by ColdQuanta in May 2022) showed how to optimize execution of the network protocol critical to ZZ SWAP quantum computing. The team also introduced a new quantum error mitigation technique that will improve the implementation of the network protocol in quantum processors. The experimental data were published this July in Physical Review Researchh, to add more ways to implement quantum algorithms with gate-based quantum computing in the short term.

An intelligent compiler for superconducting quantum hardware

Quantum processors with two- or three-dimensional architectures have limited qubit connectivity, where each qubit only interacts with a limited number of other qubits. Additionally, each qubit’s information can only exist for so long before noise and errors cause decoherence, limiting the runtime and accuracy of quantum algorithms. Therefore, when designing and executing a quantum circuit, researchers must optimize the translation of the circuit, which consists of abstract (logical) gates into physical instructions, based on the native hardware gates available in a given quantum processor. Efficient circuit decompositions minimize operational time because they take into account the number of gates and operations natively supported by the hardware to perform the desired logical operations.

SWAP gates – which exchange information between qubits – are often introduced in quantum circuits to facilitate interactions between information in non-adjacent qubits. If a quantum device only allows gates between adjacent qubits, swaps are used to move information from one qubit to another, non-adjacent qubit.

For noisy intermediate-scale quantum (NISQ) hardware, introducing swap gates can require large experimental overhead. The swap gate often needs to be broken down into native gates, such as B. Controlled NOT gates. Therefore, when designing quantum circuits with limited qubit connectivity, it is important to use an intelligent compiler that can find, decompose, and eliminate redundant quantum gates to improve the runtime of a quantum algorithm or application.

The research partnership leveraged Super.tech’s SuperstaQ software, which allows scientists to fine-tune their applications and automate circuit assembly for AQT’s superconducting hardware, specifically for a high-fidelity native controlled S-gate based on the most hardware systems is not available. Using four transmon qubits, this intelligent compilation approach allows for more efficient decomposition of the SWAP networks than traditional decomposition methods.

A network of ZZ-SWAP gates requires minimal linear connectivity between qubits with no additional couplings, and therefore offers practical benefits for efficiently running quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA). QAOA approximates solutions to combinatorial optimization problems — finding the optimal answer by specifying a set of criteria. The maximum cut problem, which can be used to arrange hubs on a transport network system, is an example of a famous combinatorial optimization problem that can potentially be solved faster with QAOA using quantum circuits.

“One of the most difficult challenges in quantum computing is performing discrete logic operations. Because our control signals are analog and continuous, they are always imperfect. As we build more complex quantum circuits, the software infrastructure that optimally compiles gates will be tailored for AQT’s hardware, helping us achieve higher operational fidelity,” Akel Hashim, the experiment’s lead AQT researcher and graduate student at the University of California, Berkeley .

“A unique feature of quantum computing is that it enables partial logic gates. This function has no parallel in traditional Boolean logic – for example, your laptop computer cannot do 50% of an AND gate. AQT’s ability to calibrate these partial -S quantum gates opened the door for us to develop a broader range of novel optimizations to get the most out of the hardware,” said Rich Rines, formerly of Super.tech and currently a software engineer at ColdQuanta.

“A key software engineering challenge in this experiment was remote collaboration, so we iteratively developed quantum circuit optimizations informed by the custom gates that the AQT team calibrated. We optimized end-to-end by figuring out how to serialize these pulses, considering the hardware. We also figured out how to integrate open-source quantum software packages into our compiler to ensure our optimizations don’t reinvent the wheel,” said Victory Omole, formerly of Super.tech and a software engineer at ColdQuanta.

As part of the experiment, the team also introduced a novel technique called Equivalent Circuit Averaging (ECA), which randomized the different parameters of the SWAP networks to produce many logically equivalent circuits. ECA randomizes the decomposition of quantum circuits and mitigates the effects of systematic coherent errors – one of the most serious errors in quantum computers and extensively studied at AQT.

“I proposed a way to merge my earlier experimental work in randomized compiling with Quantum Benchmark (acquired from Keysight) using Super.tech’s intelligent compiler to investigate a new way to reduce the effects of crosstalk errors,” Hashim said. “I would not have had the insight to come up with this idea if I hadn’t been collaborating with other researchers as part of AQT’s user program. As someone about to enter the workforce, networking is critical to building a core base of people I know who are experts in different fields who I can also bring research ideas to.”

These experimental optimizations resulted in an improvement in the performance accuracy of QAOA by up to 88%. Researchers aim to further explore and refine the methods in this work and apply them to other applications.

Supporting industry growth with an open access research lab

AQT operates a state-of-the-art open experimental testbed based on superconducting circuits and is funded by the United States Department of Energy Office of Science Advanced Scientific Computing Research (ASCR) program. Technologies developed elsewhere can be deployed at AQT and tested in the field, providing full access to the entire quantum computing stack at no additional cost.

Since the launch of its user program in 2020, AQT has granted Super.tech, one of several industry users, low-level access to the hardware to test their ideas. Few cloud-based quantum platforms offer this kind of complete access to the entire quantum computing stack and real-time feedback from the hardware experts for free. Super.tech worked with AQT’s experimental team of experts to find ways to improve performance on this type of hardware.

“By exposing the inner controls of quantum hardware, AQT’s collaborative approach with users drives innovation across the quantum computing stack. We look forward to continuing our research collaboration with AQT and will continue to share these findings with the scientific community by publishing our findings,” said Pranav Gokhale, VP of Quantum Software at ColdQuanta and former CEO and co-founder of Super.tech.

AQT at Berkeley Lab continues to grow as a state-of-the-art center for quantum information research and development, bringing together expertise and users, including early-stage startups like Super.tech, who are now continuing their growth trajectory as part of ColdQuanta.

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