qBraid's Quantum Algorithm Wins 1st Best Paper Award in th Quantum Algorithm track at IEEE QCE

‍

‍
[Seattle, 9/18/23] — qBraid, a pioneering cloud-based quantum computing software startup, is thrilled to announce that their paper, "Benchmarking Variational Quantum Circuits with Permutation Symmetry," has received the prestigious Best Paper Award for quantum algorithms at the IEEE Quantum Computing & Engineering (QCE) conference.
The award-winning paper introduces SnCQA, a set of hardware-efficient
variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries with the number of qubits n. "We are incredibly honored to receive this recognition from IEEE QCE," said Kanav Setia, CEO and co-founder of qBraid. "SnCQA represents a novel class of variational quantum ansatze that is a promising design for NISQ hardware. This achievement underscores qBraid's commitment to pushing the boundaries of quantum software and algorithms,” remarks qBraid’s research scientist Junyu Liu. A special shout out goes to our summer intern Han Zheng as a notable lead contributor to the work. Further, qBraid would like to thank our collaborators, Gokul Subramanian Ravi, Hanrui Wang, and Fredrick Chong.

Key Results (taken directly from paper):

- Architecture Design: SnCQA architectures based on theoretical foundation established in [17],[23] in the QAOA (Quantum Alternating Approximation Algorithm) form, and design their hardware-efficient version. We show that the hardware-efficient versions are implementable in the near-term quantum devices.
- Resource benchmarks: suggest that compared to pHEA (pure hardware-efficient ansatz) SnCQA could save from 200% to 1000% hardware resources with comparable performance.
- Noise resilience: We benchmark the performance ofSnCQA with shot noises simulated using IBM Qiskit [26] and qBraid [27]. We find that SnCQA is sufficiently noise resilient in our cases even with significant amount of shot noises.
- Theoretical advantages: We present theoretical arguments favoring SnCQA, including quantum neural tangent kernel [28]–[31], universality [17], quantum advantage [23], decoherence-free subspaces [32]–[34] and saddle-point avoidance [35].


Read more about the work here.

About qBraid:
qBraid is a leading quantum computing software startup focused on developing innovative solutions for quantum algorithm design and optimization. By leveraging the power of quantum computing, qBraid aims to accelerate advancements in various fields, including chemistry, materials science, machine learning, and more.

‍