Each spectral type relies on temperature, and luminosity and can be further divided into luminosity classes. Stellar classification is a technique based on stellar spectroscopy data used to classify stars. Louis Chen, Henry Makhanov, and Felix Xu of Team Durchmusterung earned second place for their exploration of Quantum-Enhanced Support Vector Machines for Stellar Classification. Second place: Quantum-Enhanced Support Vector Machines for Stellar Classification This project demonstrates that the simulation of noisy quantum circuits is an invaluable tool for exploring hardware-efficient ansatzes for NISQ devices. Comparison of the PennyLane default.mixed density matrix backend with PennyLane Lightning Qubit and PennyLane-Lightning-GPU (backed by cuQuantum), with 200 trajectories for each It became clear that PennyLane-Lightning-GPU is ideally tuned for larger simulations, outperforming the CPU simulator above 15 qubits. MFC then implemented benchmarks of the various backends of varying qubit counts. Their exploration shows that using roughly 200 trajectories is good enough. In an effort to make an apples-to-apples comparison, the team explored the effect of varying the number of trajectories on the fidelity of the stochastic noise algorithm. Team MFC took their process one step further and compared PennyLane Lightning Qubit (CPU-state vector simulator) with the PennyLane-Lightning-GPU (GPU-state vector simulator backed by cuQuantum) with the PennyLane default.mixed simulator backend by the native density matrix backend. Convergence to the ground state energy with Variational Quantum Eigensolver on the PennyLane-Lightning-GPU plugin accelerated by cuQuantum But most interestingly, intermediate levels of noise can lead to a faster and more accurate convergence, compared to the noiseless run shown in Figure 1. Next, the team explored the effects of this noise on finding the ground state of the transverse field Heisenberg model using the Variational Quantum Eigensolver (VQE).Īs one might expect, high levels of noise led to unstable solutions. The noise was modeled by injecting a depolarizing noise channel after each CNOT operation. Team MFC’s work relies upon Trotter-Suzuki decomposition to approximate Hamiltonian evolution. By understanding noise sources present in the target system, developers may be able to design ansatzes to mitigate that noise, or even take advantage of it. This work is critically important for anyone interested in running algorithms on real quantum processing units (QPUs). Team MFC’s project, Accelerating Noisy Algorithm Research with PennyLane-Lightning and NVIDIA cuQuantum SDK addresses the computational complexity of noisy simulations with the PennyLane-Lightning-GPU plugin and the NVIDIA cuQuantum SDK. Team MFC, consisting of Lion Frangoulis, Cristian Emiliano Godínez Ramírez, Emily Haworth, and Aaron Sander, won first place in the NVIDIA Challenge. First place: Accelerating Noisy Algorithm Research with PennyLane-Lightning and NVIDIA cuQuantum SDK The top three winning projects of the NVIDIA Challenge (detailed below) were awarded to the most rigorous scientific explorations, backed by good performance optimizations and presentations within the time allowed. Winning projects underscore quantum computing use cases These projects were graded on innovative ideas, scientific rigor, and capabilities with GPU-based simulators and workflows. Simulating the quantum dynamics of a space-time wormhole.Machine learning (ML) to design better variational quantum algorithms.Genomic error correction and resequencing.Participants in the NVIDIA Challenge built 23 incredible projects, including, but not limited to: Additionally, the top 24 teams received a second NVIDIA A100 80 GB GPU. At this time, 36 of these teams received private beta access to CUDA Quantum. The top 72 teams were given access to a single NVIDIA A100 80 GB GPU for 6 hours a day.Ī few days into the event, teams were asked to submit their projects in progress for consideration for additional power-ups. In support of the NVIDIA Challenge, Cyxtera provided three DGX A100 Stations located around the globe, and Run:ai provided the user management and orchestration interface. Both of these tools are optimized for the most powerful NVIDIA GPUs in the world-the same GPUs used to train ChatGPT. This included the NVIDIA cuQuantum SDK and an early-access version of NVIDIA CUDA Quantum, now available in open source. As part of the challenge, the top teams received access to the NVIDIA Quantum Platform, consisting of NVIDIA quantum software. The event was organized by Xanadu, with NVIDIA sponsoring the QHack 2023 NVIDIA Challenge. This year at QHack 2023, 2,850 individuals from 105 different countries competed for 8 days to build the most innovative solutions for quantum computing applications using NVIDIA quantum technology. QHack is an educational conference and the world’s largest quantum machine learning (QML) hackathon.
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