TorchQuantum is highlighted in UnitaryHack.
TorchQuantum is used in winning team for ACM Quantum Computing for Drug Discovery.
TorchQuantum received UnitaryFund.
QuantumNAS is covered by MIT News.
TorchQuantum is integrated to IBM Qiskit Ecosystem.
Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research efforts have explored a higher level of optimization by making the quantum circuits themselves resilient to noise. We propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing QML and quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search and parameter training by introducing a novel SuperCircuit. The SuperCircuit is constructed with multiple layers of pre-defined parameterized gates and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. Then we perform an evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates. Extensively evaluated with 12 QML and VQE benchmarks on 10 quantum computers, QuantumNAS significantly outperforms baselines. For QML, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real QC. It also achieves the lowest eigenvalue for VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD.
Basic idea of QuantumNAS: decoupling the training and search. We only pay the training cost of SuperCircuit for once but can use it to evaluate many candidates.
QuantumNAS models achieve higher robustness and accuracy than human baseline and noise-unaware search models:
@inproceedings{wang2022quantumnas, title={Quantumnas: Noise-adaptive search for robust quantum circuits}, author={Wang, Hanrui and Ding, Yongshan and Gu, Jiaqi and Lin, Yujun and Pan, David Z and Chong, Frederic T and Han, Song}, booktitle={2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)}, pages={692--708}, year={2022}, organization={IEEE}}
We thank National Science Foundation, MIT-IBM Watson AI Lab, and Qualcomm Innovation Fellowship for supporting this research. This work is funded in part by EPiQC, an NSF Expedition in Computing, under grants CCF-1730082/1730449; in part by STAQ under grant NSF Phy-1818914; in part by DOE grants DE-SC0020289 and DE-SC0020331; and in part by NSF OMA-2016136 and the Q-NEXT DOE NQI Center. We acknowledge the use of IBM Quantum services for this work.
