• Corpus ID: 248377753

QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning

@inproceedings{Wang2022QOCQO,
  title={QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning},
  author={Hanrui Wang and Zi-Chen Li and Jiaqi Gu and Yongshan Ding and David Z. Pan and Song Han},
  year={2022}
}
Parameterized Quantum Circuits (PQC) are drawing increasing re-search interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC learning, the training process needs to be offloaded to real quantum machines instead of using exponential-cost classical simulators. One common approach to obtain PQC gradients is parameter shift whose cost scales linearly with the number of qubits. We present QOC, the… 

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References

SHOWING 1-10 OF 22 REFERENCES
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
TLDR
Extensively evaluated with 12 quantum machine learning (QML) and variational quantum eigensolver (VQE) benchmarks on 14 quantum computers, QuantumNAS significantly outperforms noise-unaware search, human, random, and existing noise-adaptive qubit mapping baselines.
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
TLDR
QuantumNAT, a PQC-specific framework to perform noise-aware optimizations in both training and inference stages to improve robustness against noise is presented and post-measurement quantization is introduced to quantize the measurement outcomes to discrete values, achieving the denoising effect.
Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow (Invited Paper)
TLDR
This paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise, and can optimize QNN models for different errors in qubits.
Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs: (Invited Paper)
TLDR
This paper makes the very first attempt to mix quantum neuron designs to build quantum neural architectures, and demonstrates that the identified quantum neural architecture with mixed quantum neurons can achieve 90.62% of accuracy on the MNIST dataset.
Quantum Computing in the NISQ era and beyond
TLDR
Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future, and the 100-qubit quantum computer will not change the world right away - but it should be regarded as a significant step toward the more powerful quantum technologies of the future.
Quantum circuit learning
TLDR
A classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which is hybridizing a low-depth quantum circuit and a classical computer for machinelearning, paves the way toward applications of near- term quantum devices for quantum machine learning.
Quantum embeddings for machine learning
TLDR
This work proposes to train the first part of the circuit with the objective of maximally separating data classes in Hilbert space, a strategy it calls quantum metric learning, which provides a powerful analytic framework for quantum machine learning.
Realistic Fault Models and Fault Simulation for Quantum Dot Quantum Circuits
TLDR
Based on fault simulation of a full adder QC, a small test set of six test patterns is selected to detect all faults with 99% confidence level, and the fault simulation saves a lot of memory and CPU time.
Characterizing Quantum Gates via Randomized Benchmarking
TLDR
This work describes and expands upon the scalable randomized benchmarking protocol proposed in Phys.
Supervised learning with quantum-enhanced feature spaces
TLDR
Two classification algorithms that use the quantum state space to produce feature maps are demonstrated on a superconducting processor, enabling the solution of problems when the feature space is large and the kernel functions are computationally expensive to estimate.
...
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