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Found 5 papers, 2 papers with code
Improving Quantum Circuit Synthesis with Machine Learning
In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that minimize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs.
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics
This allows SlimFit to freeze up to 95% of layers and reduce the overall on-device GPU memory usage of transformer-based models such as ViT and BERT by an average of 2. 2x, across different NLP and CV benchmarks/datasets such as GLUE, SQuAD 2. 0, CIFAR-10, CIFAR-100 and ImageNet with an average degradation of 0. 2% in accuracy.
Constant-Depth Circuits for Dynamic Simulations of Materials on Quantum Computers
Current algorithms for Hamiltonian simulation, however, produce circuits that grow in depth with increasing simulation time, limiting feasible simulations to short-time dynamics.
Quantum Physics
QGo: Scalable Quantum Circuit Optimization Using Automated Synthesis
Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity.
Quantum Physics
QFAST: Quantum Synthesis Using a Hierarchical Continuous Circuit Space
We present QFAST, a quantum synthesis tool designed to produce short circuits and to scale well in practice.
Quantum Physics
