Adam Sorrenti

Andriy Miranskyy, Adam Sorrenti, Viral Thakar

The effectiveness of training neural networks directly impacts computational costs, resource allocation, and model development timelines in machine learning applications. An optimizer's ability to train the model adequately (in terms of trained model performance) depends on the model's initial weights. Model weight initialization schemes use pseudorandom number generators (PRNGs) as a source of randomness. We investigate whether substituting PRNGs for low-discrepancy quasirandom number generators (QRNGs) -- namely Sobol' sequences -- as a source of randomness for initializers can improve model performance. We examine Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer architectures trained on MNIST, CIFAR-10, and IMDB datasets using SGD and Adam optimizers. Our analysis uses ten initialization schemes: Glorot, He, Lecun (both Uniform and Normal); Orthogonal, Random Normal, Truncated Normal, and Random Uniform. Models with weights set using PRNG- and QRNG-based initializers are compared pairwise for each combination of dataset, architecture, optimizer, and initialization scheme. Our findings indicate that QRNG-based neural network initializers either reach a higher accuracy or achieve the same accuracy more quickly than PRNG-based initializers in 60% of the 120 experiments conducted. Thus, using QRNG-based initializers instead of PRNG-based initializers can speed up and improve model training.

Andriy Miranskyy, Adam Sorrenti, Jasmine Thind et al.

Zero-noise extrapolation (ZNE) mitigates errors in near-term quantum devices by extrapolating measurements obtained at amplified noise levels to estimate noise-free expectation values. In practice, commonly used extrapolation models are fitted without enforcing physical constraints, which can yield predictions outside the valid range of quantum observables. In this work, we introduce physically bounded variants of polynomial, exponential, and polynomial--exponential extrapolation models by explicitly parameterizing the zero-noise estimate and constraining it during optimization. We evaluate the approach using a large synthetic benchmark comprising 180,000 circuits and approximately 3.6 million ZNE experiments generated under realistic device noise models derived from IBM quantum backends. We also perform preliminary validation on real quantum hardware using GHZ and W-state circuits. Across the synthetic benchmark, bounded extrapolation substantially reduces unphysical predictions and improves the stability of exponential- and polynomial--exponential-family models, whereas polynomial models show little difference between bounded and unbounded variants. Hardware experiments show similar qualitative behaviour: bounded models generally avoid pathological extrapolations and often provide a more reliable balance between accuracy and usable coverage. At the same time, the results highlight practical limitations of current devices, including stronger-than-expected noise effects and variability not fully captured by simulation models. These results suggest that enforcing physical constraints during extrapolation improves the reliability of ZNE and that this approach can be incorporated into existing workflows with minimal modification.