Public articles linked to the same research event.
arXiv This work extends EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification by evolving parameterized quantum circuits as intermediate processing modules while retaining classical feature-extraction and prediction layers, achieving 98.42%, 90.62%, and 85.47% accuracy on MNIST, Fashion-MNIST, and CIFAR-10 with gate counts comparable to other quantum architecture-search methods, reaching 85.68% on CIFAR-10 with over 25x fewer trainable parameters, and finding that rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10.
This work extends EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification by evolving parameterized quantum circuits as intermediate processing modules while retaining classical feature-extraction and prediction layers, achieving 98.42%, 90.62%, and 85.47% accuracy on MNIST, Fashion-MNIST, and CIFAR-10 with gate counts comparable to other quantum architecture-search methods, reaching 85.68% on CIFAR-10 with over 25x fewer trainable parameters, and finding that rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10.
This work extends EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification by evolving parameterized quantum circuits as intermediate processing modules while retaining classical feature-extraction and prediction layers, achieving 98.42%, 90.62%, and 85.47% accuracy on MNIST, Fashion-MNIST, and CIFAR-10 with gate counts comparable to other quantum architecture-search methods, reaching 85.68% on CIFAR-10 with over 25x fewer trainable parameters, and finding that rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10.
This work extends EXAQC, an evolutionary framework for automated quantum circuit discovery, to image classification by evolving parameterized quantum circuits as intermediate processing modules while retaining classical feature-extraction and prediction layers, achieving 98.42%, 90.62%, and 85.47% accuracy on MNIST, Fashion-MNIST, and CIFAR-10 with gate counts comparable to other quantum architecture-search methods, reaching 85.68% on CIFAR-10 with over 25x fewer trainable parameters, and finding that rotation-based encodings (RX, RY, U3) outperform amplitude encoding by 22-25 points on CIFAR-10.