We study numerically how a sound signal stored in a quantum computer can be recognized and restored with a minimal number of measurements in presence of random quantum gate errors. A method developed uses elements of MP3 sound compression and allows to recover human speech and sound of complex quantum wavefunctions.
Developing systems capable of compressing audio files is a very appealing research topic. This is due to the necessity to improve storage utilisation and accelerate data transfer over restricted communication channels. As a result, numerous investigators have explored and developed many methods to compress audio information with several approaches; everyone has negative consequences, such as excessive time consumption or complex computations, and remains a crucial problem. Recent improvements in deep learning have prompted researchers to use unified deep network models to investigate challenges needing highly organised data. The building and design of such models for compressing audio signals have been problematic due to the demand for discrete representations that are difficult to train. This research focuses on quantum neural networks for audio file compression. Our method uses an innovative encoding process that embeds audio signals in quantum states. Our framework can compress larger files than previously possible for the BBC sound dataset while achieving a better compression ratio than classical neural networks.
We discuss the quantum algorithms simulating complex dynamics on quantum computers in presence of imperfections. The effects of random errors and static imperfections for quantum gates are analyzed. We also study numerically how a sound signal stored in a quantum computer can be recognized and restored with a minimal number of measurements in presence of random quantum gate errors. A method developed uses elements of MP3 sound compression and allows to recover human speech and sound of complex quantum wavefunctions.
The rapid advancement of quantum hardware calls for the development of reliable methods to certify its correct functioning. However, existing certification tests often fall short: they either rely on flawless state preparation and measurement or lack soundness guarantees, meaning that they do not rule out incorrect implementations of the target operations by a quantum device. We introduce an approach, which we call quantum system quizzing, for the certification of quantum gates in a practical server-user scenario, where a classical user tests the results of quantum computation performed by a quantum server by checking its responses to a set of predesigned small-sized computational problems. Importantly, this approach does not require trusted state preparation and measurement and is thus inherently free from the associated systematic errors. For a wide range of relevant gate sets, including a universal one, we prove our certification protocol to be sound; i.e., it is guaranteed to reject any incorrect gate implementation, under the assumptions of a known Hilbert space dimension and context independence of error. A major technical challenge that we are first to resolve is recovering the tensor product structure of a multi-qubit system in the memory-bounded single-device setup. Finally, we prove the robustness of our protocol and validate its sample and computational efficiency through extensive numerical experiments. Our protocol is platform-agnostic and introduces a new paradigm for benchmarking and comparing diverse quantum architectures.