Applications of Random Quantum Transformations in Data Science

Exponential quantum advantage in processing massive classical data by John Preskill https://lnkd.in/eUTvGHaX Abstract Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quantum computer of polylogarithmic size can perform large-scale classification and dimension reduction on massive classical data by processing samples on the fly, whereas any classical machine achieving the same prediction performance requires exponentially larger size. Furthermore, classical machines that are exponentially larger yet below the required size need superpolynomially more samples and time. We validate these quantum advantages in real-world applications, including single-cell RNA sequencing and movie review sentiment analysis, demonstrating four to six orders of magnitude reduction in size with fewer than 60 logical qubits. These quantum advantages are enabled by quantum oracle sketching, an algorithm for accessing the classical world in quantum superposition using only random classical data samples. Combined with classical shadows, our algorithm circumvents the data loading and readout bottleneck to construct succinct classical models from massive classical data, a task provably impossible for any classical machine that is not exponentially larger than the quantum machine. These quantum advantages persist even when classical machines are granted unlimited time or if BPP = BQP, and rely only on the correctness of quantum mechanics. Together, our results establish machine learning on classical data as a broad and natural domain of quantum advantage and a fundamental test of quantum mechanics at the complexity frontier.

Many of you will have seen the news about HSBC’s world-first application of quantum computing in algorithmic bond trading. Today, I’d like to highlight the technical paper that explains the research behind this milestone. In collaboration with IBM, our teams investigated how quantum feature maps can enhance statistical learning methods for predicting the likelihood that a trade is filled at a quoted price in the European corporate bond market. Using production-scale, real trading data, we ran quantum circuits on IBM quantum computers to generate transformed data representations. These were then used as inputs to established models including logistic regression, gradient boosting, random forest, and neural networks. The results: • Up to 34% improvement in predictive performance over classical baselines. • Demonstrated on real, production-scale trading data, not synthetic datasets. • Evidence that quantum-enhanced feature representations can capture complex market patterns beyond those typically learned by classical-only methods. This marks the first known application of quantum-enhanced statistical learning in algorithmic trading. For full technical details please see our published paper: 📄 Technical paper: https://lnkd.in/eKBqs3Y7 📰 Press release: https://lnkd.in/euMRbbJG Congratulations to Philip Intallura Ph.D , Joshua Freeland Freeland and all HSBC colleagues involved — and huge thanks to IBM for their partnership.

In a new research paper, "The role of data-induced randomness in quantum machine learning classification tasks," researchers examine the impact of data-induced randomness on quantum machine learning (QML) models for binary classification tasks. The research highlights the crucial role that data embedding plays in the performance of QML models and presents a new metric, class margin, to evaluate this performance. Key findings: * The study finds that successful classification tasks depend on the data-induced set of states exhibiting limited randomness. * For effective classification, the study finds that the class margin must concentrate below the classification boundary within a distance Ω(1/poly(n)). * Data embeddings that result in distributions of states resembling t-designs when measured with the classification observable can hinder successful classification. * Variational QML models, when executed without strong biases, tend to be inherently random. * Both model architecture and problem formulation significantly influence the randomness and generalization power of QML tasks. Class margin can be used as a diagnostic tool to evaluate the effectiveness of parameter-dependent embeddings in variational QML models. You can access the full research paper here: https://lnkd.in/dpg88bmE Amazing work (once more) by Berta Casas Font, Xavier Bonet-Monroig, and Adrián Pérez Salinas. #quantumcomputing #machinelearning #research #datarandomness