Implementing quantum dimensionality reduction for non-Markovian stochastic simulation.

Complex systems are embedded in our everyday experience. Stochastic modelling enables us to understand and predict the behaviour of such systems, cementing its utility across the quantitative sciences. Accurate models of highly non-Markovian processes - where the future behaviour depends on events that happened far in the past - must track copious amounts of information about past observations, requiring high-dimensional memories. Quantum technologies can ameliorate this cost, allowing models of the same processes with lower memory dimension than corresponding classical models. Here we implement such memory-efficient quantum models for a family of non-Markovian processes using a photonic setup. We show that with a single qubit of memory our implemented quantum models can attain higher precision than possible with any classical model of the same memory dimension. This heralds a key step towards applying quantum technologies in complex systems modelling.

FcMBL magnetic bead-based MALDI-TOF MS rapidly identifies paediatric blood stream infections from positive blood cultures.

Rapid identification of potentially life-threatening blood stream infections (BSI) improves clinical outcomes, yet conventional blood culture (BC) identification methods require ~24-72 hours of liquid culture, plus 24-48 hours to generate single colonies on solid media suitable for identification by mass spectrometry (MS). Newer rapid centrifugation techniques, such as the Bruker MBT-Sepsityper® IVD, replace culturing on solid media and expedite the diagnosis of BCs but frequently demonstrate reduced sensitivity for identifying clinically significant Gram-positive bacterial or fungal infections. This study introduces a protocol that utilises the broad-range binding properties of an engineered version of mannose-binding lectin linked to the Fc portion of immunoglobulin (FcMBL) to capture and enrich pathogens combined with matrix-assisted laser desorption-ionisation time-of-flight (MALDI-TOF) MS for enhanced infection identification in BCs. The FcMBL method identified 94.1% (64 of 68) of clinical BCs processed, with a high sensitivity for both Gram-negative and Gram-positive bacteria (94.7 and 93.2%, respectively). The FcMBL method identified more patient positive BCs than the Sepsityper® (25 of 25 vs 17 of 25), notably with 100% (3/3) sensitivity for clinical candidemia, compared to only 33% (1/3) for the Sepsityper®. Additionally, during inoculation experiments, the FcMBL method demonstrated a greater sensitivity, identifying 100% (24/24) of candida to genus level and 9/24 (37.5%) top species level compared to 70.8% (17/24) to genus and 6/24 to species (25%) using the Sepsityper®. This study demonstrates that capture and enrichment of samples using magnetic FcMBL-conjugated beads is superior to rapid centrifugation methods for identification of BCs by MALDI-TOF MS. Deploying the FcMBL method therefore offers potential clinical benefits in sensitivity and reduced turnaround times for BC diagnosis compared to the standard Sepsityper® kit, especially for fungal diagnosis.

Measures of distinguishability between stochastic processes.

Quantifying how distinguishable two stochastic processes are is at the heart of many fields, such as machine learning and quantitative finance. While several measures have been proposed for this task, none have universal applicability and ease of use. In this article, we suggest a set of requirements for a well-behaved measure of process distinguishability. Moreover, we propose a family of measures, called divergence rates, that satisfy all of these requirements. Focusing on a particular member of this family-the coemission divergence rate-we show that it can be computed efficiently, behaves qualitatively similar to other commonly used measures in their regimes of applicability, and remains well behaved in scenarios where other measures break down.

Extreme Dimensionality Reduction with Quantum Modeling.

Effective and efficient forecasting relies on identification of the relevant information contained in past observations-the predictive features-and isolating it from the rest. When the future of a process bears a strong dependence on its behavior far into the past, there are many such features to store, necessitating complex models with extensive memories. Here, we highlight a family of stochastic processes whose minimal classical models must devote unboundedly many bits to tracking the past. For this family, we identify quantum models of equal accuracy that can store all relevant information within a single two-dimensional quantum system (qubit). This represents the ultimate limit of quantum compression and highlights an immense practical advantage of quantum technologies for the forecasting and simulation of complex systems.