arXiv subjects

Paul Griffin

Publications and source records attributed to Paul Griffin.

18 recordsLinked to original sources

arXiv2026

Quantum Counterparty Credit Risk: A Study of Path-Dependent Derivatives

Estimating potential future exposure (PFE) for path-dependent derivatives, such as FX Target Redemption Forwards (TARFs), represents a formidable computational challenge due to the demand of nested Monte Carlo simulations. We present a hybrid quantum-classical framework that leverages Iterative Quantum Amplitude Estimation (IQAE) to address this via a reduced-order counterparty credit risk model. Our methodology maps the non-linear TARF payoff -- including cumulative gains and knock-out features -- into a quantum circuit via a two-step formulation, whereby a first-step percentile is computed classically and then used to condition quantum evaluation of subsequent exposure. We employ discretisation of the FX process and a linearised additive approximation of dynamics to enable implementation on current quantum platforms. Developed via the Classiq platform and validated on NVIDIA CUDA-Q and Amazon Braket SV1, our approach achieves relative errors of 1%-8% against classical benchmarks at the 97.5% and 99% confidence levels. While discretisation constraints and approximate monotonicity assumption may introduce bias and limit recovery of the full exposure distribution, our framework offers a tractable testbed for quantum acceleration. Scaling analysis suggests that $\sim$300 logical qubits could enable full 52-week exposure estimation, reducing sample complexity for tail-risk estimation via amplitude estimation at the cost of increased circuit depth.

quant-ph↗

arXiv2026

Quantum-Inspired Trace-Augmented Evidence Selection for Reasoning over Structured Hypothesis Spaces

Large language models (LLMs) now solve a wide range of expert-level exams at or above human level, yet remain brittle on specialised, evidence-intensive domains such as law. On these tasks, errors arise not only from gaps in world knowledge but also from subtle distinctions between pieces of evidence and inconsistent use of supporting evidence. The most common aggregator over sampled chain-of-thought (CoT) traces, majority vote, returns the most popular answer regardless of whether its evidence is actually strongest. We propose to treat the selection of CoT reasoning fragments into a set of evidence as an explicit combinatorial optimisation problem, allowing well-supported but minority hypotheses to override noisy majorities, and to evaluate the approach on legal-reasoning benchmarks that are particularly sensitive to evidence quality. We introduce EP-HUBO (Evidence Pool Higher-Order Binary Optimisation), which generates multiple CoT traces with a small local model, parses fragments into per-hypothesis evidence pools, solves a higher-order unconstrained binary optimisation per pool with quality-derived weights (relevance, specificity, distinctiveness), and delegates a single adjudication call per question to a frontier model. We evaluate EP-HUBO on two evidence-intensive legal benchmarks using both simulated annealing on classical hardware and the Dirac-3 photonic entropy-quantum machine from Quantum Computing Inc. HUBO-style optimisation gives a principled way to aggregate reasoning fragments while preserving minority-but-correct hypotheses, and is most valuable in low-contamination domains where frontier models have not already absorbed the benchmark material.

cs.AI↗

arXiv2026

Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions

Recovery rate prediction plays a pivotal role in bond investment strategies by enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supporting effective credit risk management. However, accurate forecasting remains challenging due to complex nonlinear dependencies, high-dimensional feature spaces, and limited sample sizes-conditions under which classical machine learning models are prone to overfitting. We propose a hybrid Quantum Machine Learning (QML) model with Amplitude Encoding, leveraging the unitarity constraint of Parametrized Quantum Circuits (PQC) and the exponential data compression capability of qubits. We evaluate the model on a global recovery rate dataset comprising 1,725 observations and 256 features from 1996 to 2023. Our hybrid method significantly outperforms both classical neural networks and QML models using Angle Encoding, achieving a lower Root Mean Squared Error (RMSE) of 0.228, compared to 0.246 and 0.242, respectively. It also performs competitively with ensemble tree methods such as XGBoost. While practical implementation challenges remain for Noisy Intermediate-Scale Quantum (NISQ) hardware, our quantum simulation and preliminary results on noisy simulators demonstrate the promise of hybrid quantum-classical architectures in enhancing the accuracy and robustness of recovery rate forecasting. These findings illustrate the potential of quantum machine learning in shaping the future of credit risk prediction.

q-fin.CP↗

arXiv2026

A Bayesian Multi-State Data Integration Approach for Estimating County-level Prevalence of Opioid Misuse in the United States

Drug overdose deaths, including from opioids, remain a significant public health threat to the United States (US). To abate the harms of opioid misuse, understanding its prevalence at the local level is crucial for stakeholders in communities to develop response strategies that effectively use limited resources. Although there exist several state-specific studies that provide county-level prevalence estimates, such estimates are not widely available across the country, as the datasets used in these studies are not always readily available in other states, which, therefore, has limited the wider applications of existing models. To fill this gap, we propose a Bayesian multi-state data integration approach that fully utilizes publicly available data sources to estimate county-level opioid misuse prevalence for all counties in the US. The hierarchical structure jointly models opioid misuse prevalence and overdose death outcomes, leverages existing county-level prevalence estimates in limited states and state-level estimates from national surveys, and accounts for heterogeneity across counties and states with counties' covariates and mixed effects. Furthermore, our parsimonious and generalizable modeling framework employs horseshoe+ prior to flexibly shrink coefficients and prevent overfitting, ensuring adaptability as new county-level prevalence data in additional states become available. Using real-world data, our model shows high estimation accuracy through cross-validation and provides nationwide county-level estimates of opioid misuse for the first time.

stat.AP↗

arXiv2023

VIOLET: Visual Analytics for Explainable Quantum Neural Networks

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET, a novel visual analytics approach to improve the explainability of quantum neural networks. Guided by the design requirements distilled from the interviews with domain experts and the literature survey, we developed three visualization views: the Encoder View unveils the process of converting classical input data into quantum states, the Ansatz View reveals the temporal evolution of quantum states in the training process, and the Feature View displays the features a QNN has learned after the training process. Two novel visual designs, i.e., satellite chart and augmented heatmap, are proposed to visually explain the variational parameters and quantum circuit measurements respectively. We evaluate VIOLET through two case studies and in-depth interviews with 12 domain experts. The results demonstrate the effectiveness and usability of VIOLET in helping QNN users and developers intuitively understand and explore quantum neural networks

cs.HC↗

arXiv2023

QuantumEyes: Towards Better Interpretability of Quantum Circuits

Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization Dandelion Chart to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated QuantumEyes as well as the novel QuantumEyes integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.

quant-ph↗

arXiv2023

Quantum Machine Learning for Credit Scoring

In this paper we explore the use of quantum machine learning (QML) applied to credit scoring for small and medium-sized enterprises (SME). A quantum/classical hybrid approach has been used with several models, activation functions, epochs and other parameters. Results are shown from the best model, using two quantum classifiers and a classical neural network, applied to data for companies in Singapore. We observe significantly more efficient training for the quantum models over the classical models with the quantum model trained for 350 epochs compared to 3500 epochs for comparable prediction performance. Surprisingly, a degradation in the accuracy was observed as the number of qubits was increased beyond 12 qubits and also with the addition of extra classifier blocks in the quantum model. Practical issues for executing on simulators and real quantum computers are also explored. Overall, we see great promise in this first in-depth exploration of the use of hybrid QML in credit scoring.

quant-ph↗

arXiv2023

A cold-atom Ramsey clock with a low volume physics package

We demonstrate a Ramsey-type microwave clock interrogating the 6.835~GHz ground-state transition in cold \textsuperscript{87}Rb atoms loaded from a grating magneto-optical trap (GMOT) enclosed in an additively manufactured loop-gap resonator microwave cavity. A short-term stability of $1.5 \times10^{-11} $~$τ^{-1/2}$ is demonstrated, in reasonable agreement with predictions from the signal-to-noise ratio of the measured Ramsey fringes. The cavity-grating package has a volume of $\approx$67~cm\textsuperscript{3}, ensuring an inherently compact system while the use of a GMOT drastically simplifies the optical requirements for laser cooled atoms. This work is another step towards the realisation of highly compact portable cold-atom frequency standards.

physics.atom-ph↗

arXiv2023

An additive-manufactured microwave cavity for a compact cold-atom clock

We present an additive-manufactured microwave cavity for a Ramsey-type, double resonance, compact cold-atom clock. Atoms can be laser cooled inside the cavity using a grating magneto-optic trap (GMOT) with the cavity providing an excellent TE011-like mode while maintaining sufficient optical access for atomic detection. The cavity features a low Q-factor of 360 which conveniently reduces the cavity-pulling of the future clock. Despite the potential porosity of the additive-manufacturing process, we demonstrate that the cavity is well-suited for vacuum. A preliminary clock setup using cold atoms allows for measuring the Zeeman spectrum and Rabi oscillations in the cavity which enables us to infer excellent field uniformity and homogeneity respectively, across the volume accessed by the cold atoms. Ramsey spectroscopy is demonstrated, indicating the cavity is suitable for clock applications. Finally, we discuss the limitations of the future clock.

physics.atom-ph↗

arXiv2022

Identifying and Analyzing Sepsis States: A Retrospective Study on Patients with Sepsis in ICUs

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials and therapeutic strategies for sepsis.

q-bio.QM↗

arXiv2022

CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors

Structural causal models (SCMs) provide a principled approach to identifying causation from observational and experimental data in disciplines ranging from economics to medicine. However, SCMs, which is typically represented as graphical models, cannot rely only on data, rather require support of domain knowledge. A key challenge in this context is the absence of a methodological framework for encoding priors (background knowledge) into causal models in a systematic manner. We propose an abstraction called causal knowledge hierarchy (CKH) for encoding priors into causal models. Our approach is based on the foundation of "levels of evidence" in medicine, with a focus on confidence in causal information. Using CKH, we present a methodological framework for encoding causal priors from various information sources and combining them to derive an SCM. We evaluate our approach on a simulated dataset and demonstrate overall performance compared to the ground truth causal model with sensitivity analysis.

cs.AI↗

arXiv2022

A Digital Alkali Spin Maser

Self-oscillating atomic magnetometers, in which the precession of atomic spins in a magnetic field is driven by resonant modulation, offer high sensitivity and dynamic range. Phase-coherent feedback from the detected signal to the applied modulation creates a resonant spin maser system, highly responsive to changes in the background magnetic field. Here we show a system in which the phase condition for resonant precession is met by digital signal processing integrated into the maser feedback loop. This system uses a modest chip-scale laser and mass-produced dual-pass caesium vapour cell and operates in a 50 microtesla field, making it a suitable technology for portable measurements of the geophysical magnetic field. We demonstrate a Cramer-Rao lower bound-limited resolution of 50 fT at 1 s sampling cadence, and a sensor bandwidth of 10 kHz. This device also represents an important class of atomic system in which low-latency digital processing forms an integral part of a coherently-driven quantum system.

physics.atom-ph↗

arXiv2021

Emergency Equity: Access and Emergency Medical Services in San Francisco

In 2020, California required San Francisco to consider equity in access to resources such as housing, transportation, and emergency services as it re-opened its economy post-pandemic. Using a public dataset maintained by the San Francisco Fire Department of every call received related to emergency response from January 2003 to April 2021, we calculated the response times and distances to the closest of 48 fire stations and 14 local emergency rooms. We used logistic regression to determine the probability of meeting the averages of response time, distance from a fire station, and distance to an emergency room based on the median income bracket of a ZIP code based on IRS statement of income data. ZIP codes in the lowest bracket ($25,000-$50,000 annually) consistently had the lowest probability of meeting average response metrics. This was most notable for distances to emergency rooms, where calls from ZIP codes in the lowest income bracket had an 11.5% chance of being within the city's average distance (1 mile) of an emergency room, while the next lowest probability (for the income bracket of $100,000-$200,000 annually) was 75.9%. As San Francisco considers equity as a part of California's "Blueprint for a Safer Economy," it should evaluate the distribution of access to emergency services. Keywords: fire department, emergency medical services, emergency rooms, equity, logistic regression

stat.AP↗

arXiv2021

Self-Organization in Cold Atoms Mediated by Diffractive Coupling

This article discusses self-organization in cold atoms via light-mediated interactions induced by feedback from a single retro-reflecting mirror. Diffractive dephasing between the pump beam and the spontaneous sidebands selects the lattice period. Spontaneous breaking of the rotational and translational symmetry occur in the 2D plane transverse to the pump. We elucidate how diffractive ripples couple sites on the self-induced atomic lattice. The nonlinear phase shift of the atomic cloud imprinted onto the optical beam is the parameter determining coupling strength. The interaction can be tailored to operate either on external degrees of freedom leading to atomic crystallization for thermal atoms and supersolids for a quantum degenerate gas, or on internal degrees of freedom like populations of the excited state or Zeeman sublevels. Using the light polarization degrees of freedom on the Poincar{é} sphere (helicity and polarization direction), specific irreducible tensor components of the atomic Zeeman states can be coupled leading to spontaneous magnetic ordering of states of dipolar and quadrupolar nature. The requirements for critical interaction strength are compared for the different situations. Connections and extensions to longitudinally pumped cavities, counterpropagating beam schemes and the CARL instability are discussed.

nlin.PS↗

arXiv2020

Spontaneous atomic crystallization via diffractive dephasing in optical cavities

The design of an experiment on the spontaneous crystallization of a laser-cooled, but thermal atomic cloud into a hexagonally structured phase is discussed. Atomic interaction is mediated by the dipole potential of an optical lattice formed spontaneously in a multi-mode degenerate cavity from single-mode longitudinal pumping. The length scale of the structure is given by the diffractive dephasing between the spontaneous sidebands and the on-axis pump. A linear stability indicates that the transition can be observed in a cavity of moderate finesse compatible with having the cavity mirrors outside the vacuum cell. A new anti-reflected cell has been assembled for this purpose.

quant-ph↗

arXiv2020

Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identification of causal effects from observational data in the cases where the causal graph is identifiable, i.e., the data generating mechanism can be recovered from the joint distribution. However, no such studies have been done to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effect from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and important research question, i.e., the effect of oxygen therapy intervention in the intensive care unit (ICU); the result of this project is useful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC III database, a standard database in the machine learning community that contains 58,976 admissions from an ICU in Boston, MA, for estimating the oxygen therapy effect on morality. We also identified the covariate-specific effect to oxygen therapy from the model for more personalized intervention.

cs.LG↗

arXiv2020

Collaborative Cloud Computing Framework for Health Data with Open Source Technologies

The proliferation of sensor technologies and advancements in data collection methods have enabled the accumulation of very large amounts of data. Increasingly, these datasets are considered for scientific research. However, the design of the system architecture to achieve high performance in terms of parallelization, query processing time, aggregation of heterogeneous data types (e.g., time series, images, structured data, among others), and difficulty in reproducing scientific research remain a major challenge. This is specifically true for health sciences research, where the systems must be i) easy to use with the flexibility to manipulate data at the most granular level, ii) agnostic of programming language kernel, iii) scalable, and iv) compliant with the HIPAA privacy law. In this paper, we review the existing literature for such big data systems for scientific research in health sciences and identify the gaps of the current system landscape. We propose a novel architecture for software-hardware-data ecosystem using open source technologies such as Apache Hadoop, Kubernetes and JupyterHub in a distributed environment. We also evaluate the system using a large clinical data set of 69M patients.

cs.DC↗

arXiv2020

A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model

Identifying causal relationships for a treatment intervention is a fundamental problem in health sciences. Randomized controlled trials (RCTs) are considered the gold standard for identifying causal relationships. However, recent advancements in the theory of causal inference based on the foundations of structural causal models (SCMs) have allowed the identification of causal relationships from observational data, under certain assumptions. Survival analysis provides standard measures, such as the hazard ratio, to quantify the effects of an intervention. While hazard ratios are widely used in clinical and epidemiological studies for RCTs, a principled approach does not exist to compute hazard ratios for observational studies with SCMs. In this work, we review existing approaches to compute hazard ratios as well as their causal interpretation, if it exists. We also propose a novel approach to compute hazard ratios from observational studies using backdoor adjustment through SCMs and do-calculus. Finally, we evaluate the approach using experimental data for Ewing's sarcoma.

stat.ME↗