arXiv subjects

Hyeonjun Yeo

Publications and source records attributed to Hyeonjun Yeo.

10 recordsLinked to original sources

arXiv2026

NEST: Stale-Robust Tokenized-Stock Measurement for a Crypto-Treasury LETF Stack

Tokenized equities trade while listed markets are closed, so their marks could improve off-hours measurement along layered exposures that link Bitcoin, crypto-treasury stocks, and leveraged ETFs. A token return and the next opening gap, however, share the price move from the previous close to the token mark, so part of their apparent fit is mechanical. We develop NEST, a stale-robust protocol that separates this shared-history fit from conditional measurement value. A shared-history identity and a variance-clock bound show how much fit timing alone can generate. The partial determination index (PDI), the squared partial correlation between token and gap given public controls, measures what remains, and a finite-sample frontier expresses the partial-F decision in PDI units. Product, provenance, timestamp, and execution audits decide whether an informative mark is admissible. For MSTRx, a tracker certificate on MicroStrategy (MSTR), a token-only $R^2$ of 0.916 shrinks to a modest increment once Bitcoin, mark age, and a pre-09:00 MSTR premarket proxy are controlled: the token explains 15.7% of the remaining gap variation (1% frontier 0.031), and 14.0% to 17.0% under stricter clock variants, including one in which every token mark precedes 09:00. A trade-level reconstruction on Gate gives 25.2%; NVDAX and AAPLX clear their frontiers, whereas TSLAX does not. An admitted mark updates, rather than replaces, BTC-unit exposure diagnostics for the BTC-MSTR-LETF chain.

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arXiv2026

Pricing the Unprovable: Ambiguity, Turnstile Runs, and the Verifiability Discount in Crypto Protocols

Privacy-preserving protocols can repair a vulnerability without establishing whether it was exploited beforehand, even when no theft is reported and aggregate supply is protected. We call the potential value reduction from this gap the verifiability discount. A turnstile model shows how early withdrawal can shift a possible shortfall to remaining holders and why credible evidence can become more valuable as exposure grows. We introduce VDISC, a reproducible framework that records reported loss, exposed value, public evidence, returns, and pool flows separately. It combines Orchard's staged disclosures with reconstructed on-chain flows, earlier Zcash cases, and a 40-event security panel. Orchard follows the predicted direction: market responses were muted after repair and supply assurance, while the largest negative return and aggregate shielded-pool net outflow aligned with the acknowledgment that past abuse could not be ruled out. These patterns are directional, not causal. Across events, market-cap-normalized reported loss and unresolved exposure overlap among the 38 events with a public loss figure (Pearson 0.86, Spearman 0.56), partly because exposure is often coded from the loss itself, so returns alone cannot distinguish reported loss from unresolved history. The findings motivate three candidate design priorities: produce privacy-compatible evidence about past states, limit the state exposed by a single flaw, and disclose residual uncertainty explicitly.

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arXiv2026

Neural Estimation of Irreversibility in Real and Simulated Limit Order Book Paths

A path indistinguishable from its own reverse carries no arrow of time, so time irreversibility is a necessary condition for a directed mechanism to be readable from trajectories alone. In finance it has been measured only on scalar series such as returns and volatility. We define the irreversibility rate of a coarse-grained limit order book path as the forward-to-reverse log-density ratio per event, and estimate it from three weeks of Nasdaq market-by-order data with an order-invariant MLP and a temporal convolutional network that receive identical blocks. For NVDA, the order-invariant and order-sensitive estimates are 0.036 and 0.054 nats per event, respectively. Their difference, the ordering increment, is 0.018. A surrogate preserving the coarse one-step transition table recovers 30 percent less irreversibility than the observed paths. The increment is positive for all twelve symbols on every held-out daytime date, and for NVDA in each session window. It changes more than tenfold with the coarse-graining scale. Neither a linear score nor any single feature group reproduces the full fitted rate. A queue-reactive simulator calibrated on the same days matches the market's ordered rate, 0.052 against 0.054, yet its ordering increment is 0.0006 against 0.018, building temporal asymmetry out of block composition instead of block order. We propose the ordering and scale profile of path irreversibility as a benchmark for order book simulators. Code and audit materials are available at https://github.com/hjyeo30/lob-irreversibility.

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arXiv2026

Entanglement Structure Across $\mathbb{Z}_n$ Phase Transitions in 1D Rydberg Atom Arrays

Multipartite quantum entanglement plays a crucial role in the emergence of different quantum phases and their transitions in quantum many-body systems. It is of general interest to know what sort of analysis on quantum entanglement can bring us a profound insight to understand the rich dynamics of quantum many-body systems. In this work we study the characteristics of quantum entanglement in relation to $\mathbb{Z}_n$-ordered phases emerging under a varied strength of 1-dim Rydberg interaction. We propose an approach based on the structure of pair-wise entanglement across the Rydberg chain using two-qubit concurrence as an entanglement measure. We define an entanglement-structure factor via Fourier analysis of total concurrence at each site and address $\mathbb{Z}_n$ phase transitions in comparison with the conventional order-parameter based on local density, i.e. magnetization. We also discuss how the required two-qubit concurrence can be measured in analog Rydberg atom arrays using site-selective erasure and parametrized laser pulses. Our investigation suggests that an entanglement-structure-based approach can provide a powerful tool in analyzing symmetry-breaking in quantum phase transitions.

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arXiv2026

Rodeo Filtering for Direct Steady-State Estimation in Open Quantum Systems

Computing non-equilibrium steady states of open quantum systems is a challenging task on conventional computers, motivating quantum algorithms for direct steady-state estimation. A natural route is to regard the steady state as the zero mode of the Liouvillian and to isolate this sector spectrally. We formulate this task as a known-zero-sector projection problem and implement the corresponding filter using the Rodeo algorithm, which performs stochastic spectral filtering through repeated controlled evolutions and measurement-conditioned filtering steps. In the steady-state setting, the filter can be centered directly at the known zero eigenvalue, avoiding the spectral search required in generic eigenstate preparation. Compared with a phase-estimation-based implementation of the same projection, the Rodeo approach enables restart on failure and reduces the target-error dependence of the filtering cost and controlled-evolution depth from power-law to logarithmic. This advantage becomes more pronounced as the spectral separation of the Hermitian Liouvillian embedding increases, allowing Rodeo filtering to outperform phase-estimation filtering already at modest controlled-evolution depths. Our results identify Rodeo filtering as a resource-efficient primitive for estimating steady-state observables in open quantum systems.

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arXiv2025

Reducing Circuit Depth in Quantum State Preparation for Quantum Simulation Using Measurements and Feedforward

Reducing circuit depth and identifying an optimal trade-off between circuit depth and width is crucial for successful quantum computation. In this context, midcircuit measurement and feedforward have been shown to significantly reduce the depth of quantum circuits, particularly in implementing logical gates. By leveraging these techniques, we propose several parallelization strategies that reduce quantum circuit depth at the expense of increasing width in preparing various quantum states relevant to quantum simulation. With measurements and feedforward, we demonstrate that utilizing unary encoding as a bridge between two quantum states substantially reduces the circuit depth required for preparing quantum states, such as sparse quantum states and sums of Slater determinants within the first quantization framework, while maintaining an efficient circuit width. Additionally, we show that a Bethe wave function, characterized by its high degree of freedom in its phase, can be probabilistically prepared in a constant-depth quantum circuit using measurements and feedforward. We anticipate that our study will contribute to the reduction of circuit depth in initial state preparation, particularly for quantum simulation, which is a critical step toward achieving quantum advantage.

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arXiv2025

Designing Minimalistic Variational Quantum Ansatz Inspired by Algorithmic Cooling

This study introduces a novel minimalistic variational quantum ansatz inspired by algorithmic cooling principles. The proposed Heat Exchange algorithmic cooling ansatz (HE ansatz) facilitates efficient population redistribution without requiring bath resets, simplifying implementation on noisy intermediate-scale quantum (NISQ) devices. The HE ansatz achieves superior approximation ratios with the complete network \textsc{Maxcut} optimization problem compared to the conventional Hardware efficient and QAOA ansatz. We also proposed a new variational algorithm that utilize HE ansatz to compute the ground state of impure dissipative-system variational quantum eigensolver (dVQE) which achieved a sub-$1\%$ error in ground-state energy calculations of the 1D Heisenberg chain with impurity and successfully simulates the edge effect of impure spin chain, highlighting its potential for applications in quantum many-body physics. These results underscore the compatibility of the ansatz with hardware-efficient implementations, offering a scalable approach for solving complex quantum problems in disordered and open quantum systems.

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arXiv2025

Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures

The estimation of quantum entropies and distance measures, such as von Neumann entropy, Rényi entropy, Tsallis entropy, trace distance, and fidelity-induced distances such as the Bures distance, has been a key area of research in quantum information science. In our study, we introduce the disentangling quantum neural network (DEQNN), designed to efficiently estimate various physical quantities in quantum information. Estimation algorithms for these quantities are generally tied to the size of the Hilbert space of the quantum state to be estimated. Our proposed DEQNN offers a unified dimensionality reduction methodology that can significantly reduce the size of the Hilbert space while preserving the values of diverse physical quantities. We provide an in-depth discussion of the physical scenarios and limitations in which our algorithm is applicable, as well as the learnability of the proposed quantum neural network.

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arXiv2024

Approximating maximum independent set on Rydberg atom arrays using local detunings

Rydberg atom arrays operated by a quantum adiabatic principle are among the most promising quantum simulating platforms due to their scalability and long coherence time. From the perspective of combinatorial optimization, they offer an efficient solution for an intrinsic maximum independent set problem because of the resemblance between the Rydberg Hamiltonian and the cost function of the maximum independent set problem. In this study, a strategy is suggested to approximate maximum independent sets by adjusting local detunings on the Rydberg Hamiltonian according to each vertex's vertex support, which is a quantity that represents connectivity between vertices. By doing so, the strategy successfully reduces the error rate three times for the checkerboard graphs with defects when the adiabaticity is sufficient. In addition, the strategy decreases the error rate for random graphs even when the adiabaticity is relatively insufficient. Moreover, it is shown that the strategy helps to prepare a quantum many-body ground state by raising the fidelity between the evolved quantum state and a 2D cat state on a square lattice. Finally, the strategy is combined with the non-abelian adiabatic mixing and this approach is highly successful in finding maximum independent sets compared to the conventional adiabatic evolution with local detunings.

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arXiv2023

Weighted $p$-Rényi Entropy Power Inequality: Information Theory to Quantum Shannon Theory

We study the $p$-Rényi entropy power inequality with a weight factor $t$ on two independent continuous random variables $X$ and $Y$. The extension essentially relies on a modulation on the sharp Young's inequality due to Bobkov and Marsiglietti. Our research provides a key result that can be used as a fundamental research finding in quantum Shannon theory, as it offers a Rényi version of the entropy power inequality for quantum systems.

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