Searcharxiv⌕ Search

arXiv subjects

Lenny Putri Yulianti

Publications and source records attributed to Lenny Putri Yulianti.

2 recordsLinked to original sources

Information-theoretic limits on undetectable parameter-estimation attacks in continuous-variable quantum key distribution

We formalize side-channel detection in Gaussian-modulated continuous-variable quantum key distribution (CV-QKD) as a certificate-forgery hypothesis test and identify its detectability with an information-theoretic rate. Relative to a set $\mathcal{M}$ of trusted, real-time-monitored observables, the forgery- detectability rate $g_{\mathcal M}$ of a benign-statistics certificate is the missed-detection Stein exponent. We prove a dichotomy: $g_{\mathcal M}$ is monotone and strictly positive in the leaked Holevo information when the shot- noise unit is trusted, and identically zero otherwise, recovering local- oscillator and calibration attacks as the degenerate case. Monotonicity yields a certificate reusability rate bounding the Holevo leakage compatible with an undetected certificate over $N_{\mathrm{PE}}$ estimation rounds, via the non- asymptotic bound $\varepsilon_{\mathrm{PE}}\le\exp(-N_{\mathrm{PE}}ψ(r))$ whose near-threshold expansion matches the Gaussian confidence-interval scaling of standard finite-size analyses. We then give a composable finite-size key- length statement under collective Gaussian attacks, whose worst-case Holevo term is the reusability rate and whose parameter-estimation failure probability is bounded by the Stein exponent at every block length. Both results are unconditional in the trusted-correlation model, where strict monotonicity of the Holevo leakage in the excess noise follows from a noise-injection argument; for general $\mathcal{M}$ they hold under an explicit no-spurious-local-minima condition on the divergence landscape, verifiable by low-dimensional inspection. Convexity of the rate further assumes a numerically supported concavity of the Holevo bound, the sole numerical ingredient; proof status is delimited throughout.

quant-ph↗

Dissipative Quantum Multiplicative Weights with Sampling Feedback: A Classically Hard Primitive Realized via Engineered Open-System Dynamics

We introduce \emph{Dissipative Quantum Multiplicative Weights with Sampling Feedback} (DQMW-Sample), an online-learning primitive in which engineered open quantum-system dynamics prepare a Gibbs state whose computational-basis measurement supplies the loss feedback. The central conceptual contribution is to lift the computational hardness of constant-temperature Gibbs sampling into a physically realizable online-learning primitive. By engineering a Davies-type dissipator whose per-round feedback cannot be efficiently simulated classically, we obtain a learning-theoretic separation in which DQMW-Sample achieves asymptotically sublinear regret while every efficient classical learner suffers constant average regret on a suitably constructed instance. We further prove that the spectral gap of the engineered dissipator contracts hardware noise, yielding sublinear noise-induced regret under a balanced dissipation schedule, and we strengthen the single-round hardness to the full adaptive interaction: an efficient classical simulator of the entire $T$-round feedback process would collapse the polynomial hierarchy. We state the required realizability assumption in explicit form and report an initial hardware characterization on the IBM Heron~r2 processor. These results position DQMW-Sample as a concrete route toward computational advantage in online learning that is grounded in complexity theory and compatible with near-term superconducting hardware.

quant-ph↗