arXiv subjects

Sagar Dubey

Publications and source records attributed to Sagar Dubey.

3 recordsLinked to original sources

arXiv2026

Quantum Entanglement Halves the Oblivious Update Bandwidth

We consider $(n,k)$ MDS-coded distributed storage over $\mathbb{F}_q$ with per-node storage $\alpha$ symbols. For the oblivious update problem, where a single message symbol changes and neither helpers nor the stale node know which, the classical lower bound is $\alpha k \log_2 q$ bits. We prove that when the $k$ contacted helpers share prior quantum entanglement, the update bandwidth is $\lceil \alpha/2 \rceil \cdot k \log_2 q$ bits-equivalent, a factor approaching 2 reduction. For $\alpha = 2$, a $[[k, k-2]]_q$ CSS code achieves bandwidth $k \log_2 q$ with one qudit per helper. For general $\alpha$, a $[[\lceil \alpha/2 \rceil k, \lceil \alpha/2 \rceil k - \alpha]]_q$ CSS code achieves the bound with $\lceil \alpha/2 \rceil$ qudits per helper. The matching converse uses the superdense coding bound: the stale node holds all transmitted qudits and hence the entangled partners, so each helper's channel supports at most $D^2$ distinguishable signals for dimension $D$. The result holds for all $(n,k)$ pairs with sufficiently large prime $q$.

quant-ph↗

arXiv2026

No chaos required: traversable wormhole signals survive 98% coupling deletion

The traversable wormhole protocol in coupled Sachdev-Ye-Kitaev (SYK) systems produces a transmission signal C(t) widely interpreted as evidence of holographic dynamics. Recent work has questioned this interpretation, showing that similar signals arise in generic thermalizing systems. We address what the signal actually probes by systematically destroying quantum chaos in the SYK model via random coupling deletion, while monitoring the transmission signal across the chaos-to-integrable transition. Using exact diagonalization of the doubled SYK model at N=10 with 50 disorder realizations per sparsity, supplemented by Krylov-subspace extensions to N=20, we find that the ensemble-averaged peak height varies by less than 1.1% across a 50-fold sparsification range, even as the underlying spectrum transitions from Gaussian-unitary-ensemble to sub-Poisson statistics. A 1,200-instance sweep over the inter-system coupling mu confirms that the signal is controlled by mu alone, with no dependence on internal chaos. We further verify that the thermofield double state retains its thermal structure under sparsification despite substantial changes to the state vector, providing a structural explanation for the invariance. These results indicate that the transmission signal diagnoses inter-system coupling fidelity rather than holographic dynamics, and that future quantum-simulation experiments require independent chaos diagnostics to substantiate gravitational claims. As a practical consequence, the invariance implies that 98% of the Hamiltonian's coupling terms can be discarded (with variance rescaling of the survivors), reducing the gate count per Trotter step by approximately 50x at N=10 and bringing larger traversable-wormhole simulations within experimental reach.

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arXiv2026

The Feedback Hamiltonian is the Score Function: A Diffusion-Model Framework for Quantum Trajectory Reversal

In continuously monitored quantum systems, the feedback protocol of Garc\'ia-Pintos, Liu, and Gorshkov reshapes the arrow of time: a Hamiltonian $H_{\mathrm{meas}} = r A / \tau$ applied with gain $X$ tilts the distribution of measurement trajectories, with $X < -2$ producing statistically time-reversed outcomes. Why this specific Hamiltonian achieves reversal, and how the mechanism relates to score-based diffusion models in machine learning, has remained unexplained. We compute the functional derivative of the log path probability of the quantum trajectory distribution directly in density-matrix space. Combining Girsanov's theorem applied to the measurement record, Fr\'echet differentiation on the Banach space of trace-class operators, and K\"ahler geometry on the pure-state projective manifold, we prove that $\delta \log P_F / \delta \rho = r A / \tau = H_{\mathrm{meas}}$. The Garc\'ia-Pintos feedback Hamiltonian is the score function of the quantum trajectory distribution -- exactly the object Anderson's reverse-time diffusion theorem requires for trajectory reversal. The identification extends to multi-qubit systems with independent measurement channels, where the score is a sum of local operators. Two consequences follow. First, the feedback gain $X$ generates a continuous one-parameter family of path measures (for feedback-active Hamiltonians with $[H, A] \neq 0$), with $X = -2$ recovering the backward process in leading-order linearization -- a structure absent from classical diffusion, where reversal is binary. Second, the score identification enables machine learning (ML) score estimation methods -- denoising score matching, sliced score matching -- to replace the analytic formula when its idealizations (unit efficiency, zero delay, Gaussian noise) fail in real experiments.

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