arXiv subjects

Priyam Srivastava

Publications and source records attributed to Priyam Srivastava.

6 recordsLinked to original sources

arXiv2026

Four Generations of Quantum Biomedical Sensors

Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence, extending precision with the coherence time up to the standard quantum limit, while third-generation architectures employ entanglement and spin squeezing to approach Heisenberg-limited precision. We define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By introducing a bandwidth-matching analysis pairing the neural signal hierarchy with platform response bandwidths, classifying deployed clinical devices by precision-scaling class and sensor-tissue proximity, and outlining a staged physical-milestone roadmap toward learning-integrated sensor networks, we identify key technological bottlenecks and chart the transition from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.

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arXiv2026

When AI meets quantum information: A comprehensive review

Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.

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arXiv2026

Variational Probe and Measurement Optimization for Structured Phase Estimation

We present a proof-of-principle study of variational quantum sensing for estimating a structured linear function of local phase parameters, in which each qubit in a spin-1/2 array accumulates a phase phi_i = alpha_i theta with known weights alpha and a global parameter theta. In a hardware-motivated regime of shallow circuits and shallow decoding measurements, we optimize the probe state with respect to the classical Fisher information (CFI) using the Covariance Matrix Adaptation Evolution Strategy. The variational ansatz is built from dipolar-interacting gates and global rotations on a polygon-centered qubit layout. To assess whether the standard Ramsey readout extracts all available information, we introduce a shallow global decoder and optimize it independently with the encoder frozen. For uniform (alpha_i = 1/N) and weighted-central (alpha_c = 1, alpha_i = 0.5) encodings with N = 2-8 qubits and depths L = 1-3, the optimized probes approach the respective entanglement-enhanced precision bounds, which reduce to the Heisenberg limit only for uniform encoding. The decoder provides systematic but modest CFI gains. For uniform encoding, these gains are smallest at the deepest circuits, confirming that fixed Ramsey readout is near-optimal for well-converged probes. For weighted encoding, a persistent component remains, reflecting the broken permutation symmetry of the generator under unequal weights. At large N, the weighted-encoding CFI also exhibits non-monotonic growth with system size, revealing an expressivity limit of the polygon-symmetric ansatz under asymmetric encoding.

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arXiv2026

Variational Joint Magnetometry and Gradiometry on Dipolar Spin Chains

Estimating a uniform magnetic field B0 and its spatial gradient g on a dipolar-coupled spin chain calls for a multiparameter figure of merit. The GHZ state, optimal for single-parameter Heisenberg-limited sensing, has a rank-one quantum Fisher information matrix with det(Q^GHZ) = 0 at every chain length N, ruling it out for the two-parameter problem. We present a variational framework that takes det(F) as the objective and a hardware-motivated layered dipolar circuit as the ansatz. Both encoding generators are diagonal in the computational basis, which reduces the search for the quantum Fisher information benchmark to a probability-simplex optimization and yields a tractable best-found benchmark det(Q*) against which variational performance is compared. The same diagonal structure makes the classical Fisher information depend only on basis-state probabilities under any single-qubit decoder, so encoder and decoder parameters are co-trained with CMA-ES in a single run. Decoder optimization past fixed Ramsey adds at most a few percentage points across the grid, in contrast to the persistent decoder gains seen in our prior single-parameter work. Variational probes at L = 3 reach 0.92 of the best-found benchmark at N = 5, a 4.2x SQL advantage in det(F), and concentrate on a four-string motif of the two GHZ extrema and two half-chain-flip strings whose structure follows from the Dicke-sector decomposition of the two generators.

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arXiv2026

Sequential vs. Simultaneous Entanglement Swapping under Optimal Link-Layer Control

Connection-less, packet-switched quantum network architectures distribute entanglement across multi-hop paths through sequential entanglement swapping, in which each node acts on purely local state information. The architectural advantages over the connection-oriented alternative -- simultaneous SWAP-ASAP -- are compelling, but sequential swapping holds partial chains in intermediate buffers between successive swaps, exposing them to memory decoherence in a way simultaneous SWAP-ASAP avoids by design. We present a proof-of-principle study at fixed chain length $n = 4$ in which each elementary link is governed by a fixed reinforcement-learning policy optimizing the secret-key rate of the six-state protocol, leaving the network-layer protocol as the sole independent variable. Sweeping the network-layer memory coherence time $T_c^{\mathrm{ext}}$ over four orders of magnitude reveals a clear regime structure governed by the dimensionless ratio $T_c^{\mathrm{ext}}/τ$, where $τ$ is the per-link entanglement heralding latency. Simultaneous SWAP-ASAP delivers a constant rate across the full sweep. Sequential swapping, by contrast, collapses to zero end-to-end deliveries below $T_c^{\mathrm{ext}}/τ= 25$, and begins recovering at $T_c^{\mathrm{ext}}/τ= 50$. It remains limited by the simultaneous rate, which it saturates only at the relaxed end of the sweep. These results suggest that the connection-less penalty is a near-term phenomenon tied to present-day memory coherence rather than a fundamental property of sequential swapping.

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arXiv2023

Encoder Circuit For Surface Code using Measurement-Based Quantum Computing Model

Surface codes are one of the most important topological stabilizer codes in the theory of quantum error correction. In this paper, we provide an efficient way to obtain surface codes through Measurement-based quantum computation (MBQC) using cluster state as the resource state. Simple twodimensional surface codes are studied and analyzed using stabilizer formalism. We also present an algorithm to computationally obtain the stabilizer of the surface codes, through which we later determine the distance of the codes. We note the difference in the stabilizers of the surface codes obtained by Fowler et al. wherein they used CNOT entangling operation to create the resource state as opposed to the cluster state which is formed using CZ entangling operation. We provide a theoretical calculation to understand this difference. The obtained surface codes can be used practically as an encoder circuit to encode one logical qubit.

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