arXiv subjects

Mohsin Raza

Publications and source records attributed to Mohsin Raza.

12 recordsLinked to original sources

arXiv2026

Benchmarking quantum simulation at scale

The applications for which quantum computers will clearly outperform classical computers are still being identified and benchmarking such an advantage is challenging. We propose a scalable verification scheme for non-equilibrium quantum simulation based on stabilizer scars, a special class of quantum many-body scars, whose structure ensures both classical simulability and efficient direct fidelity estimation. Assuming a physically motivated error model, we show that the fidelity of quantum simulating these states bounds the fidelity of classically intractable simulations, providing a benchmark for quantum-advantage experiments in non-equilibrium dynamics.

quant-ph↗

arXiv2026

Robust Negativity in the Quantum-to-Classical Transition of Kerr Dynamics

We quantify the quantum-to-classical transition of the single-mode Kerr nonlinear dynamics in the presence of loss. We establish three time scales that govern the dynamics, each with distinct characteristics. For times short compared to the Ehrenfest time, the evolution is classical, characterized by Gaussian dynamics. For sufficiently long times, as we increase the initial photon number, unitary Kerr evolution would generate macroscopic superpositions of coherent states (so-called kitten states), but this is severely restricted in the presence of small photon loss so that expectation values of observables coincide with their classical values. The intermediate time scale, however, shows resilient quantum behavior in the macroscopic limit. We show that in the mean-field non-Gaussian regime, the Kerr Hamiltonian (with small photon loss) generates a significant amount of Wigner-negativity, and classical flow is recovered only if the loss rate grows with system size. Our results broaden the usual understanding of quantum-to-classical transitions and demonstrate the potential for creating robust nonclassical resources for continuous-variable quantum information processing in the presence of loss.

quant-ph↗

arXiv2026

Rashba Spin Demons in Two-Dimensional d-Wave Altermagnets and their Electrostatic Contro

Spin demons in d-wave altermagnets are acoustic collective excitations produced by nearly out-ofphase motion of spin-split quasiparticle populations. We identify a new mode, Rashba spin demon mode, in 2D d-wave altermagnets in the presence of Rashba Spin-Orbit Coupling (RSOC). The pristine spin demon mode in altermagnets, without RSOC, is a charge-dark excitation which is invisible to charge-sensitive probes. We show that Rashba spin demon mode is a spin-dominated acoustic mode that is distinct from the pristine mode and has a finite charge density spectral weight. RSOC rotates the quasiparticle spin texture in momentum space, generating mixed charge-spin coherence factors that give the longitudinal spin pole a charge residue. We also show that a nearby metallic gate provides a complementary electrostatic knob by screening the Coulomb interaction and tuning the pole dispersion and linewidth. Within a charge-spin random-phase approximation, we identify regimes where the Rashba mode is underdamped, charge visible, and still predominantly longitudinal-spin-like. Our results establish RSOC and electrostatic screening as complementary controls for tuning the spin and charge character of altermagnetic spin demons and making them accessible to charge probes without destroying their spin-dominated character.

cond-mat.mes-hall↗

arXiv2026

Topological Edge States from Molecular Chirality: A General Framework for Dimerized Dipolar Arrays

We establish a general theoretical framework for realizing topological edge states in dimerized arrays of chiral dipolar molecules and demonstrate that molecular handedness provides a natural and tunable route to SSH-like topology in an interacting one-dimensional setting. Starting from an effective spin-$\tfrac{1}{2}$ model generated by Stark-dressed chiral molecules, we introduce bond dimerization and show that the chirality-induced Dzyaloshinskii--Moriya interaction amplifies the effective hopping amplitudes and enlarges the bulk topological gap relative to an achiral chain of equivalent dipole strength. Using self-consistent mean-field theory with periodic- and open-boundary calculations, we map out the trivial, critical, and topological regimes through bulk spectra, complex-plane winding, and boundary-localized probability densities. A central result is that the two in-gap boundary modes carry \emph{opposite molecular chirality}: the left edge state localizes on a left-handed molecule and the right edge state on a right-handed molecule, a stereochemical labeling with no analogue in conventional SSH implementations. The two-leg ladder extension supports a richer four-band bulk structure and a rung-split edge sector whose robustness is characterized by a continuous sweep of the interchain coupling. All results are expressed in dimensionless units of the reference hopping scale $t_0$, making the framework directly applicable to any dipolar molecular platform -- from bialkali polar molecules at MHz coupling scales to future arrays of ultracold chiral polyatomic species. These findings establish dimerized chiral molecular arrays as a controllable and chirality-addressable platform for quasi-one-dimensional topological quantum matter.

quant-ph↗

arXiv2026

Efficient simulation of noisy IQP circuits with amplitude-damping noise

Efficient classical simulation of noisy intermediate-scale quantum (NISQ) circuits has been a topic of intense study over the past few years. The majority of results on efficient simulation assume that the circuits undergo some variant of unital noise or involve sufficient randomness. However, there are limited results for circuits undergoing non-unital noise in the absence of randomness. In this work, we present a polynomial-time classical algorithm to sample from the output distributions of amplitude-damped instantaneous quantum polynomial (IQP) circuits. Our algorithm works for circuits generated by arbitrary $l$-local diagonal gates with depth $d = Ω(\log(n))$, undergoing constant amplitude-damping noise.

quant-ph↗

arXiv2025

Single layer tiny Co$^4$ outpaces GPT-2 and GPT-BERT

We show that a tiny Co$^4$ machine(Adeel,2025) with a single layer, two heads, and 8M parameters, operating at an approximate cost of $O(N)$ (where $N$ is the number of input tokens), outpaces the BabyLM Challenge baselines GPT-2 (124M, 12 layers, $O(N^2))$ and GPT-BERT (30M, 12 layers, $O(N^2))$ in just two epochs, while both are trained for ten. Co$^4$ achieves orders-of-magnitude greater training efficiency on 10M tokens, demonstrating highly sample efficient pretraining. Using the BabyLM challenge evaluation pipeline across complex benchmarks, Co$^4$ exhibits strong zero-shot and fine-tuning performance on SuperGLUE tasks. Specifically, Co$^4$ outperforms GPT-2 on 5 out of 7 zero-shot metrics and 6 out of 7 fine-tuning tasks, and GPT-BERT on 4 out of 7 metrics in both cases. These results suggest the need to rethink prevailing deep learning paradigms and associated scaling laws.

cs.CL↗

arXiv2025

Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing

Deep learning (DL) has big-data processing capabilities that are as good, or even better, than those of humans in many real-world domains, but at the cost of high energy requirements that may be unsustainable in some applications and of errors, that, though infrequent, can be large. We hypothesise that a fundamental weakness of DL lies in its intrinsic dependence on integrate-and-fire point neurons that maximise information transmission irrespective of whether it is relevant in the current context or not. This leads to unnecessary neural firing and to the feedforward transmission of conflicting messages, which makes learning difficult and processing energy inefficient. Here we show how to circumvent these limitations by mimicking the capabilities of context-sensitive neocortical neurons that receive input from diverse sources as a context to amplify and attenuate the transmission of relevant and irrelevant information, respectively. We demonstrate that a deep network composed of such local processors seeks to maximise agreement between the active neurons, thus restricting the transmission of conflicting information to higher levels and reducing the neural activity required to process large amounts of heterogeneous real-world data. As shown to be far more effective and efficient than current forms of DL, this two-point neuron study offers a possible step-change in transforming the cellular foundations of deep network architectures.

cs.NE↗

arXiv2025

Cooperation Is All You Need

Going beyond 'dendritic democracy', we introduce a 'democracy of local processors', termed Cooperator. Here we compare their capabilities when used in permutation invariant neural networks for reinforcement learning (RL), with machine learning algorithms based on Transformers, such as ChatGPT. Transformers are based on the long standing conception of integrate-and-fire 'point' neurons, whereas Cooperator is inspired by recent neurobiological breakthroughs suggesting that the cellular foundations of mental life depend on context-sensitive pyramidal neurons in the neocortex which have two functionally distinct points. Weshow that when used for RL, an algorithm based on Cooperator learns far quicker than that based on Transformer, even while having the same number of parameters.

cs.LG↗

arXiv2024

Multimodal Speech Enhancement Using Burst Propagation

This paper proposes the MBURST, a novel multimodal solution for audio-visual speech enhancements that consider the most recent neurological discoveries regarding pyramidal cells of the prefrontal cortex and other brain regions. The so-called burst propagation implements several criteria to address the credit assignment problem in a more biologically plausible manner: steering the sign and magnitude of plasticity through feedback, multiplexing the feedback and feedforward information across layers through different weight connections, approximating feedback and feedforward connections, and linearizing the feedback signals. MBURST benefits from such capabilities to learn correlations between the noisy signal and the visual stimuli, thus attributing meaning to the speech by amplifying relevant information and suppressing noise. Experiments conducted over a Grid Corpus and CHiME3-based dataset show that MBURST can reproduce similar mask reconstructions to the multimodal backpropagation-based baseline while demonstrating outstanding energy efficiency management, reducing the neuron firing rates to values up to \textbf{$70\%$} lower. Such a feature implies more sustainable implementations, suitable and desirable for hearing aids or any other similar embedded systems.

cs.SD↗

arXiv2024

An Overlooked Role of Context-Sensitive Dendrites

To date, most dendritic studies have predominantly focused on the apical zone of pyramidal two-point neurons (TPNs) receiving only feedback (FB) connections from higher perceptual layers and using them for learning. Recent cellular neurophysiology and computational neuroscience studies suggests that the apical input (context), coming from feedback and lateral connections, is multifaceted and far more diverse, with greater implications for ongoing learning and processing in the brain than previously realized. In addition to the FB, the apical tuft receives signals from neighboring cells of the same network as proximal (P) context, other parts of the brain as distal (D) context, and overall coherent information across the network as universal (U) context. The integrated context (C) amplifies and suppresses the transmission of coherent and conflicting feedforward (FF) signals, respectively. Specifically, we show that complex context-sensitive (CS)-TPNs flexibly integrate C moment-by-moment with the FF somatic current at the soma such that the somatic current is amplified when both feedforward (FF) and C are coherent; otherwise, it is attenuated. This generates the event only when the FF and C currents are coherent, which is then translated into a singlet or a burst based on the FB information. Spiking simulation results show that this flexible integration of somatic and contextual currents enables the propagation of more coherent signals (bursts), making learning faster with fewer neurons. Similar behavior is observed when this functioning is used in conventional artificial networks, where orders of magnitude fewer neurons are required to process vast amounts of heterogeneous real-world audio-visual (AV) data trained using backpropagation (BP). The computational findings presented here demonstrate the universality of CS-TPNs, suggesting a dendritic narrative that was previously overlooked.

q-bio.NC↗

arXiv2020

Impact of independent reservoirs on the quantum Zeno and anti-Zeno effects

In this paper, we look into what happens to a quantum system under repeated measurements if it interacts with two independent reservoirs. In particular, we look at the behavior of a two-level system interacting with reservoirs consisting of harmonic oscillators. The interaction with one reservoir is weak with a dissipative-type coupling, while the interaction with the other reservoir is strong with a dephasing-type coupling. Using a polaron transformation, we show that the presence of the strongly coupled reservoir can actually reduce the decay rate of the quantum system due to the effect of the weakly-coupled reservoir.

quant-ph↗

arXiv2020

The Zeno and anti-Zeno effects: studying modified decay rates for spin-boson models with both strong and weak system-environment couplings

In this paper, we look into what happens to a quantum system under repeated measurements if system evolution is removed before each measurement is performed. Beginning with investigating a single two-level system coupled to two independent baths of harmonic oscillators, we move to replacing it with a large collection of such systems, thereby invoking the large spin-boson model. Whereas each of our two-level systems interacts strongly with one of the aforementioned baths, it interacts weakly with the other. A polaron transformation is used to make it possible for the problem in the strong coupling regime to be treated with perturbation theory. We find that the case involving a single two-level system exhibits qualitative and quantitative differences from the case involving a collection of them; however, the general effects of strong and weak couplings turn out to be the same as those in the presence of system evolution, something which allows us to establish that system evolution has no practical bearing on any of these effects.

quant-ph↗