Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.29640

Entangled Futures: Quantum Computing's Potential and Challenges in Computer Science

Devki Nandan Jha·Jonte R. Hance·Tomasz Szydlo·Nick Chancellor·Omer F. Rana·Rajiv Ranjan
View original source ↗Read source document ↗Export metadata ↓

Abstract

Quantum computing represents a fundamental shift in computation, promising to solve problems currently intractable for classical computers. However, misconceptions persist; quantum computers are not universally faster, nor do they replace classical systems. Current Noisy Intermediate-Scale Quantum (NISQ) devices face significant challenges, including decoherence, high error rates, and limited scalability. Additionally, leveraging quantum systems requires rethinking traditional computing principles, adopting different programming models, and addressing the complexities of integrating quantum and classical workflows, making their practical use in computer science both demanding and non-trivial. This article provides a system-level perspective on quantum computing for computer scientists, software engineers, and IT professionals, examining its current capabilities, key architectural and software challenges, and potential paths toward quantum advantage in real-world applications within hybrid quantum-classical environments.

Explore related subjects

cs.ET

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Devki Nandan Jha, Jonte R. Hance, Tomasz Szydlo, Nick Chancellor, Omer F. Rana, Rajiv Ranjan. 2026-10-21. Entangled Futures: Quantum Computing's Potential and Challenges in Computer Science. https://doi.org/10.1109/mitp.2026.3739898

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

BERS: Locally Optimal Continuous Algorithm for Maritime Weather Routing with Just-in-Time Arrival

Maritime weather routing must optimize route geometry under dynamic wind-wave conditions, obstacle constraints, and fixed-arrival requirements. We present Bézier Evolve and Refine Strategy (BERS), a two-stage framework that combines global evolutionary search (CMA-ES) with local variational refinement (FMS). Routes are parametrized as Bézier curves and evaluated with dense along-path sampling, enabling smooth trajectories while preserving practical feasibility constraints and accounting for mid-segment effects. We evaluate BERS on synthetic benchmarks designed to stress seven operational criteria: continuity, obstacle avoidance, dynamic adaptation, flexible objective design, constant-load feasibility, just-in-time arrival, and local optimality. Across these tests, BERS matches or improves published baselines while maintaining robust convergence under challenging flow fields and land geometries. We then validate the method on real ocean data using hourly ERA5 forcing over 366 daily departures in 2024 for two trans-oceanic corridors (Atlantic and Pacific), with a physics-based model of an 88m cargo vessel with optional rigid wingsails. In real-ocean experiments, route optimization alone reduces mean propulsive energy by 23--59\% versus great-circle baselines of the same propulsion mode. Combined with wind-assisted propulsion, total savings reach up to 75\%. These results show that BERS provides a practical and scalable foundation for just-in-time, energy-efficient weather routing in maritime decarbonization workflows.

cs.ET↗
arXiv2026

Local learning with imperfect analog synapses

Analog in-memory computing delivers efficient neural network inference at the edge, but systems deployed in non-stationary environments may need to adapt in situ to locally acquired data. Backpropagation faces two structural obstacles here. Its global error transport lets stochastic write errors compound across layers, and its backward pass demands bidirectional, high-precision peripheral circuitry absent from inference-oriented crossbar arrays. We introduce Channel-wise Spatial Gradient learning (CwSG), a strictly local, forward-only rule that trains each convolutional layer by rewarding spatial variation in its feature maps. In simulations replaying the measured trajectories of 1,189 phase-change memory and 1,268 filamentary memristor devices, CwSG learns with single, unverified, sub-1 V pulses. It matches backpropagation on phase-change memory with 59-fold fewer pulses and outperforms it by 53 percentage points on highly variable filamentary memristors, with 8-bit forward converters and without transposed crossbar reads or network-wide activation storage. Analog training thus becomes a problem of credit assignment, in which algorithmic design rather than hardware compensation alone overcomes severe programming noise.

cs.ET↗
arXiv2026

Superposed analog memory for vector storage in neural networks

The growing memory demands of artificial neural networks pose a major challenge for energy-efficient computing, particularly for models that rely on large collections of high-dimensional weight and embedding vectors. Conventional memory architectures store these vectors individually, requiring many memory cells per vector and limiting achievable storage density. Here we demonstrate an analog memory architecture that encodes multiple vectors in a distributed superposition within a single memory array. Individual vectors are recovered through in-memory matrix-vector multiplication, allowing the degree of superposition to be tuned to balance storage density against recall fidelity. This approach introduces a new scaling dimension for analog memory, in which effective storage density can be increased without a corresponding increase in the number of physical memory cells. The distributed representation also provides resilience to device-level noise. We implement the architecture on a prototype phase-change memory chip and experimentally evaluate it using embedding tables from deep learning recommendation models and large language models. The results demonstrate inference accuracy comparable to full-precision software baselines, establishing analog superposition as a promising route towards dense, energy-efficient memory for artificial intelligence.

cs.ET↗