Quantum Hardware
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Related Articles from SNS
Nanomagnets control diamond qubits, pointing to more scalable quantum hardware
Nanomagnets control diamond qubits, pointing to more scalable quantum hardware Gaby Clark Scientific Editor Robert Egan Associate Editor Quantum computing, once only a theoretical possibility, promises to deliver faster, more energy-efficient computers—but only if scientists can build and scale the hardware needed to run the machines. New research from Virginia Commonwealth University brings scientists one small step closer to quantum computing at a practical scale, which could help...
Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes. In this work, we introduce a training framework that reduces this cost to logarithmic in the number of qubits, making...
Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems
arXiv:2505.13356v2 Announce Type: replace Abstract: Quantum computing has emerged as a promising computational paradigm to address unresolved challenges in the modeling and control of modern power systems. However, most existing studies focus on offline simulations, and a practical framework for validating quantum algorithms in real-time operational environments remains lacking.
Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems
arXiv:2606.07666v1 Announce Type: cross Abstract: Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates. Existing compilation and error-detection toolchains treat these concerns in isolation, with no principled way to balance detection overhead against success probability under latency constraints....
Zero-shot Quantum Neural Architecture Search
arXiv:2605.27410v2 Announce Type: replace-cross Abstract: Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and classical optimization to achieve advantage. Despite their promise, the practical deployment of VQAs is challenged by the difficulty of designing quantum circuit architectures that balance expressivity, trainability, and hardware constraints. Existing evolutionary-based quantum neural...
Benchmarking Quantum Algorithmic Resilience for CVaR Portfolio Optimization: The Expressibility-Coherence Trade-off
Announce Type: cross Abstract: Quantum combinatorial optimization offers theoretical advantages for complex financial modeling, but physical implementation on Noisy Intermediate Scale Quantum (NISQ) devices is severely constrained by hardware topology. This study presents a hardware benchmarking analysis between a Hardware Efficient Variational Quantum Neural Network (HE-VQNN) and the Warm Start Quantum Approximate Optimization Algorithm (WS-QAOA) for a hybrid Mean Variance and Conditional...
Adaptive directional gradients for parameterised quantum circuits
Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale. In this work, we propose a framework of forward gradient estimators for PQCs, based on the forward mode of automatic differentiation, that yields an unbiased estimator of the gradient...
Predictive surrogates could cut quantum computing measurement overhead by more than 99.97%
June 6, 2026 feature Predictive surrogates could cut quantum computing measurement overhead by more than 99.97% Ingrid Fadelli Author Sadie Harley Scientific Editor Robert Egan Associate Editor Quantum computers, systems that process information leveraging quantum mechanical effects, have the potential of outperforming classical computers on some tasks. Despite their potential, the use of these systems remains very limited, due to their high cost and other challenges that have so far...
Branch-Aware Quantum Constant Propagation for Dynamic Quantum Circuits
arXiv:2606.02018v1 Announce Type: cross Abstract: Compile-time optimization is important for improving the efficiency and reliability of quantum circuits on current noisy hardware. While many existing methods simplify circuits using structural patterns or quantum-state information, most of them target only unitary circuits and do not support dynamic circuits with mid-circuit measurements and classical feedforward. In this work, we present Branch-Aware Quantum Constant Propagation (BQCP), a...
Shallow Electronic State Preparation for Quantum Chemistry with Quantum Monte Carlo Pre-Selection
arXiv:2605.31139v1 Announce Type: cross Abstract: Quantum computers hold great promise for molecular simulation, but noise remains a fundamental obstacle. We introduce a Quantum Monte Carlo (QMC) pre-screening procedure that constructs compact, physically motivated Givens rotation ans\"atze tailored to realistic quantum hardware. By identifying the most important wavefunction contributions early in a QMC simulation, we build circuits that are shallower that conventional alternatives while...