Papers
Live trends in quantum computing research, updated daily from arXiv.
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13,642 papers in 12 months (-16% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
In-situ mid-circuit qubit measurement and reset in a single-species trapped-ion quantum computing system
Yichao Yu, Keqin Yan, D. Biswas +5 more·Apr 17, 2025
We implement in-situ mid-circuit measurement and reset (MCMR) operations on a trapped-ion quantum computing system by using metastable qubit states in $^{171}\textrm{Yb}^+$ ions. We introduce and compare two methods for isolating data qubits from mea...
Machine Learning Decoding of Circuit-Level Noise for Bivariate Bicycle Codes
John Blue, Harshil Avlani, Zhiyang He +2 more·Apr 17, 2025
Fault-tolerant quantum computers will depend crucially on the performance of the classical decoding algorithm which takes in the results of measurements and outputs corrections to the errors inferred to have occurred. Machine learning models have sho...
A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks
Georgios Papadopoulos, Shaltiel Eloul, Yash Satsangi +4 more·Apr 17, 2025
The loss landscape of Variational Quantum Neural Networks (VQNNs) is characterized by local minima that grow exponentially with increasing qubits. Because of this, it is more challenging to recover information from model gradients during training com...
Noninvasive mid-circuit measurement and reset on atomic qubits
Zuowei Chen, Isabella Goetting, G. Toh +8 more·Apr 17, 2025
Mid-circuit measurement and reset of subsets of qubits is a crucial ingredient of quantum error correction and many quantum information applications. Measurement of atomic qubits is accomplished through resonant fluorescence, which typically disturbs...
Dead Gate Elimination
Yanbin Chen, C. Mendl, Helmut Seidl·Apr 17, 2025
Hybrid quantum algorithms combine the strengths of quantum and classical computing. Many quantum algorithms, such as the variational quantum eigensolver (VQE), leverage this synergy. However, quantum circuits are executed in full, even when only subs...
Quantum algorithm for solving nonlinear differential equations based on physics-informed effective Hamiltonians
Hsin‐Yu Wu, Annie E. Paine, E. Philip +2 more·Apr 17, 2025
We propose a distinct approach to solving linear and nonlinear differential equations (DEs) on quantum computers by encoding the problem into ground states of effective Hamiltonian operators. Our algorithm relies on constructing such operators in the...
Transferring linearly fixed QAOA angles: performance and real device results
R. Sakai, Hiromichi Matsuyama, Wai-Hong Tam +1 more·Apr 17, 2025
Quantum Approximate Optimization Algorithm (QAOA) enables solving combinatorial optimization problems on quantum computers by optimizing variational parameters for quantum circuits. We investigate a simplified approach that combines linear parameteri...
Enhancing NDAR with Delay-Gate-Induced Amplitude Damping
Wai-Hong Tam, Hiromichi Matsuyama, R. Sakai +1 more·Apr 17, 2025
The Noise-Directed Adaptive Remapping (NDAR) method utilizes amplitude damping noise to enhance the performance of quantum optimization algorithms. NDAR alternates between exploration by sampling solutions from the quantum circuit and exploitation by...
Featuremetric benchmarking: Quantum computer benchmarks based on circuit features
Timothy Proctor, A. Tran, Xingxin Liu +4 more·Apr 17, 2025
Benchmarks that concisely summarize the performance of many-qubit quantum computers are essential for measuring progress towards the goal of useful quantum computation. In this work, we present a benchmarking framework that is based on quantifying ho...
Energy landscape plummeting in variational quantum eigensolver: Subspace optimization, non-iterative corrections, and generator-informed initialization for improved quantum efficiency.
Chayan Patra, Rahul Maitra·Apr 17, 2025
Variational Quantum Eigensolver (VQE) faces significant challenges due to hardware noise and the presence of barren plateaus and local traps in the optimization landscape. To mitigate the detrimental effects of these issues, we introduce a general fo...
Practical Application of the Quantum Carleman Lattice Boltzmann Method in Industrial CFD Simulations
Francesco Turro, Alessandra Lignarolo, Daniele Dragoni·Apr 17, 2025
Computational Fluid Dynamics simulations are crucial in industrial applications but require extensive computational resources, particularly for extreme turbulent regimes. While classical digital approaches remain the standard, quantum computing promi...
A trace distance-based geometric analysis of the stabilizer polytope for few-qubit systems
Alberto B. P. Junior, Santiago Zamora, Rafael A. Macêdo +5 more·Apr 16, 2025
Non-stabilizerness is a fundamental resource for quantum computational advantage, differentiating classically simulable circuits from those capable of universal quantum computation. Recently, non-stabilizerness has been shown to be relevant for a few...
Layered KIK quantum error mitigation for dynamic circuits
Ben Bar, Jader P. Santos, Raam Uzdin·Apr 16, 2025
Quantum Error Mitigation is essential for enhancing the reliability of quantum computing experiments. The adaptive KIK error mitigation method has demonstrated significant advantages, including resilience to temporal noise drifts, applicability to no...
Valley Splitting Correlations Across a Silicon Quantum Well Containing Germanium
Jonathan C. Marcks, Emily Eagen, Emma C. Brann +12 more·Apr 16, 2025
Quantum dots in SiGe/Si/SiGe heterostructures host coherent electron spin qubits, which are promising for future quantum computers. The silicon quantum well hosts near-degenerate electron valley states, creating a low-lying excited state that is know...
Standardization of Multi-Objective QUBOs
Loong Kuan Lee, Thore Gerlach, Nico Piatkowski·Apr 16, 2025
Multi-objective optimization involving Quadratic Unconstrained Binary Optimization (QUBO) problems arises in various domains. A fundamental challenge in this context is the effective balancing of multiple objectives, each potentially operating on ver...
Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning
Tobias Fellner, David Kreplin, Samuel Tovey +1 more·Apr 16, 2025
Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectively than classical approaches. However, their practical advantage over...
Quantum Cramer-Rao Precision Limit of Noisy Continuous Sensing
Dayou Yang, Moulik Ketkar, Koenraad Audenaert +2 more·Apr 16, 2025
Quantum sensors hold considerable promise for precision measurement, yet their capabilities are inherently constrained by environmental noise. A fundamental task in quantum sensing is determining the precision limit of noisy sensor devices. For conti...
Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networks
Nayoung Lee, Minsoo Shin, Asel Sagingalieva +5 more·Apr 16, 2025
Accurate prediction and stabilization of blast furnace temperatures are crucial for optimizing the efficiency and productivity of steel production. Traditional methods often struggle with the complex and non-linear nature of the temperature fluctuati...
Learning transitions in classical Ising models and deformed toric codes
Malte Pütz, Samuel J. Garratt, Hidetoshi Nishimori +2 more·Apr 16, 2025
Conditional probability distributions describe the effect of learning an initially unknown classical state through Bayesian inference. Here we demonstrate the existence of a \textit{learning transition}, having signatures in the long distance behavio...
Universal work extraction in quantum thermodynamics
Kaito Watanabe, Ryuji Takagi·Apr 16, 2025
Evaluating the maximum amount of work extractable from a nanoscale quantum system is one of the central problems in quantum thermodynamics. Previous works identified the free energy of the input state as the optimal rate of extractable work under the...