Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Enhancing quantum field theory simulations on NISQ devices with Hamiltonian truncation
J. Ingoldby, M. Spannowsky, Timur Sypchenko +1 more·Jul 26, 2024
Quantum computers can efficiently simulate highly entangled quantum systems, offering a solution to challenges facing classical simulation of quantum field theories (QFTs). This paper presents an alternative to traditional methods for simulating the ...
Polarization-encoded photonic quantum-to-quantum Bernoulli factory based on a quantum dot source
G. Rodari, Francesco Hoch, Alessia Suprano +6 more·Jul 26, 2024
A Bernoulli factory is a randomness manipulation routine that takes as input a Bernoulli random variable, outputting another Bernoulli variable whose bias is a function of the input bias. Recently proposed quantum-to-quantum Bernoulli factory schemes...
Exploring quantum active learning for materials design and discovery.
M. P. Lourenço, Hadi Zadeh-Haghighi, Jiří Hostaš +4 more·Jul 26, 2024
The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classica...
Methods to achieve near-millisecond energy relaxation and dephasing times for a superconducting transmon qubit
Mikko Tuokkola, Y. Sunada, Heidi Kivijärvi +6 more·Jul 26, 2024
Superconducting qubits are one of the most promising physical systems for implementing quantum computers. However, executing quantum algorithms of practical computational advantage requires further improvements in the fidelities of qubit operations, ...
Stabilization of cat-state manifolds using nonlinear reservoir engineering
Ivan Rojkov, Matteo Simoni, Elias Zapusek +2 more·Jul 25, 2024
We introduce a novel reservoir engineering approach for stabilizing multi-component Schrödinger's cat manifolds. The fundamental principle of the method lies in the destructive interference at crossings of gain and loss Hamiltonian terms in the coupl...
Implementing multi-controlled X gates using the quantum Fourier transform
V. Arsoski·Jul 25, 2024
Quantum computing has the potential to solve many complex algorithms in the domains of optimization, arithmetics, structural search, financial risk analysis, machine learning, image processing, and others. Quantum circuits built to implement these al...
ArtA: automating Design Space Exploration of spin-qubit architectures
Nikiforos Paraskevopoulos, David Hamel, Aritra Sarkar +2 more·Jul 25, 2024
In the fast-paced field of quantum computing, identifying the architectural characteristics that will enable quantum processors to achieve high performance across a diverse range of quantum algorithms continues to pose a significant challenge. Given ...
The signaling dimension of two-dimensional and polytopic systems
Shuriku Kai, Michele Dall'Arno·Jul 25, 2024
The signaling dimension of any given physical system represents its classical simulation cost, that is, the minimum dimension of a classical system capable of reproducing all the input/output correlations of the given system. The signaling dimension ...
Quantum-inspired evolutionary algorithms for feature subset selection: a comprehensive survey
Yelleti Vivek, V. Ravi, P. R. Krishna·Jul 25, 2024
The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits to adopt a probabilistic repre...
Computational Phase Transitions in Two-Dimensional Antiferromagnetic Melting
Zack Weinstein, Jalal Ahmad, D. Podolsky +1 more·Jul 25, 2024
A computational phase transition in a classical or quantum system is a non-analytic change in behavior of an order parameter which can only be observed with the assistance of a nontrivial classical computation. Such phase transitions, and the computa...
Few-Shot, Robust Calibration of Single Qubit Gates Using Bayesian Robust Phase Estimation
Travis Hurant, Ke-jia Sun, Zhubing Jia +2 more·Jul 25, 2024
Accurate calibration of control parameters in quan-tum gates is crucial for high-fidelity operations, yet it represents a significant time and resource challenge, necessitating periods of downtime for quantum computers. Robust Phase Estimation (RPE) ...
Phase transitions in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo><mml:mi mathvariant="normal">D</mml:mi></mml:mrow></mml:math> subsystem-symmetric monitored quantum circuits
Cole Kelson-Packer, A. Miyake·Jul 25, 2024
The interplay of unitary evolution and projective measurements is a modern interest in the study of many-body entanglement. On the one hand, the competition between these two processes leads to the recently discovered measurement-induced phase transi...
Quantum landscape tomography for efficient single-gate optimization on quantum computers
Matan Ben-Dov, I. Arad, Emanuele G. Dalla Torre·Jul 25, 2024
Circuit optimization is a fundamental task for practical applications of near-term quantum computers. In this work we address this challenge through the powerful lenses of tensor network theory. Our approach involves the full characterization of the ...
Discretized Quantum Exhaustive Search for Variational Quantum Algorithms
Ittay Alfassi, Dekel Meirom, Tal Mor·Jul 24, 2024
Quantum computers promise a great computational advantage over classical computers, yet currently available quantum devices have only a limited amount of qubits and a high level of noise, limiting the size of problems that can be solved accurately wi...
Route-Forcing: Scalable Quantum Circuit Mapping for Scalable Quantum Computing Architectures
Pau Escofet, Alejandro Gonzalvo, Eduard Alarc'on +2 more·Jul 24, 2024
Quantum computers are expected to scale in size to close the gap that currently exists between quantum algorithms and quantum hardware. To this end, quantum compilation techniques must scale along with the hardware constraints, shifting the current p...
Quanv4EO: Empowering Earth Observation by Means of Quanvolutional Neural Networks
A. Sebastianelli, Francesco Mauro, Giulia Ciabatti +4 more·Jul 24, 2024
A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different applications use those data, such as natural hazard monitoring, global cl...
Effective core potentials as a pathway to self-interaction error correction: a proof-of-concept study on one-electron systems
D. Lonsdale, L. Goerigk·Jul 24, 2024
In all applications of Density Functional Theory there is always a degree of one-electron self-interaction error (SIE). Here, we propose a simple self-interaction correction by applying an effective core potential (ECP) that replaces no electrons: we...
Tensor network enhanced dynamic multiproduct formulas
Niall F. Robertson, Bibek Pokharel, Bryce Fuller +10 more·Jul 24, 2024
Tensor networks and quantum computation are two of the most powerful tools for the simulation of quantum many-body systems. Rather than viewing them as competing approaches, here we consider how these two methods can work in tandem. We introduce a no...
Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-Term Quantum Hardware
Kuan-Cheng Chen, Xiaotian Xu, Felix Burt +1 more·Jul 24, 2024
This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. Leveraging a distributed framework, we address the limitations of current Noisy Intermediate-Scale...
Quantum Algorithms for Realizing Symmetric, Asymmetric, and Antisymmetric Projectors
Margarite L. LaBorde, Soorya Rethinasamy, Mark M. Wilde·Jul 24, 2024
In quantum computing, knowing the symmetries a given system or state obeys or disobeys is often useful. For example, Hamiltonian symmetries may limit allowed state transitions or simplify learning parameters in machine learning applications, and cert...