Papers
Live trends in quantum computing research, updated daily from arXiv.
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Linear circuit synthesis using weighted Steiner trees
Michele Nir Gavrielov, Alexander Ivrii, Shelly Garion·Aug 7, 2024
CNOT circuits are a common building block of general quantum circuits. The problem of synthesizing and optimizing such circuits has received a lot of attention in the quantum computing literature. This problem is especially challenging for quantum de...
Machine Learning Supported Annealing for Prediction of Grand Canonical Crystal Structures
Yannick Couzinié, Yuya Seki, Yusuke Nishiya +4 more·Aug 7, 2024
This study investigates the application of Factorization Machines with Quantum Annealing (FMQA) to address the crystal structure problem (CSP) in materials science. FMQA is a black-box optimization algorithm that combines machine learning with anneal...
Finding quantum partial assignments by search-to-decision reductions
Jordi Weggemans·Aug 7, 2024
In computer science, many search problems are reducible to decision problems, which implies that finding a solution is as hard as deciding whether a solution exists. A quantum analogue of search-to-decision reductions would be to ask whether a quantu...
Principal Trotter observation error with truncated commutators
Langyu Li·Aug 7, 2024
Hamiltonian simulation is one of the most promising applications of quantum computers, and the product formula is one of the most important methods for this purpose. Previous related work has mainly focused on the worst$-$case or average$-$case scena...
Double-bracket quantum algorithms for high-fidelity ground state preparation
Matteo Robbiati, Edoardo Pedicillo, Andrea Pasquale +12 more·Aug 7, 2024
Ground state preparation is a central application for quantum computers but remains challenging in practice. In this work, we quantitatively investigate the performance and gate counts of double-bracket quantum algorithms (DBQAs) for ground state pre...
Mutual information fluctuations and non-stabilizerness in random circuits
Arash Ahmadi, J. Helsen, C. Karaca +1 more·Aug 7, 2024
The emergence of quantum technologies has brought much attention to the characterization of quantum resources as well as the classical simulatability of quantum processes. Quantum resources, as quantified by non-stabilizerness, have in one theoretica...
Quantum Annealing Based Power Grid Partitioning for Parallel Simulation
Carsten Hartmann, Junjie Zhang, C. Calaza +3 more·Aug 7, 2024
Graph partitioning has many applications in power systems, from decentralized state estimation to parallel simulation. Focusing on parallel simulation, optimal grid partitioning minimizes the idle time caused by different simulation times for the sub...
On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning
Chaorong Shen, Yuan Li, Wenkang Zhan +15 more·Aug 7, 2024
Developing tailored semiconductor heterostructures on demand represents a critical capability for addressing the escalating performance demands in electronic and optoelectronic devices. However, traditional fabrication methods remain constrained by s...
NetQIR: An extension of QIR for distributed quantum computing
F. J. Cardama, Jorge V'azquez-P'erez, César Piñeiro +3 more·Aug 7, 2024
The rapid advancement of quantum computing has highlighted the need for scalable and efficient software infrastructures to fully exploit its potential. Current quantum processors face significant scalability constraints due to the limited number of q...
Explicit quantum surrogates for quantum kernel models
Akimoto Nakayama, Hayata Morisaki, Kosuke Mitarai +2 more·Aug 6, 2024
Quantum machine learning (QML) leverages quantum states for data encoding, with key approaches being explicit models that use parameterized quantum circuits and implicit models that use quantum kernels. Implicit models often have lower training error...
Binary Triorthogonal and CSS-T Codes for Quantum Error Correction
Eduardo Camps-Moreno, Hiram H. L'opez, Gretchen L. Matthews +3 more·Aug 6, 2024
In this paper, we study binary triorthogonal codes and their relation to CSS-T quantum codes. We characterize the binary triorthogonal codes that are minimal or maximal with respect to the CSS-T poset, and we also study how to derive new triorthogona...
Entanglement-enhanced learning of quantum processes at scale
A. Seif, Senrui Chen, Swarnadeep Majumder +6 more·Aug 6, 2024
Learning unknown processes affecting a quantum system reveals underlying physical mechanisms and enables suppression, mitigation, and correction of unwanted effects. Describing a general quantum process requires an exponentially large number of param...
QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction
Siddhant Dutta, Nouhaila Innan, Alberto Marchisio +2 more·Aug 6, 2024
Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of quantum-class...
MarQSim: Reconciling Determinism and Randomness in Compiler Optimization for Quantum Simulation
Xiuqi Cao, Junyu Zhou, Yuhao Liu +2 more·Aug 6, 2024
Quantum Hamiltonian simulation, fundamental in quantum algorithm design, extends far beyond its foundational roots, powering diverse quantum computing applications. However, optimizing the compilation of quantum Hamiltonian simulation poses significa...
Optimally Generating su(2^{N}) Using Pauli Strings.
Isaac D. Smith, Maxime Cautrès, David T. Stephen +1 more·Aug 6, 2024
Any quantum computation consists of a sequence of unitary evolutions described by a finite set of Hamiltonians. When this set is taken to consist of only products of Pauli operators, we show that the minimal such set generating su(2^{N}) contains 2N+...
Universal Matrix Multiplication on Quantum Computer
Jiaqi Yao, Tianjian Huang, Ding Liu·Aug 6, 2024
As a core underlying operation in pattern recognition and machine learning, matrix multiplication plays a crucial role in modern machine learning models and constitutes a major contributor to computational expenditure. Hence, researchers have spent d...
Benchmarking variational quantum algorithms for combinatorial optimization in practice
Tim Schwägerl, Yahui Chai, T. Hartung +2 more·Aug 6, 2024
Variational quantum algorithms, and in particular variants of the variational quantum eigensolver, have been proposed as approaches to combinatorial optimization (CO) problems. With only shallow ansatz circuits, these methods are considered suitabl...
Quantum simulations of chemistry in first quantization with any basis set
Timothy N Georges, Marius Bothe, Christoph Sünderhauf +3 more·Aug 6, 2024
Quantum computation of the energy of molecules and materials is one of the most promising applications of fault-tolerant quantum computers. Practical applications require development of quantum algorithms with reduced resource requirements. Previous ...
Investigating and improving student understanding of the basics of quantum computing
Peter Hu, Yangqiuting Li, Chandralekha Singh·Aug 6, 2024
Quantum information science and engineering (QISE) is a rapidly developing field that leverages the skills of experts from many disciplines to utilize the potential of quantum systems in a variety of applications. It requires talent from a wide varie...
Deep unfolded local quantum annealing
Shunta Arai, Satoshi Takabe·Aug 6, 2024
Local quantum annealing (LQA), an iterative algorithm, is designed to solve combinatorial optimization problems. It draws inspiration from QA, which utilizes adiabatic time evolution to determine the global minimum of a given objective function. In t...