Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Quantum DeepONet: Neural operators accelerated by quantum computing
Peng Xiao, Muqing Zheng, Anran Jiao +2 more·Sep 24, 2024
In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep opera...
Quantum resources of quantum and classical variational methods
Thomas Spriggs, Arash Ahmadi, Bo-Ting Chen +1 more·Sep 19, 2024
Variational techniques have long been at the heart of atomic, solid-state, and many-body physics. They have recently extended to quantum and classical machine learning, providing a basis for representing quantum states via neural networks. These meth...
Ultracompact Programmable Silicon Photonics Using Layers of Low-Loss Phase-Change Material Sb2Se3 of Increasing Thickness
Sophie Blundell, Tom Radford, Idris A. Ajia +6 more·Sep 19, 2024
High-performance programmable silicon photonic circuits are considered to be a critical part of next-generation architectures for optical processing, photonic quantum circuits, and neural networks. Low-loss optical phase-change materials (PCMs) offer...
Quantum integration of decay rates at second order in perturbation theory
Jorge J. Martínez de Lejarza, David F. Renter'ia-Estrada, Michele Grossi +1 more·Sep 18, 2024
We present the first quantum computation of a total decay rate in high-energy physics at second order in perturbative quantum field theory. This work underscores the confluence of two recent cutting-edge advances. On the one hand, the quantum integra...
Using Optimal Control to Guide Neural-Network Interpolation of Continuously-Parameterized Gates
Bikrant Bhattacharyya, Fredy An, Dominik Kozbiel +2 more·Sep 15, 2024
Control synthesis for continuously-parameterized families of quantum gates can enable critical advantages for mid-sized quantum computing applications in advance of fault-tolerance. We combine quantum optimal control with physics-informed machine lea...
Q-SCALE: Quantum Computing-Based Sensor Calibration for Advanced Learning and Efficiency
Lorenzo Bergadano, Andrea Ceschini, Pietro Chiavassa +4 more·Sep 15, 2024
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency of air quality monitoring syste...
Towards a Cryogenic CMOS-Memristor Neural Decoder for Quantum Error Correction
Pierre-Antoine Mouny, M. Benhouria, Victor Yon +6 more·Sep 15, 2024
This paper presents a novel approach utilizing a scalable neural decoder application-specific integrated circuit (ASIC) based on metal oxide memristors in a 180nm CMOS technology. The ASIC architecture employs in-memory computing with memristor cross...
Quantum-Train with Tensor Network Mapping Model and Distributed Circuit Ansatz
Chen-Yu Liu, Chu-Hsuan Abraham Lin, Kuan-Cheng Chen·Sep 11, 2024
In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a...
Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network
Hector Hutin, Pavlo Bilous, Chengzhi Ye +8 more·Sep 9, 2024
Scaling up quantum computing devices requires solving ever more complex quantum control tasks. Machine learning has been proposed as a promising approach to tackle the resulting challenges. However, experimental implementations are still scarce. In t...
An Equivariant Machine Learning Decoder for 3D Toric Codes
Oliver Weissl, E. Egorov·Sep 6, 2024
Research on mitigating errors in computing and communication systems has grown with their widespread use. In quantum computing, error correction is crucial as errors can quickly propagate, undermining results and the theoretical speedup over classica...
Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems
Freya Shah, Taylor L. Patti, Julius Berner +3 more·Sep 5, 2024
Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective approach for simulating the time evolution of quantum wavefunctions, wh...
Federated Quantum-Train with Batched Parameter Generation
Chen-Yu Liu, Samuel Yen-Chi Chen·Sep 4, 2024
In this work, we introduce the Federated Quantum-Train (QT) framework, which integrates the QT model into federated learning to leverage quantum computing for distributed learning systems. Quantum client nodes employ Quantum Neural Networks (QNNs) an...
Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?
Chukwudubem Umeano, Stefano Scali, O. Kyriienko·Sep 2, 2024
We consider the problem of distinguishing two vectors (visualized as images or barcodes) and learning if they are related to one another. For this, we develop a geometric quantum machine learning (GQML) approach with embedded symmetries that allows f...
Unconditional advantage of noisy qudit quantum circuits over biased threshold circuits in constant depth
Michael de Oliveira, Sathyawageeswar Subramanian, Leandro Mendes +1 more·Aug 29, 2024
The rapid evolution of quantum devices fuels concerted efforts to experimentally establish quantum advantage over classical computing. Many demonstrations of quantum advantage, however, rely on computational assumptions and face verification challeng...
Distributed quantum machine learning via classical communication
Kiwmann Hwang, Hyang-Tag Lim, Yong-Su Kim +2 more·Aug 29, 2024
Quantum machine learning is emerging as a promising application of quantum computing due to its distinct way of encoding and processing data. It is believed that large-scale quantum machine learning demonstrates substantial advantages over classical ...
CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural Networks
Alejandro Mayorga, Alexander Yuan, Andrew Yuan +2 more·Aug 28, 2024
Neural networks have continued to gain prevalence in the modern era for their ability to model complex data through pattern recognition and behavior remodeling. However, the static construction of traditional neural networks inhibits dynamic intellig...
Theoretical framework for quantum associative memories
Adrià Labay-Mora, Eliana Fiorelli, R. Zambrini +1 more·Aug 26, 2024
Associative memory (AM) refers to the ability to relate a memory with an input and targets the restoration of corrupted patterns. It has been intensively studied in classical physical systems, as in neural networks where an attractor dynamics settles...
Optimal Quantum Circuit Design via Unitary Neural Networks
M. Zomorodi, H. Amini, M. Abbaszadeh +3 more·Aug 23, 2024
The process of translating a quantum algorithm into a form suitable for implementation on a quantum computing platform is crucial but yet challenging. This entails specifying quantum operations with precision, a typically intricate task. In this pape...
Quantum Convolutional Neural Networks are Effectively Classically Simulable
Pablo Bermejo, Paolo Braccia, Manuel S. Rudolph +3 more·Aug 22, 2024
Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts. First, that when randomly initialized, they can only operate on the i...
Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso
Yelleti Vivek, S. Vadlamani, Vadlamani Ravi +1 more·Aug 20, 2024
Modern deep learning continues to achieve outstanding performance on an astounding variety of high-dimensional tasks. In practice, this is obtained by fitting deep neural models to all the input data with minimal feature engineering, thus sacrificing...