Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
A Survey on Quantum Reinforcement Learning
Nico Meyer, Christian Ufrecht, Maniraman Periyasamy +3 more·Nov 7, 2022
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning - our interpretation of this term will ...
Two-Qutrit entanglement: 56-years old algorithm challenges machine learning
Marcin Wie'sniak·Nov 6, 2022
Classifying states as entangled or separable is a highly challenging task, while it is also one of the foundations of quantum information processing theory. This task is higly nontrivial even for relatively simple cases, such as two-qutrit Bell-diago...
The effect of the processing and measurement operators on the expressive power of quantum models
A. Gratsea, Patrick Huembeli·Nov 6, 2022
There is an increasing interest in Quantum Machine Learning (QML) models, how they work and for which applications they could be useful. There have been many different proposals on how classical data can be encoded and what circuit ansätze and measur...
A Comparison of Encoding Techniques for an Analog Quantum Emulation Device
Sharan Mourya·Nov 6, 2022
Quantum computers can outperform classical computers in certain tasks. However, there are still many challenges to the current quantum computers such as decoherence and fault tolerance, and other drawbacks such as portability and accessibility. In th...
Low-overhead quantum error-correction codes with a cyclic topology
I. A. Simakov, I. Besedin·Nov 6, 2022
Quantum error correction is an important ingredient for scalable quantum computing. Stabilizer codes are one of the most promising and straightforward ways to correct quantum errors, are convenient for logical operations, and improve performance with...
Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets
Miguel Caçador Peixoto, N. Castro, M. Romão +2 more·Nov 6, 2022
Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle thi...
Quantum Deep Dreaming: A Novel Approach for Quantum Circuit Design
Romi Lifshitz·Nov 5, 2022
One of the challenges currently facing the quantum computing community is the design of quantum circuits which can efficiently run on near-term quantum computers, known as the quantum compiling problem. Algorithms such as the Variational Quantum Eigens...
Toward Neural Network Simulation of Variational Quantum Algorithms
Oliver Knitter, J. Stokes, S. Veerapaneni·Nov 5, 2022
Variational quantum algorithms (VQAs) utilize a hybrid quantum–classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization. Despite the promise of leveraging near- to intermediate-term quantum resou...
Reservoir Computing via Quantum Recurrent Neural Networks
Samuel Yen-Chi Chen, D. Fry, Amol Deshmukh +2 more·Nov 4, 2022
Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with high scientific and commercial value. Existing VQC or QNN-based method...
Excited-state molecular dynamics simulation based on variational quantum algorithms
H. Hirai·Nov 4, 2022
We propose an excited-state molecular dynamics simulation method based on variational quantum algorithms at a computational cost comparable to that of ground-state simulations. We utilize the feature that excited states can be obtained as metastable ...
Analog Quantum Variational Embedding Classifier
Rui Yang, Samuel Bosch, B. Kiani +2 more·Nov 4, 2022
Quantum machine learning has the potential to provide powerful algorithms for artificial intelligence. The pursuit of quantum advantage in quantum machine learning is an active area of research. For current noisy, intermediate-scale quantum (NISQ) co...
Hamiltonian quantum generative adversarial networks
Leeseok Kim, S. Lloyd, M. Marvian·Nov 4, 2022
We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of class...
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence
Rahul Jain, G. Piliouras, Ryann Sim·Nov 3, 2022
Recent advances in quantum computing and in particular, the introduction of quantum GANs, have led to increased interest in quantum zero-sum game theory, extending the scope of learning algorithms for classical games into the quantum realm. In this p...
Exploiting Qubit Reuse through Mid-circuit Measurement and Reset
Fei Hua, Yuwei Jin, Yan-Hao Chen +6 more·Nov 3, 2022
Quantum measurement is important to quantum computing as it extracts the outcome of the circuit at the end of the computation. Previously, all measurements have to be done at the end of the circuit. Otherwise, it will incur significant errors. But it...
Divide-and-conquer embedding for QUBO quantum annealing
Minjae Jo, M. Hanks, M. Kim·Nov 3, 2022
Quantum annealing promises to be an effective heuristic for complex NP-hard problems. However, clear demonstrations of quantum advantage are wanting, primarily constrained by the difficulty of embedding the problem into the quantum hardware. Communit...
Quantum Similarity Testing with Convolutional Neural Networks.
Yadong Wu, Yan Zhu, Ge Bai +2 more·Nov 3, 2022
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous variable quantum systems. In this Lette...
Entanglement complexity of the Rokhsar-Kivelson-sign wavefunctions
Stefano Piemontese, T. Roscilde, A. Hamma·Nov 2, 2022
In this paper we study the transitions of entanglement complexity in an exemplary family of states - the Rokhsar-Kivelson-sign wavefunctions - whose degree of entanglement is controlled by a single parameter. This family of states is known to feature...
Revealing microcanonical phases and phase transitions of strongly correlated electrons via time-averaged classical shadows
Gaurav Gyawali, Mabrur Ahmed, Eric W. Aspling +2 more·Nov 2, 2022
Quantum computers and simulators promise to enable the study of strongly correlated quantum systems. Yet, surprisingly, it is hard for them to compute ground states. They can, however, efficiently compute the dynamics of closed quantum systems. We pr...
Quantum City: simulation of a practical near-term metropolitan quantum network
Raja Yehia, Simon Neves, E. Diamanti +1 more·Nov 2, 2022
We present the architecture and analyze the applications of a metropolitan-scale quantum network that requires only limited hardware resources for end users. Using NetSquid, a quantum network simulation tool based on discrete events, we assess the pe...
Pulse-efficient quantum machine learning
A. Melo, Nathan Earnest-Noble, F. Tacchino·Nov 2, 2022
Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits thei...