Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Variational quantum solutions to the advection–diffusion equation for applications in fluid dynamics
Reuben Demirdjian, D. Gunlycke, C. Reynolds +2 more·Aug 24, 2022
Constraints in power consumption and computational power limit the skill of operational numerical weather prediction by classical computing methods. Quantum computing could potentially address both of these challenges. Herein, we present one method t...
Adaptive Resource Allocation in Quantum Key Distribution (QKD) for Federated Learning
Rakpong Kaewpuang, Minrui Xu, D. Niyato +3 more·Aug 24, 2022
Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured federated learning (QKD-FL), in which data owners connected via quantum channels can train an FL global model collaboratively without...
Alternating Layered Variational Quantum Circuits Can Be Classically Optimized Efficiently Using Classical Shadows
Afrad Basheer, Yuan Feng, C. Ferrie +1 more·Aug 24, 2022
Variational quantum algorithms (VQAs) are the quantum analog of classical neural networks (NNs). A VQA consists of a parameterized quantum circuit (PQC) which is composed of multiple layers of ansatzes (simpler PQCs, which are an analogy of NN layers...
Everything You Always Wanted to Know About Quantum Circuits
Edgard Muñoz-Coreas, H. Thapliyal·Aug 24, 2022
The development of circuits for quantum computing has been motivated by the proliferation of quantum algorithms which promise up to a superpolynomial factor speedup over classical equivalents. The quantum algorithms developed have the potential to im...
Noise tailoring for robust amplitude estimation
Archismita Dalal, Amara Katabarwa·Aug 24, 2022
A universal fault-tolerant quantum computer holds the promise to speed up computational problems that are otherwise intractable on classical computers; however, for the next decade or so, our access is restricted to noisy intermediate-scale quantum (...
Quantum Volume for Photonic Quantum Processors.
Yuxuan Zhang, Daoheng Niu, A. Shabani +1 more·Aug 24, 2022
Defining metrics for near-term quantum computing processors has been an integral part of the quantum hardware research and development efforts. Such quantitative characteristics are not only useful for reporting the progress and comparing different q...
Mid-circuit correction of correlated phase errors using an array of spectator qubits
Kevin Singh, C. Bradley, Shraddha Anand +3 more·Aug 24, 2022
Scaling up invariably error-prone quantum processors is a formidable challenge. Although quantum error correction ultimately promises fault-tolerant operation, the required qubit overhead and error thresholds are daunting. In a complementary proposal...
Quantum algorithm for Markov random fields structure learning
Liming Zhao, Lin-Chun Wan, Maohui Luo·Aug 24, 2022
Probabilistic graphical models play a crucial role in machine learning and have wide applications across various fields. A pivotal subset within this realm is undirected graphical models, also known as Markov random fields. In this work, we explore t...
Financial Index Tracking via Quantum Computing with Cardinality Constraints
Samuel Palmer, K. Karagiannis, Adam Florence +4 more·Aug 24, 2022
In this work, we demonstrate how to apply non-linear cardinality constraints, important for real-world asset management, to quantum portfolio optimization. This enables us to tackle non-convex portfolio optimization problems using quantum annealing t...
Q2Chemistry: A quantum computation platform for quantum chemistry
Yi Fan, Jie Liu, Xiongzhi Zeng +4 more·Aug 23, 2022
Quantum computers provide new opportunities for quantum chemistry. In this article,we present a versatile, extensible, and efficient software package, named Q2Chemistry, for developing quantum algorithms and quantum inspired classical algorithms in t...
Exponential concentration in quantum kernel methods
Supanut Thanasilp, Samson Wang, M. Cerezo +1 more·Aug 23, 2022
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guar...
Variational quantum algorithms for local Hamiltonian problems
A. Uvarov·Aug 23, 2022
Variational quantum algorithms (VQAs) are a modern family of quantum algorithms designed to solve optimization problems using a quantum computer. Typically VQAs rely on a feedback loop between the quantum device and a classical optimization algorithm...
Benchmarking of different optimizers in the variational quantum algorithms for applications in quantum chemistry.
Harshdeep Singh, S. Majumder, Sabyashachi Mishra·Aug 22, 2022
Classical optimizers play a crucial role in determining the accuracy and convergence of variational quantum algorithms; leading algorithms use a near-term quantum computer to solve the ground state properties of molecules, simulate dynamics of differ...
Quantum Multi-Agent Meta Reinforcement Learning
Won Joon Yun, Jihong Park, Joongheon Kim·Aug 22, 2022
Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-desig...
Solvable model of deep thermalization with distinct design times
Matteo Ippoliti, W. Ho·Aug 22, 2022
We study the emergence over time of a universal, uniform distribution of quantum states supported on a finite subsystem, induced by projectively measuring the rest of the system. Dubbed deep thermalization, this phenomenon represents a form of equili...
Alternative approach to quantum imaginary time evolution
P. Jouzdani, Calvin W. Johnson, E. Mucciolo +1 more·Aug 22, 2022
There is increasing interest in quantum algorithms that are based on the imaginary-time evolution (ITE), a successful classical numerical approach to obtain ground states. However, most of the proposals so far require heavy post-processing computatio...
Distributed Quantum Machine Learning
N. Neumann, R. Wezeman·Aug 22, 2022
Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, mu...
Revisiting semiconductor bulk hamiltonians using quantum computers
Raphael César de Souza Pimenta, A. T. Bezerra·Aug 22, 2022
With the advent of near-term quantum computers, it is now possible to simulate solid-state properties using quantum algorithms. By an adequate description of the system's Hamiltonian, variational methods enable to fetch of the band structure and othe...
Efficient algorithms for quantum information bottleneck
Masahito Hayashi, Yuxiang Yang·Aug 22, 2022
The ability to extract relevant information is critical to learning. An ingenious approach as such is the information bottleneck, an optimisation problem whose solution corresponds to a faithful and memory-efficient representation of relevant informa...
Problem-size-independent angles for a Grover-driven quantum approximate optimization algorithm
David Headley, F. Wilhelm·Aug 22, 2022
The Quantum Approximate Optimization Algorithm (QAOA) requires that circuit parameters are determined that allow one to sample from high-quality solutions to combinatorial optimization problems. Such parameters can be obtained using either costly out...