Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Toward superpolynomial quantum speedup of equivariant quantum algorithms with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SU</mml:mi> <mml:mo>(</mml:mo> <mml:mi>d</mml:mi>
Han Zheng, Zimu Li, Sergii Strelchuk +2 more·Jul 15, 2022
We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU$(d)$ symmetries. It allows us to enhance a natural model of quantum computatio...
Demonstrating Scalable Randomized Benchmarking of Universal Gate Sets
Jordan Hines, Marie Lu, R. Naik +12 more·Jul 15, 2022
Randomized benchmarking (RB) protocols are the most widely used methods for assessing the performance of quantum gates. However, the existing RB methods either do not scale to many qubits or cannot benchmark a universal gate set. Here, we introduce a...
Nonclassical Advantage in Metrology Established via Quantum Simulations of Hypothetical Closed Timelike Curves.
D. Arvidsson-Shukur, Aidan G. McConnell, Nicole Yunger Halpern·Jul 15, 2022
We construct a metrology experiment in which the metrologist can sometimes amend the input state by simulating a closed timelike curve, a worldline that travels backward in time. The existence of closed timelike curves is hypothetical. Nevertheless, ...
Comparative Benchmark of a Quantum Algorithm for the Bin Packing Problem
Mikel Garcia de Andoin, I. Oregi, Esther Villar-Rodriguez +2 more·Jul 15, 2022
The Bin Packing Problem (BPP) stands out as a paradigmatic combinatorial optimization problem in logistics. Quantum and hybrid quantum-classical algorithms are expected to show an advantage over their classical counterparts in obtaining approximate s...
Demonstration of Algorithmic Quantum Speedup.
Bibek Pokharel, Daniel A. Lidar·Jul 15, 2022
Despite the development of increasingly capable quantum computers, an experimental demonstration of a provable algorithmic quantum speedup employing today's non-fault-tolerant devices has remained elusive. Here, we unequivocally demonstrate such a sp...
QSAN: A Near-Term Achievable Quantum Self-Attention Network
Ren-Xin Zhao, Jinjing Shi, Shenmin Zhang·Jul 14, 2022
Self-attention mechanism (SAM) is good at capturing the intrinsic connection between features to dramatically boost the performance of machine learning models. Nevertheless, the capability of SAM is not equipped with many current quantum machine lear...
A rapidly mixing Markov chain from any gapped quantum many-body system
S. Bravyi, Giuseppe Carleo, David Gosset +1 more·Jul 14, 2022
We consider the computational task of sampling a bit string x from a distribution π(x)=|⟨x|ψ⟩|2, where ψ is the unique ground state of a local Hamiltonian H. Our main result describes a direct link between the inverse spectral gap of H and the mixing...
Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum
S. Stein, N. Wiebe, Yufei Ding +2 more·Jul 14, 2022
Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processors' qubit capacity growing. However, near-term quantum algorithms are still unable to be induced with...
Parity-encoding-based quantum computing with Bayesian error tracking
Seok-Hyung Lee, S. Omkar, Yong-Siah Teo +1 more·Jul 14, 2022
Measurement-based quantum computing (MBQC) in linear optical systems is promising for near-future quantum computing architecture. However, the nondeterministic nature of entangling operations and photon losses hinder the large-scale generation of gra...
Demonstration of a Bosonic Quantum Classifier with Data Reuploading.
T. Ono, W. Roga, K. Wakui +4 more·Jul 14, 2022
In a single qubit system, a universal quantum classifier can be realized using the data reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrate it using a silic...
Reinforcement learning assisted recursive QAOA
Yash J. Patel, S. Jerbi, Thomas Bäck +1 more·Jul 13, 2022
In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, ...
Syndrome decoding by quantum approximate optimization
C. Lai, Kao-Yueh Kuo, Bo-Jyun Liao·Jul 13, 2022
The syndrome decoding problem is known to be NP-complete. The goal of the decoder is to find an error of low weight that corresponds to a given syndrome obtained from a parity-check matrix. We use the quantum approximate optimization algorithm (QAOA)...
Suppressing quantum errors by scaling a surface code logical qubit
R. Acharya, I. Aleiner, R. Allen +155 more·Jul 13, 2022
Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction^ 1 , 2 offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for w...
Quantum Metropolis Solver: a quantum walks approach to optimization problems
Roberto Campos, Pablo Antonio Moreno Casares, M. Martin-Delgado·Jul 13, 2022
The efficient resolution of optimization problems is one of the key issues in today’s industry. This task relies mainly on classical algorithms that present scalability problems and processing limitations. Quantum computing has emerged to challenge t...
Quantum Data Reduction with Application to Video Classification
Kostas Blekos, D. Kosmopoulos·Jul 13, 2022
We investigate a quantum data reduction technique with application to video classification. A hybrid quantum-classical step performs data reduction on the video dataset generating “representative” distributions for each video class. These distributio...
Quantum computing fidelity susceptibility using automatic differentiation
Olivia Di Matteo, R. Woloshyn·Jul 13, 2022
Automatic differentiation is an invaluable feature of machine learning and quantum machine learning software libraries. In this work it is shown how quantum automatic differentiation can be used to solve the condensed-matter problem of computing fide...
Distributed Shor's algorithm
Li Xiao, Daowen Qiu, Leon Luo +1 more·Jul 13, 2022
Shor's algorithm is one of the most important quantum algorithm proposed by Peter Shor [Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 1994, pp. 124--134]. Shor's algorithm can factor a large integer with certain probabi...
Testing and Learning Quantum Juntas Nearly Optimally
Thomas Chen, Shivam Nadimpalli, H. Yuen·Jul 13, 2022
We consider the problem of testing and learning quantum $k$-juntas: $n$-qubit unitary matrices which act non-trivially on just $k$ of the $n$ qubits and as the identity on the rest. As our main algorithmic results, we give (a) a $\widetilde{O}(\sqrt{...
Visualizing heterogeneous dipole fields by terahertz light coupling in individual nano-junctions
R. Kim, J. Park, S. Haeuser +12 more·Jul 13, 2022
To enhance the development of superior quantum circuits, the ability to detect defects accurately is crucial. The authors use broadband terahertz scanning near-field optical microscopy that enables the non-destructive and contactless identification o...
Scalable and programmable phononic network with trapped ions
Wentao Chen, Yao Lu, Shuaining Zhang +9 more·Jul 13, 2022
A network of bosons evolving among different modes while passing through beam splitters and phase shifters has been applied to demonstrate quantum computational advantage. While such networks have mostly been implemented in optical systems using phot...