Current quantum computers suffer from a limited number of qubits and high error rates, limiting practical applicability. Different techniques exist to mitigate these effects and run larger algorithms. In this work, we analyze one of these techniques called quantum circuit cutting. With circuit cutting, a quantum circuit is decomposed into smaller sub-circuits, each of which can be run on smaller quantum hardware. We compare the performance of quantum circuit cutting with different cutting strategies, and then apply circuit cutting to a QAOA algorithm. Using simulations, we first show that Randomized Clifford measurements outperform both Pauli and random unitary measurements. Second, we show that circuit cutting has trouble providing correct answers in noisy settings, especially as the number of circuits increases.
We propose a new method to extend the size of a quantum computation beyond the number of physical qubits available on a single device. This is accomplished by randomly inserting measure-and-prepare channels to express the output state of a large circuit as a separable state across distinct devices. Our method employs randomized measurements, resulting in a sample overhead that is $\widetilde{O}(4^k / \varepsilon ^2)$, where $\varepsilon $ is the accuracy of the computation and $k$ the number of parallel wires that are "cut" to obtain smaller sub-circuits. We also show an information-theoretic …
This paper proposes a quantum approximate optimization algorithm (QAOA) method for wireless scheduling problems. The QAOA is one of the promising hybrid quantum-classical algorithms for many applications and it provides highly accurate optimization solutions in NP-hard problems. QAOA maps the given problems into Hilbert spaces, and then it generates Hamiltonian for the given objectives and constraints. Then, QAOA finds proper parameters from classical optimization approaches in order to optimize the expectation value of generated Hamiltonian. Based on the parameters, the optimal solution to th…
Wire cutting is a technique for partitioning large quantum circuits into smaller subcircuits in such a way that observables for the original circuits can be estimated from measurements on the smaller subcircuits. Such techniques provide workarounds for the limited numbers of qubits that are available on near-term quantum devices. Wire cutting, however, introduces multiplicative factors in the number of times such subcircuits need to be executed in order to estimate desired quantum observables to desired levels of statistical accuracy. An optimal wire-cutting methodology has recently been repor…
Simulation of quantum computing on supercomputers is a significant research topic, which plays a vital role in quantum algorithm verification, error-tolerant verification and other applications. Tensor network contraction based on density matrix is an important single-amplitude simulation strategy, but it is hard to execute on the distributed computing systems currently. In this paper, we dive deep into this problem, and propose a scheme based on cutting edges of undirected graphs. This scheme cuts edges of undirected graphs with large tree width to obtain many undirected subgraphs with small …
Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits
Graduate School of Science and Technology, Keio University（庆应义塾大学理工学府）；International Center for Elementary Particle Physics, University of Tokyo（东京大学基础粒子物理国际中心）；Department of Applied Physics and Physico-Informatics, Keio University（庆应义塾大学应用物理与物理信息学部）；Quantum Computing Center, Keio University（庆应义塾大学量子计算中心）；NTT Computer and Data Science Laboratories, NTT Inc.（日本电信电话株式会社计算机与数据科学实验室）；NTT Research Center for Theoretical Quantum Information, NTT Inc.（日本电信电话株式会社理论量子信息研究中心）
CutBackdoor: A Circuit Cut Triggered Backdoor Attack on Variational Quantum Algorithms
University of Central Florida（中佛罗里达大学）；University of Florida（佛罗里达大学）；North Carolina State University（北卡罗来纳州立大学）；Indiana University Bloomington（印第安纳大学伯明顿分校）
