Investigating the effect of circuit cutting in QAOA for the MaxCut
problem on NISQ devices
- URL: http://arxiv.org/abs/2302.01792v2
- Date: Thu, 5 Oct 2023 08:13:01 GMT
- Title: Investigating the effect of circuit cutting in QAOA for the MaxCut
problem on NISQ devices
- Authors: Marvin Bechtold, Johanna Barzen, Frank Leymann, Alexander Mandl,
Julian Obst, Felix Truger, Benjamin Weder
- Abstract summary: Noisy Intermediate-Scale Quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times.
quantum circuit cutting decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits.
- Score: 36.32934805738396
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Noisy Intermediate-Scale Quantum (NISQ) devices are restricted by their
limited number of qubits and their short decoherence times. An approach
addressing these problems is quantum circuit cutting. It decomposes the
execution of a large quantum circuit into the execution of multiple smaller
quantum circuits with additional classical postprocessing. Since these smaller
quantum circuits require fewer qubits and gates, they are more suitable for
NISQ devices. To investigate the effect of quantum circuit cutting in a quantum
algorithm targeting NISQ devices, we design two experiments using the Quantum
Approximate Optimization Algorithm (QAOA) for the Maximum Cut (MaxCut) problem
and conduct them on state-of-the-art superconducting devices. Our first
experiment studies the influence of circuit cutting on the objective function
of QAOA, and the second evaluates the quality of results obtained by the whole
algorithm with circuit cutting. The results show that circuit cutting can
reduce the effects of noise in QAOA, and therefore, the algorithm yields better
solutions on NISQ devices.
Related papers
- Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
We develop a reinforcement learning-based quantum compiler for a superconducting processor.
We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths.
Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
arXiv Detail & Related papers (2024-06-18T01:49:48Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - FragQC: An Efficient Quantum Error Reduction Technique using Quantum
Circuit Fragmentation [4.2754140179767415]
We present it FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold.
We achieve an increase of fidelity by 14.83% compared to direct execution without cutting the circuit, and 8.45% over the state-of-the-art ILP-based method.
arXiv Detail & Related papers (2023-09-30T17:38:31Z) - Circuit Cutting with Non-Maximally Entangled States [59.11160990637615]
Distributed quantum computing combines the computational power of multiple devices to overcome the limitations of individual devices.
circuit cutting techniques enable the distribution of quantum computations through classical communication.
Quantum teleportation allows the distribution of quantum computations without an exponential increase in shots.
We propose a novel circuit cutting technique that leverages non-maximally entangled qubit pairs.
arXiv Detail & Related papers (2023-06-21T08:03:34Z) - Iterative Qubits Management for Quantum Index Searching in a Hybrid
System [56.39703478198019]
IQuCS aims at index searching and counting in a quantum-classical hybrid system.
We implement IQuCS with Qiskit and conduct intensive experiments.
Results demonstrate that it reduces qubits consumption by up to 66.2%.
arXiv Detail & Related papers (2022-09-22T21:54:28Z) - Limitations of variational quantum algorithms: a quantum optimal
transport approach [11.202435939275675]
We obtain extremely tight bounds for standard NISQ proposals in both the noisy and noiseless regimes.
The bounds limit the performance of both circuit model algorithms, such as QAOA, and also continuous-time algorithms, such as quantum annealing.
arXiv Detail & Related papers (2022-04-07T13:58:44Z) - Efficient Classical Computation of Quantum Mean Values for Shallow QAOA
Circuits [15.279642278652654]
We present a novel graph decomposition based classical algorithm that scales linearly with the number of qubits for the shallow QAOA circuits.
Our results are not only important for the exploration of quantum advantages with QAOA, but also useful for the benchmarking of NISQ processors.
arXiv Detail & Related papers (2021-12-21T12:41:31Z) - Shortcuts to Quantum Approximate Optimization Algorithm [2.150418646956503]
We propose a new ansatz dubbed as "Shortcuts to QAOA" (S-QAOA)
S-QAOA provides shortcuts to the ground state of target Hamiltonian by including more two-body interactions and releasing the parameter freedoms.
Considering the MaxCut problem and Sherrington-Kirkpatrick (SK) model, numerically shows the YY interaction has the best performance.
arXiv Detail & Related papers (2021-12-21T02:24:19Z) - CutQC: Using Small Quantum Computers for Large Quantum Circuit
Evaluations [18.78105450344374]
This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers.
CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices.
In real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers.
arXiv Detail & Related papers (2020-12-03T23:52:04Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.