Investigation into the Potential of Parallel Quantum Annealing for
Simultaneous Optimization of Multiple Problems: A Comprehensive Study
- URL: http://arxiv.org/abs/2403.05764v1
- Date: Sat, 9 Mar 2024 02:18:48 GMT
- Title: Investigation into the Potential of Parallel Quantum Annealing for
Simultaneous Optimization of Multiple Problems: A Comprehensive Study
- Authors: Arit Kumar Bishwas, Anuraj Som, Saurabh Choudhary
- Abstract summary: Annealing is a technique to solve multiple optimization problems simultaneously.
Annealing method minimizes idle qubits and holds promise for substantial speed-up.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Parallel Quantum Annealing is a technique to solve multiple optimization
problems simultaneously. Parallel quantum annealing aims to optimize the
utilization of available qubits on a quantum topology by addressing multiple
independent problems in a single annealing cycle. This study provides insights
into the potential and the limitations of this parallelization method. The
experiments consisting of two different problems are integrated, and various
problem dimensions are explored including normalization techniques using
specific methods such as DWaveSampler with Default Embedding, DWaveSampler with
Custom Embedding and LeapHybridSampler. This method minimizes idle qubits and
holds promise for substantial speed-up, as indicated by the Time-to-Solution
(TTS) metric, compared to traditional quantum annealing, which solves problems
sequentially and may leave qubits unutilized.
Related papers
- MG-Net: Learn to Customize QAOA with Circuit Depth Awareness [51.78425545377329]
Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling optimization challenges.
The requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices.
We introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians.
arXiv Detail & Related papers (2024-09-27T12:28:18Z) - Bias-field digitized counterdiabatic quantum optimization [39.58317527488534]
We call this protocol bias-field digitizeddiabatic quantum optimization (BF-DCQO)
Our purely quantum approach eliminates the dependency on classical variational quantum algorithms.
It achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude.
arXiv Detail & Related papers (2024-05-22T18:11:42Z) - Benchmarking digital quantum simulations above hundreds of qubits using quantum critical dynamics [42.29248343585333]
We benchmark quantum hardware and error mitigation techniques on up to 133 qubits.
We show reliable control up to a two-qubit gate depth of 28, featuring a maximum of 1396 two-qubit gates.
Results are transferable to applications such as Hamiltonian simulation, variational algorithms, optimization, or quantum machine learning.
arXiv Detail & Related papers (2024-04-11T18:00:05Z) - Noise Dynamics of Quantum Annealers: Estimating the Effective Noise
Using Idle Qubits [0.0]
We show that long term trends in solution quality exist on the D-Wave device, and that the unused qubits can be used to measure the current level of noise of the quantum system.
In this work, we embed a disjoint random QUBO on the unused parts of the chip alongside the QUBO to be solved, which acts as an indicator of the solution quality of the device over time.
arXiv Detail & Related papers (2022-09-12T23:06:51Z) - Quantum Optimization of Maximum Independent Set using Rydberg Atom
Arrays [39.76254807200083]
We experimentally investigate quantum algorithms for solving the Maximum Independent Set problem.
We find the problem hardness is controlled by the solution degeneracy and number of local minima.
On the hardest graphs, we observe a superlinear quantum speedup in finding exact solutions.
arXiv Detail & Related papers (2022-02-18T19:00:01Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - Parallel Quantum Annealing [0.0]
Quantum annealers of D-Wave Systems, Inc., offer an efficient way to compute high quality solutions of NP-hard problems.
We propose a novel method, called parallel quantum annealing, to make better use of available qubits.
We demonstrate that our method may give dramatic speed-ups in terms of Time-to-Solution (TTS) for solving instances of the Maximum Clique problem.
arXiv Detail & Related papers (2021-11-11T00:10:44Z) - Quantum amplitude damping for solving homogeneous linear differential
equations: A noninterferometric algorithm [0.0]
This work proposes a novel approach by using the Quantum Amplitude Damping operation as a resource, in order to construct an efficient quantum algorithm for solving homogeneous LDEs.
We show that such an open quantum system-inspired circuitry allows for constructing the real exponential terms in the solution in a non-interferometric.
arXiv Detail & Related papers (2021-11-10T11:25:32Z) - Sampling diverse near-optimal solutions via algorithmic quantum
annealing [0.3506539188356145]
One of the main open problems is the lack of ergodicity, or mode collapse, for typical Monte Carlo solvers.
Here, we introduce a new diversity measure for quantifying the number of independent approximate solutions for NP-hard optimization problems.
arXiv Detail & Related papers (2021-10-20T13:33:37Z) - Theoretical survey of unconventional quantum annealing methods applied
to adifficult trial problem [2.2209333405427585]
We consider a range of unconventional modifications to Quantum Annealing (QA)
In this problem, inspired by "transverse field chaos" in larger systems, classical and quantum methods are steered toward a false local minimum.
We numerically study this problem by using a variety of new methods from the literature.
arXiv Detail & Related papers (2020-11-12T05:54:57Z) - Quantum Geometric Machine Learning for Quantum Circuits and Control [78.50747042819503]
We review and extend the application of deep learning to quantum geometric control problems.
We demonstrate enhancements in time-optimal control in the context of quantum circuit synthesis problems.
Our results are of interest to researchers in quantum control and quantum information theory seeking to combine machine learning and geometric techniques for time-optimal control problems.
arXiv Detail & Related papers (2020-06-19T19:12:14Z)
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.