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Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood detection to overcome the limitations of the currently deployed linear detectors. This paper presents X-ResQ, a QA-based MIMO detector system featuring flexible parallelism that is uniquely enabled by quantum Reverse Annealing (RA). Unlike prior designs, X-ResQ has many desirable parallel QA system properties and has effectively improved detection performance as more qubits are assigned. In our evaluations on a state-of-the-art quantum annealer, fully parallel X-ResQ achieves near-optimal throughput for 4 ×6 MIMO with 16-QAM using approx. 240 qubits achieving 2.5–5× gains compared against other classical and quantum detectors. We also implement and evaluate X-ResQ in the non-quantum digital setting for more comprehensive evaluations. This classical X-ResQ showcases the potential to realize ultra-large 1024 ×1024 MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.
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Benchmarking Chain Strength: An Optimal Approach for Quantum Annealing
Quantum annealing (QA) is a promising optimization technique used to find global optimal solution of a combinatorial optimization problem by leveraging quantum fluctuations. In QA, the problem being solved is mapped onto the quantum processing unit (QPU) composed of qubits through a procedure called minor-embedding. The qubits are connected by a network of couplers, which determine the strength of the interactions between the qubits. The strength of the couplers that connect qubits within a chain is often referred to as the chain strength. The appropriate balance of chain strength is equally imperative in enabling the qubits to interact with one another in a way that is strong enough to obtain the optimal solution, but not excessively strong so as not to bias the original problem terms. To this end, we address the problem of identifying the optimal chain strength through the utilization of Path Integral Monte Carlo (PIMC) quantum simulation algorithm. The results indicate that our judicious choice of chain strength parameter facilitates enhancements in quantum annealer performance and solution quality, thereby paving the way for QA to compete with, or potentially outperform, classical optimization algorithms.
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- PAR ID:
- 10477832
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE International Conference on Quantum Computing and Engineering (QCE)
- ISBN:
- 979-8-3503-4323-6
- Page Range / eLocation ID:
- 397 to 406
- Subject(s) / Keyword(s):
- quantum annealing chain strength minor embedding
- Format(s):
- Medium: X
- Location:
- Bellevue, WA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/QCE57702.2023.00052
