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arXiv:2402.18778

X-ResQ: Reverse Annealing for Quantum MIMO Detection with Flexible Parallelism

Kim, Minsung, Abhishek Kumar Singh, Davide Venturelli, John Kaewell, Kyle Jamieson

Published Feb 29, 2024• 2 citations
arXivHTMLPDF

Abstract

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 fine-grained quantum task parallelism that is uniquely enabled by the Reverse Annealing (RA) protocol. Unlike prior designs, X-ResQ has many desirable system properties for a parallel QA detector 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 (over 10 bits/s/Hz) for $4\times6$ MIMO with 16-QAM using six levels of parallelism with 240 qubits and $220~μ$s QA compute time, achieving 2.5--5$\times$ gains compared against other tested detectors. For more comprehensive evaluations, we implement and evaluate X-ResQ in the non-quantum digital setting. This non-quantum X-ResQ demonstration showcases the potential to realize ultra-large $1024\times1024$ MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.

DOI
10.48550/arxiv.2402.18778

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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 fine-grained quantum task parallelism that is uniquely enabled by the Reverse Annealing (RA) protocol. Unlike prior designs, X-ResQ has many desirable system properties for a parallel QA detector 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 (over 10 bits/s/Hz) for 4×64\times64×6 MIMO with 16-QAM using six levels of parallelism with 240 qubits and 220 μ220~μ220 μs QA compute time, achieving 2.5--5×\times× gains compared against other tested detectors. For more comprehensive evaluations, we implement and evaluate X-ResQ in the non-quantum digital setting. This non-quantum X-ResQ demonstration showcases the potential to realize ultra-large 1024×10241024\times10241024×1024 MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.
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.
Exploiting (near-)optimal MIMO signal processing algorithms in the next generation (NextG) cellular systems holds great promise in achieving significant wireless performance gains in spectral efficiency and device connectivity, to name a few. However, it is extremely difficult to enable optimal processing methods in the systems, since the required computational amount increases exponentially with more users and higher data rates, while available processing time is strictly limited. In this regard, quantum signal processing has been recently identified as a promising potential enabler of the (near-)optimal algorithms in the systems, since quantum computing could dramatically speed up the computation via non-conventional effects based on quantum mechanics. Given existing quantum decoherence and noise on quantum hardware, parallel quantum optimization could accelerate the process even further at the expense of more qubit usage. In this paper, we discuss the parallelization of quantum MIMO processing and investigate a spin-level preprocessing method for relatively finer-grained decomposition that can support more flexible parallel quantum signal processing, compared to the recently reported symbol-level decomposition method. We evaluate the method on the state-of-the-art analog D-Wave Advantage quantum processor.
User demand for increasing amounts of wireless capacity continues to outpace supply, and so to meet this demand, significant progress has been made in new MIMO wireless physical layer techniques. Higher-performance systems now remain impractical largely only because their algorithms are extremely computationally demanding. For optimal performance, an amount of computation that increases at an exponential rate both with the number of users and with the data rate of each user is often required. The base station's computational capacity is thus becoming one of the key limiting factors on wireless capacity. QuAMax is the first large MIMO centralized radio access network design to address this issue by leveraging quantum annealing on the problem. We have implemented QuAMax on the 2,031 qubit D-Wave 2000Q quantum annealer, the state-of-the-art in the field. Our experimental results evaluate that implementation on real and synthetic MIMO channel traces, showing that 10 µs of compute time on the 2000Q can enable 48 user, 48 AP antenna BPSK communication at 20 dB SNR with a bit error rate of 10-6 and a 1,500 byte frame error rate of 10-4.
Multi-user (MU), multiple-input, multiple-output (MIMO) detection has been extensively investigated, and many techniques have been proposed. However, further performance improvements may be constrained by limitations in classical computation. The motivation for this work is to test whether a machine that exploits quantum principles can offer improved performance over conventional detection approaches. This paper presents an evaluation of MIMO detection based on quantum annealing (QA) when run on an actual QA quantum processing unit (QPU) and describes the challenges and potential improvements. The evaluations show promising results in some cases, such as near-optimality in a QPSK-modulated 8×8 MIMO case, but poor results in other cases, such as for larger systems or when using 16-QAM. We show that some challenges of QA detection include dealing with integrated control errors (ICE), the limited dynamic range of QA QPUs, an exponential increase in the number of qubits to the problem size, and a high computation overhead. Solving these challenges could make QA-based detection superior to conventional approaches and bring a new generation of MU-MIMO detection methods.
With the continuous growth of the Internet of Things (IoT), the trend of increasing numbers of IoT devices will continue. To increase the network's capability to support a large number of active devices accessing a network concurrently, this work presents IoT-ResQ, a warm-started quantum annealing-based multi-device detector via quantum reverse annealing (RA). Unlike in typical quantum forward annealing (FA) protocol, IoT-ResQ's RA starts its search operation on a controllable candidate classical state, instead of a quantum superposition, and thus allows refined local quantum search around the initial state. This procedure can provide an opportunity of utilizing both conventional classical- and quantum-based detectors together in a hybrid synergy, to boost quantum optimization performance, mitigating the effect of quantum decoherence and noise on quantum hardware. In our evaluation, IoT-ResQ achieves nearly two to three orders of magnitude better BER and over 2X packet success rate with packet size of 32-byte compared to other quantum and conventional detectors at SNR 9 dB to support 48 active IoT devices with QPSK modulation (implying 48,000 deployed devices with 0.1% wake-up radio rate at a time), requiring ≈ 140 μs pure compute time for detection.
This extended abstract outlines our research on quantum and emerging computing systems for next-generation wireless networks. The research aims to leverage quantum and quantum-inspired computation to expedite baseband processing at base stations. We introduce our system design directions and prototype systems that are implemented on analog quantum processors. The prototypes are designed for quantum-accelerated near-optimal multi-user detection processing in MIMO systems that could drastically increase wireless performance for tomorrow's NextG cellular networking standards, as well as in NextG wireless local area networks (LAN).