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Quantum-assisted and Quantum-based Solutions in Wireless Systems

Sandor Imre, Laszlo Gyongyosi

arXiv:1206.5996v1quant-phcs.ITmath.IT

TL;DR

The paper describes the challenge of identifying transmitted symbols from received signals when the channel-dependent mapping is not reversible. It discusses quantum-search for finding likely transmitted vectors and quantum-channel examples, including superactivation.

  • Problem

    Identifying the transmitted symbol vector from the received signal is ambiguous because the mapping depends on random channel parameters and is not reversible.

    Source: §I · p. 2

  • Method

    Quantum-search uses parallel processing to form a discrete probability distribution from transmitted vectors and find the most likely vector for a received signal.

    Source: §I · p. 2

  • Results

    Two zero-capacity quantum channels used in parallel with an entanglement-controlled decoder can deliver classical information, activating their capacities.

    Source: C. Quantum-based Communications · p. 5

  • Takeaways & Limitations

    Quantum parallel processing is identified as a source of significant algorithmic speedup, while the no-cloning theorem constrains construction of ML MUD detectors.

    Source: §I · pp. 2–3

    Space limitations leave topics including Ultra-Wideband systems and cognitive solutions untreated.

    Source: §VI · p. 5

Abstract

from arXiv · show

In wireless systems there is always a trade-off between reducing the transmit power and mitigating the resultant signal-degradation imposed by the transmit-power reduction with the aid of sophisticated receiver algorithms, when considering the total energy consumption. Quantum-assisted wireless communications exploits the extra computing power offered by quantum mechanics based architectures. This paper summarizes some recent results in quantum computing and the corresponding application areas in wireless communications.

I. INTRODUCTION

The introduction connects continued semiconductor miniaturization and computing growth with interest in quantum computing and communications. It uses DS-CDMA maximum-likelihood multi-user detection to explain how quantum states, processing, and measurement could relate to wireless problems.

I. INTRODUCTION

  • Moore’s law describes an empirical trend of chip transistor counts roughly doubling every 18–24 months, while continued miniaturization brings computing toward a nanoscale regime governed by quantum mechanics.The passage says the trend has held provided sufficient investment in science and technology is attracted by the semiconductor industry.Source: §I · p. 1
  • The uplink DS-CDMA model is asynchronous because users have different delays, and unknown transmitted symbols make detection blind; the receiver maps symbol hypotheses to matched-filter outputs.The mapping is not reversible because it also depends on random channel parameters, so the optimal decision selects the hypothesis with the largest conditional probability density.Source: §I · p. 2
  • Quantum search can use parallel processing to estimate received-signal probabilities and find the most likely transmitted-symbol hypothesis, with algorithms decomposable into elementary quantum gates.The discussion presents this as a possible quantum implementation of maximum-likelihood multi-user detection, not as a reported detector result.Source: §I · pp. 2–3
  • A qbit can occupy a superposition of 0 and 1, while closed-system state evolution is represented by unitary transforms.The paper presents qbits as quantum-information units and contrasts their superposition with a classic bit's one-or-zero state.Source: §I · p. 2
  • The no-cloning theorem [141] prevents copying an unknown intermediate quantum state for separate calculations, constraining multi-user detector design.The passage identifies designing repeaters for large wireless and optical quantum networks as a current research challenge.Source: §I · p. 3
  • Entangled qbits are linked so measuring one constrains the other's outcome; the paper describes entanglement as a tool for quantum computing and communications.The discussion also notes that entanglement-related effects have been demonstrated for qbits sent to arbitrarily distant locations,.Source: §I · p. 3

B. Quantum-assisted Communications

Quantum-assisted methods are presented as a way to address difficult wireless search and optimization problems, while balancing transmit power against receiver-processing energy. The section connects quantum search to multiuser detection, MIMO detection, and resource allocation.

B. Quantum-assisted Communications

  • Wireless design balances transmit-power reduction against receiver-processing energy, since sophisticated receivers consume more power and power- and bandwidth-efficiency are generally inversely related.The stated objective is to minimize total power used for both transmission and signal processing.Source: B. Quantum-assisted Communications · p. 4
  • Grover search uses parallel database queries but requires consecutive query-evaluation pairs, and its results can have error probability; variants address multiple matches and non-uniform inputs.The section relates database search to wireless cost-function optimization and cites quantum neural networks and quantum bee colony optimization for MUD; QMUD was published in and improved in and.Source: B. Quantum-assisted Communications · pp. 4–5
  • Classical MUD and MIMO detection can face high complexity on unsorted search spaces or cost functions with many local optima.The section also describes resource allocation as a database-search problem, including selecting a resource allocation with low network-wide co-channel interference.Source: B. Quantum-assisted Communications · p. 4
  • A K = 10-user 64QAM ML MUD would evaluate the MMSE cost function for all 64^10 legitimate symbol combinations, which the passage calls unrealistic.The section also notes multiple cost-function optima in MIMO when transmit antennas outnumber receiver antennas and the channel matrix is rank-deficient and non-invertible.Source: B. Quantum-assisted Communications · p. 4
  • Jointly detecting R symbols for all K users can mitigate or eliminate MUI and ISI in dispersive channels, but creates a potentially excessive search space.The passage contrasts this with classic MUDs, which estimate users' symbols one symbol at a time.Source: B. Quantum-assisted Communications · p. 4
  • The section identifies zero-forcing, MMSE, MBER, and MLS as possible detection cost functions, emphasizing that the criterion can strongly affect the final result.The examples include MIMO multi-stream detection.Source: B. Quantum-assisted Communications · p. 4

C. Quantum-based Communications

The section reviews quantum-channel capacities and examples in which quantum encoding or entangled decoding changes what classical information can be transmitted. It also identifies unresolved questions about entanglement-assisted capacity.

C. Quantum-based Communications

  • The paper confines its treatment of quantum information theory to selected communication-related aspects, noting that a generalization of classic capacity theory is beyond its scope.It notes quantum entropy S(ρ) for quantum-source information and mentions block-coding and pilot-symbol approaches to quantum-channel error correction.Source: C. Quantum-based Communications · p. 5
  • The section distinguishes classical-input/output channel capacity from capacity within larger quantum systems, where the central question is whether and when capacity can increase.The classical-capacity measure for decomposable product-state inputs is called unentangled classical capacity, or Holevo-Schumacher-Westmoreland (HSW) capacity C(N).Source: C. Quantum-based Communications · p. 5
  • A binary symmetric channel with p = 1/2 has zero classical capacity, yet the described classic-bit-to-single-qubit encoding and receiver detector permit error-free transmission with C(N) = 1.The passage presents this as enabling redundancy-free error correction over a specific subset of quantum channels, with the rationale traced to increased degrees of freedom in encoding and decoding.Source: C. Quantum-based Communications · p. 5
  • Two zero-capacity quantum channels used in parallel can deliver classical information with an entanglement-controlled decoder, a phenomenon termed superactivation.The decoder couples subdecoders through an entangled input state; Fig. 20 depicts superactivation of zero-capacity quantum channels.Source: C. Quantum-based Communications · p. 5, Figure 20 · p. 5
  • Entanglement-assisted capacity CE(N) raises the open question of whether entangled channel inputs can increase attainable capacity.The passage frames this as a challenging unresolved issue.Source: C. Quantum-based Communications · p. 5

VI. CONCLUSIONS

The conclusion points to optical wireless and quantum-domain transceivers as areas needing further development, while noting that several wireless topics were omitted. It also identifies error-resilient holographic-video streaming as a further challenge.

VI. CONCLUSIONS

  • The conclusion says UWB systems and cognitive radios were left untreated because of space limitations.Source: §VI · pp. 5–6
  • Increasing tele-traffic and demand for new frequency bands accompany a shift toward higher carrier frequencies, where pathloss rises and cell sizes have shrunk; optical wireless still needs RF-level maturity.The passage notes the availability of unlicensed bandwidth in the optical frequency domain.Source: §VI · p. 6
  • The conclusion calls for advances in quantum-information theory to establish performance limits and in quantum-domain RF transceivers to approach those limits.Source: §VI · p. 6
  • Error-resilient streaming of multiview and 3D holographic video over optical wireless and quantum-domain transceivers remains a further challenge.The conclusion connects this challenge to prospective flawless tele-presence requiring a sufficiently high-speed, high-integrity wireless link.Source: §VI · p. 6

ABOUT THE AUTHORS

The authors' biographies describe research and professional work in telecommunications, wireless systems, and quantum computing and communications.

ABOUT THE AUTHORS

  • Sandor Imre is Head of the Telecommunications Department at BME and R&D Director of its Mobile Innovation Centre.His stated interests include mobile and wireless systems, quantum computing, and communications.Source: ABOUT THE AUTHORS · p. 10
  • Imre's listed contributions span wireless access technologies, mobility protocols, security and privacy, reconfigurable systems, and quantum-computing algorithms and protocols.Source: ABOUT THE AUTHORS · p. 10
  • Laszlo Gyongyosi is pursuing a Ph.D. in computer science at BUTE and works on quantum channel capacities, computation, communication, cryptography, and information theory.Source: ABOUT THE AUTHORS · p. 10
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