Computation-Aided Classical-Quantum Multiple Access to Boost Network Communication Speeds

Hayashi, Masahito; Vázquez-Castro, M. A.

doi:10.1103/physrevapplied.16.054021

Cited by 10 publications

(4 citation statements)

References 54 publications

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“…The sufficiency of the N -sum box abstraction for the LC-QMAC is an especially intriguing question. Aside from the LC-QMAC, generalizations in other directions, e.g., towards noisy quantum channels and correlated inputs as in [59][60][61], other forms of decoding locality restrictions as in [74], and to non-linear computations as in [75,76] are also of interest.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, since we wish to explore multi-partite entanglements among a large number of parties it is important that we explore Σ-QMAC settings with arbitrarily large number of entangled servers. These considerations highlight the essential distinctions that separate our work from other interesting research directions pursued, for example in [59], [60] and [61], that explore -error sum computation over a QMAC with correlated data streams and noisy quantum channels, albeit with only 2 servers (transmitters).…”

Section: Significance Of Key Assumptionsmentioning

confidence: 99%

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The Capacity of Classical Summation over a Quantum MAC with Arbitrarily Replicated Inputs

Yao,

Jafar

2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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The Σ-QMAC problem is introduced, involving S servers, K classical (F d ) data streams, and T independent quantum systems. Data stream W k , k ∈ [K] is replicated at a subset of servers W(k) ⊂ [S], and quantum system Q t , t ∈ [T ] is distributed among a subset of servers E(t) ⊂ [S] such that Server s ∈ E(t) receives subsystem Q t,s of Q t = (Q t,s ) s∈E(t) . Servers manipulate their quantum subsystems according to their data and send the subsystems to a receiver. The total download cost is t∈[T ] s∈E(t) log d |Q t,s | qudits, where |Q| is the dimension of Q. The states and measurements of (Q t ) t∈[T ] are required to be separable across t ∈ [T ] throughout, but for each t ∈ [T ], the subsystems of Q t can be prepared initially in an arbitrary (independent of data) entangled state, manipulated arbitrarily by the respective servers, and measured jointly by the receiver. From the measurements, the receiver must recover the sum of all data streams. Rate is defined as the number of dits (F d symbols) of the desired sum computed per qudit of download. The capacity of Σ-QMAC, i.e., the supremum of achievable rates is characterized for arbitrary data and entanglement distributions W, E. For example, in the symmetric setting with K = S α data-streams, each replicated among a distinct α-subset of [S], and T = S β quantum systems, each distributed among a distinct β-subset of [S], the capacity of the Σ-QMAC is * Presented in part at IEEE GLOBECOM 2023. Connection to Simultaneous Message Passing (SMP):The underlying quantum multiple access (QMAC) communication model in the Σ-QMAC is similar to what is known in the literature as simultaneous message passing (SMP) model with quantum messages [21,24,47] . A noteworthy distinction is that the SMP model is typically studied from a communication complexity perspective which does not allow batch processing, whereas since our perspective is information theoretic, batch processing is not only allowed, it is essential to our problem formulation. For brevity, and to underscore the information theoretic perspective, we say QMAC when we mean an SMP model with quantum messages and batch processing. Connection to Quantum Metrology: The Σ-QMAC is conceptually related to various physically motivated and commonly studied models in the active area of distributed quantum sensing and quantum metrology. A general theme in this area is how the entanglement across quantum sensors allows higher precision (approaching the Heisenberg limit) in the computation of a function of distributed classical parameters, than what is possible without entanglement (the standard Quantum limit) [48]. For example, note the similarity of Fig. 1 and the quantum metrology protocol illustrated in [49]. In the quantum metrology protocol, entangled quantum systems are distributed to sensors (which take the role of servers in Fig. 1), classical parameters are encoded into them through quantum transducers, and the quantum systems are sent to a central receiver where the desired function is estimated by a joint measurement....

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Section: Discussionmentioning

confidence: 99%

Section: Significance Of Key Assumptionsmentioning

confidence: 99%

The Capacity of Classical Summation over a Quantum MAC with Arbitrarily Replicated Inputs

Yao,

Jafar

2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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“…In fact, the method introduced by references [ 29 , 30 ] can be efficiently implemented with a rate close to

. Furthermore, the recent reference [ 36 ] studied its quantum extension.…”

Section: Caf and Secure Cafmentioning

confidence: 99%

Secure Physical Layer Network Coding versus Secure Network Coding

Hayashi

2021

Entropy

Self Cite

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When a network has relay nodes, there is a risk that a part of the information is leaked to an untrusted relay. Secure network coding (secure NC) is known as a method to resolve this problem, which enables the secrecy of the message when the message is transmitted over a noiseless network and a part of the edges or a part of the intermediate (untrusted) nodes are eavesdropped. If the channels on the network are noisy, the error correction is applied to noisy channels before the application of secure NC on an upper layer. In contrast, secure physical layer network coding (secure PLNC) is a method to securely transmit a message by a combination of coding operation on nodes when the network is composed of set of noisy channels. Since secure NC is a protocol on an upper layer, secure PLNC can be considered as a cross-layer protocol. In this paper, we compare secure PLNC with a simple combination of secure NC and error correction over several typical network models studied in secure NC.

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“…With notable exception of [13], [14], previous work has largely focused on the quantum channels that act on finitedimensional qudits [15]- [17]. While recent results focused on coding [18]- [20], entanglement-assisted communication [21]- [24], and secrecy [25], [26], there is a gap in understanding of the fundamental limits of the bosonic multiple access communication in the presence of thermal-noise.…”

Section: Introductionmentioning

confidence: 99%

Fundamental Limits of Thermal-noise Lossy Bosonic Multiple Access Channel

Anderson¹,

Bash²

2022

Preprint

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Bosonic channels describe quantum-mechanically many practical communication links such as optical, microwave, and radiofrequency. We investigate the maximum rates for the bosonic multiple access channel (MAC) in the presence of thermal noise added by the environment and when the transmitters utilize Gaussian state inputs. We develop an outer bound for the capacity region for the thermal-noise lossy bosonic MAC. We additionally find that the use of coherent states at the transmitters is capacity-achieving in the limits of high and low mean input photon numbers. Furthermore, we verify that coherent states are capacity-achieving for the sum rate of the channel. In the non-asymptotic regime, when a global mean photon-number constraint is imposed on the transmitters, coherent states are the optimal Gaussian state. Surprisingly however, the use of single-mode squeezed states can increase the capacity over that afforded by coherent state encoding when each transmitter is photon number constrained individually.

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Computation-Aided Classical-Quantum Multiple Access to Boost Network Communication Speeds

Cited by 10 publications

References 54 publications

The Capacity of Classical Summation over a Quantum MAC with Arbitrarily Replicated Inputs

The Capacity of Classical Summation over a Quantum MAC with Arbitrarily Replicated Inputs

Secure Physical Layer Network Coding versus Secure Network Coding

Fundamental Limits of Thermal-noise Lossy Bosonic Multiple Access Channel

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