Large-scale quantum computers mandate error correction and fault tolerance. Due to constraints on the number of qubits, fault tolerance is difficult to achieve in near-term quantum systems. Therefore, error correction...
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ISBN:
(数字)9781728197104
ISBN:
(纸本)9781728197111
Large-scale quantum computers mandate error correction and fault tolerance. Due to constraints on the number of qubits, fault tolerance is difficult to achieve in near-term quantum systems. Therefore, error correction should require minimal resources. Gates in the near-term devices are also noisy. Quantum error correction code blocks built with these noisy gates can inject further error in the circuit. The goals for error correction in near-term systems are as follows: (i) using a small number of qubits for encoding, and (ii) keeping cost of circuits for encoding and decoding low. In this paper, we propose two techniques to achieve these mutually orthogonal goals. For a binary quantum system we propose an error estimation method that can aid in reducing the number of error correcting blocks via sparse scheduling. For ternary quantum systems, we propose an approximate code that can correct errors with high probability while significantly reducing the circuit cost. These techniques are expected to be helpful for error mitigation in near-term systems in the absence of fault tolerance.
Multi-valued quantum systems can store more information than binary ones for a given number of quantum states. For reliable operation of multi-valued quantum systems, error correction is mandated. In this paper, we pr...
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Quantum states have high affinity for errors and hence error correction is of utmost importance to realise a quantum computer. Laflamme showed that 5 qubits are necessary to correct a single error on a qubit. In a Pau...
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With increasing popularity of wireless local area network (WLAN) and emerging real-time applications, seamless mobility has become one of the primary concerns. Hence, the choice of a proper handover algorithm is of ut...
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Many real-life networks such as social networks, biological networks, citation networks etc., are temporally evolving by nature. In general, the evolution takes place gradually with time. But occasionally, the nodes m...
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Error correction is of utmost necessity for large-scale quantum computing. Quantum error correcting codes can be degenerate, if more than one type of error can map the input state to the same error state. In this pape...
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To cope with high bandwidth demands of modern applications, device-to-device (D2D) communications using millimeter-wave (mmWave) signals are being harnessed. The major challenge of mmWave signals is that they require ...
To cope with high bandwidth demands of modern applications, device-to-device (D2D) communications using millimeter-wave (mmWave) signals are being harnessed. The major challenge of mmWave signals is that they require a strict, obstacle-free line-of-sight communication. Static obstacles are easier to avoid; dynamic obstacles pose the main hurdle, their movement not being known. In this work, we propose a way to learn link blockages due to dynamic obstacles, using the link activation history. For this, one might have to explore non-optimal link activations. This ensures that all links are tried a sufficient number of times, ensuring adequate knowledge about link failures, thus creating an exploration-exploitation dilemma. To this end, we propose a systematic way of exploring such non-optimal channel allocations, so that the number of link failures is minimized. Given the hardness of this problem, we devise a greedy solution, and show its effectiveness over existing strategies through simulations.
We formulate the joint power and channel allocation problem (JPCAP) for device to device (D2D) communication as a cost minimization problem, where cost is defined as a linear combination of the number of channels used...
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We formulate the joint power and channel allocation problem (JPCAP) for device to device (D2D) communication as a cost minimization problem, where cost is defined as a linear combination of the number of channels used and total power requirement. We first show that JPCAP is NP-hard and then propose a greedy channel and power allocation (GCPA) algorithm to assign channels and powers to the links. We design GCPA in such a fashion that there exists an order of the links for which it produces optimum solution. Finally using GCPA we develop a randomized algorithm (RA) that increase the optimum hitting probability by an exponential factor of total number of links. Through simulation, we evaluate the performance of RA and show that RA outperforms an existing approach.
Use of millimeter waves for short range device to device (D2D) communication is one of the most promising ways to provide high data rates. But millimeter waves are susceptible to blockages due to high penetration loss...
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ISBN:
(纸本)9781665495516
Use of millimeter waves for short range device to device (D2D) communication is one of the most promising ways to provide high data rates. But millimeter waves are susceptible to blockages due to high penetration losses and hence require almost a line of sight (LoS) communication to provide such high data rates. When the direct LoS of a pair of devices is blocked by an obstacle, a reconfigurable intelligent surfaces (RIS) in the vicinity can be exploited to bypass the obstacle and achieve an indirect LoS between them. In this paper, we develop a strategy for placing minimum number of RISs in an environment filled with obstacles such that any pair of devices in the environment who does not have direct LoS can communicate via these RISs by establishing an indirect LoS. We convert this RIS placement problem to a set cover problem and give a greedy algorithm to get a near optimal but practical solution. This is done as a preprocessing step before the actual devices are deployed in the region. Next we present a strategy which knowing the actual deployment of the devices, selects a subset of the deployed RISs for operation together with their channel coefficients and phase shifts such that the system sum throughput of the devices is maximised. Finally simulation results show that strategically placed RISs can significantly improve system throughput in comparison to the existing deployment strategies.
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