Channel Coding is the technique that enables reliable delivery of digital data over unreliable communication channels. For most high performance channel coding techniques, the existing classical algorithms are computationally expensive, making them impractical for throughput-demanding applications with large code sizes. Today’s Noisy Intermediate-Scale Quantum (NISQ) computers, although limited due to a modest number of qubits, short coherence time, and poor gate fidelity, are useful tools for exploring and experimenting with possible solutions to a wide variety of computational problems.In this paper we show how careful initialization of qubits combined with a simple quantum circuit, enables us to perform channel decoding for different linear block codes. We first explain our novel qubit initialization technique which we call "Quantum Soft Decision". We then show how to build a simple quantum circuit based on the Generator or Parity-check matrix using another technique called "Quantum Generator". Using these universal concepts, we implement Quantum Decoders for two different types of linear block codes, namely Hamming codes and Polar codes. Our simple quantum circuits achieve decoding performances comparable with best classical algorithms such as Maximum Likelihood (ML) for Hamming codes and Successive Cancellation (SC) and Successive Cancellation List (SCL) for Polar codes. Using Qiskit, we implemented and compared the decoding performance at different code sizes and noise levels on simulated (both ideal and noisy) quantum computers. Also using Amazon Braket, we verified the algorithm on real quantum computers.
Channel Coding is the technique that enables reliable delivery of digital data over unreliable communication channels. For most high performance channel coding techniques, the existing classical algorithms are computationally expensive, making them impractical for throughput-demanding applications with large code sizes. Today’s Noisy Intermediate-Scale Quantum (NISQ) computers, although limited due to a modest number of qubits, short coherence time, and poor gate fidelity, are useful tools for exploring and experimenting with possible solutions to a wide variety of computational problems.In this paper we show how careful initialization of qubits combined with a simple quantum circuit, enables us to perform channel decoding for different linear block codes. We first explain our novel qubit initialization technique which we call "Quantum Soft Decision". We then show how to build a simple quantum circuit based on the Generator or Parity-check matrix using another technique called "Quantum Generator". Using these universal concepts, we implement Quantum Decoders for two different types of linear block codes, namely Hamming codes and Polar codes. Our simple quantum circuits achieve decoding performances comparable with best classical algorithms such as Maximum Likelihood (ML) for Hamming codes and Successive Cancellation (SC) and Successive Cancellation List (SCL) for Polar codes. Using Qiskit, we implemented and compared the decoding performance at different code sizes and noise levels on simulated (both ideal and noisy) quantum computers. Also using Amazon Braket, we verified the algorithm on real quantum computers.
The use of quantum computation for wireless network applications is emerging as a promising paradigm to bridge the performance gap between in-practice and optimal wireless algorithms. While today's quantum technology offers limited number of qubits and low fidelity gates, application-based quantum solutions help us to understand and improve the performance of such technology even further. This paper introduces QGateD-Polar, a novel Quantum Gate-based Maximum-Likelihood Decoder design for Polar error correction codes, which are becoming widespread in today's 5G and tomorrow's NextG wireless networks. QGateD-Polar uses quantum gates to dictate the time evolution of Polar code decoding -- from the received wireless soft data to the final decoded solution -- by leveraging quantum phenomena such as superposition, entanglement, and interference, making it amenable to quantum gate-based computers. Our early results show that QGateD-Polar achieves the Maximum Likelihood performance in ideal quantum simulations, demonstrating how performance varies with noise.
Motivated by the recent advancement of quantum processors, we investigate quantum approximate optimization algorithm (QAOA) to employ quasi-maximum-likelihood (ML) decoding of classical channel codes. QAOA is a hybrid quantum-classical variational algorithm, which is advantageous for the near-term noisy intermediate-scale quantum (NISQ) devices, where the fidelity of quantum gates is limited by noise and de-coherence. We first describe how to construct Ising Hamiltonian model to realize quasi-ML decoding with QAOA. For level-1 QAOA, we derive the systematic way to generate theoretical expressions of cost expectation for arbitrary binary linear codes. Focusing on [7], [4] Hamming code as an example, we analyze the impact of the degree distribution in associated generator matrix on the quantum decoding performance. The excellent performance of higher-level QAOA decoding is verified when Pauli rotation angles are optimized through meta-heuristic variational quantum eigensolver (VQE). Furthermore, we demonstrate the QAOA decoding performance in a real quantum device.
Recent years have seen rapid development in the subject of quantum coding theory, with breakthroughs on many exciting classes of codes, including quantum LDPC codes, quantum locally testable codes, and quantum codes with interesting transversal gates. However, a natural class of quantum codes, which has been well-studied classically, has not yet been treated: those which can be quickly encoded and decoded. This problem concerns the channel capacity setting, where a noise channel sits between perfect encoding and unencoding/decoding operations; this is the setting that is relevant for communication between fault-tolerant quantum computers. In this work, we construct asymptotically good quantum codes that can be encoded and unencoded by quantum circuits of logarithmic depth and consisting of a linear total number of gates. The classical decoding algorithms also run in logarithmic depth and use O(nlogn) gates, or alternatively a linear number of gates but with higher depth. We further construct explicit and asymptotically good quantum codes whose encoding, unencoding and decoding all use a linear number of gates, and additionally whose encoding and unencoding may be run in logarithmic depth.
We provide a purely quantum version of polar codes, achieving the coherent information of any quantum channel. Our scheme relies on a recursive channel combining and splitting construction, where a two-qubit gate randomly chosen from the Clifford group is used to combine two single-qubit channels. The inputs to the synthesized bad channels are frozen by preshared EPR pairs between the sender and the receiver, so our scheme is entanglement assisted. We further show that quantum polarization can be achieved by choosing the channel combining Clifford operator randomly, from a much smaller subset of only 9 two-qubit Clifford gates. Subsequently, we show that a Pauli channel polarizes if and only if a specific classical channel over four symbol input set polarizes. We exploit this equivalence to prove fast polarization for Pauli channels, and to devise an efficient successive cancellation based decoding algorithm for such channels. Finally, we present a code construction based on chaining several quantum polar codes, which is shown to require a rate of preshared entanglement that vanishes asymptotically.
Error correctiong codes (ECC) are widly used in applications to correct errors in data transmission over unreliable or noisy communication channels. Recently, two kinds of promising codes attracted lots of research interest because they provide excellent error correction performance. One is non-binary LDPC codes, and the other is polar codes. This dissertation focuses on efficient decoding algorithms and decoder design for thesetwo types of codes.Non-binary low-density parity-check (LDPC) codes have some advantages over their binary counterparts, but unfortunately their decoding complexity is a significant challenge. The iterative hard- and soft-reliability based majority-logic decoding algorithms are attractive for non-binary LDPC codes, since they involve only finite field additions and multiplications as well as integer operations and hence have significantly lower complexity than other algorithms. We propose two improvements to the majority-logic decoding algorithms. Instead of the accumulation of reliability information in the ex-isting majority-logic decoding algorithms, our first improvement is a new reliability information update. The new update not only results in better error performance and fewer iterations on average, but also further reduces computational complexity. Since existing majority-logic decoding algorithms tend to have a high error floor for codes whose parity check matrices have low column weights, our second improvement is a re-selection scheme, which leads to much lower error floors, at the expense of more finite field operations and integer operations, by identifying periodic points, re-selectingintermediate hard decisions, and changing reliability information.Polar codes are of great interests because they provably achieve the symmetric capacity of discrete memoryless channels with arbitrary input alphabet sizes an explicit construction. Most existing decoding algorithms of polar codes are based on bit-wise hard or soft decisions. We propose symbol-decision successive cancellation (SC) and successive cancellation list (SCL) decoders for polar codes, which use symbol-wise hard or soft decisions for higher throughput or better error performance. Then wepropose to use a recursive channel combination to calculate symbol-wise channel transition probabilities, which lead to symbol decisions. Our proposed recursive channel combination has lower complexity than simply combining bit-wise channel transition probabilities. The similarity between our proposed method and Arıkan's channel transformations also helps to share hardware resources between calculating bit- and symbol-wise channel transition probabilities. To reduce the complexity of the list pruning, atwo-stage list pruning network is proposed to provide a trade-off between the error performance and the complexity of the symbol-decision SCL decoder. Since memory is a significant part of SCL decoders, we also propose a pre-computation memory-saving technique to reduce memory requirement of an SCL decoder.To reduce the complexity of the recursive channel combination further, we propose an approximate ML (AML) decoding unit for SCL decoders. In particular, we investigate the distribution of frozen bits of polar codes designed for both the binary erasure and additive white Gaussian noise channels, and take advantage of the distribution to reduce the complexity of the AML decoding unit, improving the throughput-area efficiency of SCL decoders.Furthermore, to adapt to variable throughput or latency requirements which exist widely in current communication applications, a multi-mode SCL decoder with variable list sizes and parallelism is proposed. If high throughput or small latency is required, the decoder decodes multiple received words in parallel with a small list size. However, if error performance is of higher priority, the multi-mode decoder switches to a serialmode with a bigger list size. Therefore, the multi-mode SCL decoder provides a flexible tradeoff between latency, throughput and error performance at the expense of small overhead.
Polar codes, as the first provable capacity-achieving error-correcting codes, have received much attention in recent years. However, the decoding performance of polar codes with traditional successive-cancellation (SC) algorithm cannot match that of the low-density parity-check or Turbo codes. Because SC list (SCL) decoding algorithm can significantly improve the error-correcting performance of polar codes, design of SCL decoders is important for polar codes to be deployed in practical applications. However, because the prior latency reduction approaches for SC decoders are not applicable for SCL decoders, these list decoders suffer from the long-latency bottleneck. In this paper, we propose a multibit-decision approach that can significantly reduce latency of SCL decoders. First, we present a reformulated SCL algorithm that can perform intermediate decoding of 2 b together. The proposed approach, referred as 2-bit reformulated SCL (2b-rSCL) algorithm, can reduce the latency of SCL decoder from (3n-2) to (2n-2) clock cycles without any performance loss. Then, we extend the idea of 2-b-decision to general case, and propose a general decoding scheme that can perform intermediate decoding of any 2Kbits simultaneously. This general approach, referred as 2K-bit reformulated SCL (2Kb-rSCL) algorithm, can reduce the overall decoding latency to as short as n/2K-2-2 cycles. Furthermore, on the basis of the proposed algorithms, very large-scale integration architectures for 2b-rSCL and 4b-rSCL decoders are synthesized. Compared with a prior SCL decoder, the proposed (1024, 512) 2b-rSCL and 4b-rSCL decoders can achieve 21% and 60% reduction in latency, 1.66 and 2.77 times increase in coded throughput with list size 2, and 2.11 and 3.23 times increase in coded throughput with list size 4, respectively.