  Guessing Random Additive Noise Decoding (GRAND) is a recently proposed
universal Maximum Likelihood (ML) decoder for short-length and high-rate linear
block-codes. Soft-GRAND (SGRAND) is a prominent soft-input GRAND variant,
outperforming the other GRAND variants in decoding performance; nevertheless,
SGRAND is not suitable for parallel hardware implementation. Ordered
Reliability Bits-GRAND (ORBGRAND) is another soft-input GRAND variant that is
suitable for parallel hardware implementation, however it has lower decoding
performance than SGRAND. In this paper, we propose List-GRAND (LGRAND), a
technique for enhancing the decoding performance of ORBGRAND to match the ML
decoding performance of SGRAND. Numerical simulation results show that LGRAND
enhances ORBGRAND's decoding performance by $0.5-0.75$ dB for channel-codes of
various classes at a target FER of $10^{-7}$. For linear block codes of length
$127/128$ and different code-rates, LGRAND's VLSI implementation can achieve an
average information throughput of $47.27-51.36$ Gbps. In comparison to
ORBGRAND's VLSI implementation, the proposed LGRAND hardware has a $4.84\%$
area overhead.
