?? Wednesday in Quantum_Computing: Today's Cutting-Edge Papers
Hyun Ho Park
Quantum Algorithm Developer | Data Scientist | Professional at Computer Vision and Gen AI.
Top Quantum Computing Papers (06 January - 12 January)
Dive into the most compelling and innovative research in the field of quantum computing. This week’s selections highlight cutting-edge advances and theoretical developments.
(1) Partial Auger Decay Widths from Complex-Valued Density Matrices - Proposes a method to compute partial Auger decay widths using EOMIP-CCSD wave functions with non-Hermitian quantum mechanics, avoiding channel projection issues by analyzing two-electron density matrix contributions. Validates the approach with spectra calculations for various molecules, including a novel spectrum for the cyanide anion.
Read More : https://arxiv.org/abs/2501.03362
(2) Dynamic Structure Factor of Calogero-Sutherland Fluids - Determines the dynamic structure factor of the chiral component in the Calogero-Sutherland model's repulsive regime using an extended conformal field theory. Confirms the equivalence between first- and second-quantized formulations and highlights a sharp response function.
Read More : https://arxiv.org/abs/2501.03762
(3) Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study - Investigates the application of Quantum Support Vector Classifiers for detecting buggy software commits, comparing QSVC and PQSVC with classical SVC. Proposes data subset aggregation and incremental testing methods to enhance detection accuracy and manage quantum feature mapping.
Read More : https://arxiv.org/abs/2501.04690
(4) EOM Minimum Point Bias Voltage Estimation for Application in Quantum Computing - Addresses bias drift in electro-optic modulators by estimating instantaneous bias voltage using a small pilot tone and photodetector feedback, improving modulation quality for quantum computing systems.
Read More : https://arxiv.org/abs/2412.14060
(5) Exact Decoding of Repetition Code under Circuit Level Noise - Develops a maximum likelihood decoding algorithm for repetition codes under circuit-level noise, achieving exact error thresholds and reducing logical error rates. Demonstrates the algorithm's efficacy in quantum memory systems with realistic noise models.
Read More : https://arxiv.org/abs/2501.03582
Thank you for joining us for this week’s Quantum Computing Highlights!
Trust you found these papers to be a valuable addition to your quantum research journey. Keep an eye out for the next newsletter, will bring you more of the latest breakthroughs and discussions in quantum computing.
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Best regards,
Hyunho