arXiv subjects

Yicheng Guang

Publications and source records attributed to Yicheng Guang.

3 recordsLinked to original sources

arXiv2026

CoDecoder: Make Quantum Decoders Up to 100x Faster and Even More Accurate

Fault-tolerant quantum computers need fast and accurate error decoding. We present CoDecoder, a small neural frontend that composes with an existing decoder to improve accuracy and reduce mean decoding time. It predicts each shot's logical outcome, retains confident predictions and routes the remaining shots to the unchanged fallback. A logical-operator-aware output head scores shifted logical operators, combining equivalent logical lines on surface codes and deciding logical qubits jointly on bivariate-bicycle (BB) codes. Fused GPU inference and efficient routing reduce the overhead paid on every shot. In registered tests under circuit-level noise, composition reduces failures by 6.4-31.4% relative to correlated matching on surface codes of distance 7-17 and by 22-50% relative to full-DEM Relay-BP on two BB codes, with 2.3-124-fold speed-ups across these comparisons. With our re-implementations of the neural decoder Cascade, composition reduces decoding time in all four settings. A joint decision of both decoders on the routed shots keeps this speed and makes the composition more accurate than Cascade in all four, with 14-18% fewer failures on the [[72,12,6]] code. On the [[72,12,6]] BB code, a conventional head with independent logical outputs fails 3.8-5.0 times as often as our joint class score under matched training conditions. Per-shot analysis shows how composition gains accuracy. On retained shots, the frontend corrects more fallback errors than it introduces, even when it is less accurate overall.

quant-ph↗

arXiv2026

Breaking Memory Bottlenecks in Quantum Control Systems for More Precise Experiments and Higher Throughput Computing

As quantum computing matures, there is an increasing need for deeper and longer experiments to characterize quantum devices precisely and higher circuit throughput to validate more domain applications. However, this need is hindered by a memory bottleneck at the quantum control system layer, arising from the limited capacity of on-chip block random-access memory (BRAM) and the non-deterministic latency of dynamic random-access memory (DRAM). To break this bottleneck, we present Ant-Q, a memory hierarchy design for the QubiC 2.0 control system that integrates DRAM with BRAM to support pipelined quantum circuit execution while keeping inter-circuit timing deterministic whenever each circuit outlasts the refill of the next. A switch between command buffers then takes a constant 134 ns and, inside a circuit, acts on the qubit as a 144 ns idle. On the bench, Ant-Q runs deep one- and two-qubit randomized benchmarking beyond the command capacity of the baseline. For circuits with at least 1000 shots, it reduces the time the board spends beyond the circuit execution from 18.91%-104.54% to at most 0.53% of the execution time, and it improves the board-level throughput of 30-circuit batches by 51.06% against a baseline with the same shared tables (59.90% against the stock baseline). On a transmon qubit, Ant-Q executes a 5101-Clifford sequence and acquires uninterrupted 100 s Ramsey records with 0.01 Hz frequency spacing. In simulations, these records detect 1/f frequency noise of 1.37 and 1.62 times lower spectral density than the same records split into the 2.048 s runs of the baseline. The measured spectra resolve no 1/f component and match the measurement floor from 0.1 to 100 Hz. Ant-Q has been merged into the open-source QubiC gateware and software.

cs.AR↗

arXiv2025

On the Potential of Quantum Computing in Classical Program Analysis

Classical program analysis techniques, such as abstract interpretation and symbolic execution, are essential for ensuring software correctness, optimizing performance, and enabling compiler optimizations. However, these techniques face computational limitations when analyzing programs with large or exponential state spaces, limiting their effectiveness in ensuring system reliability. Quantum computing, with its parallelism and ability to process superposed states, offers a promising solution to these challenges. In this work, we present QEX, a design that uses quantum computing to analyze classical programs. By synthesizing quantum circuits that encode program states in superposition and trace data dependency between program variables through entanglement, QEX enables the simultaneous exploration of program behaviors, significantly improving scalability and precision. This advancement has broad applications, from debugging and security verification to optimizing compilers for next-generation hardware. As a proof-of-concept, we evaluated QEX on 22 benchmark programs, demonstrating its effectiveness in analyzing program states. To support more language features and make QEX realized sooner in Fault-Tolerant Quantum Computing (FTQC), we propose QEX-H which hybridizes QEX with classical analysis techniques. To our knowledge, this work is the first proposal to use quantum computing for classical program analysis.

quant-ph↗