arXiv subjects

Yi Lee

Publications and source records attributed to Yi Lee.

7 recordsLinked to original sources

arXiv2026

Verified Pythagorean Composition for Adaptive Cryptographic Games: Noise Flooding in Homomorphic Encryption

Noise flooding is a standard defense against decryption attacks on approximate homomorphic encryption, but its security proof is unusually sensitive to composition. Replacing each of $q$ adaptive decryption answers with a statistically close simulation and applying an ordinary hybrid argument loses linearly in $q$. The cryptographic proof instead accumulates conditional Kullback-Leibler (KL) costs and converts to statistical distance once, giving the parameter-critical square-root loss. We machine-check this argument using Rocq and SSProve. Given any fully homomorphic encryption scheme that is approximately correct and IND-CPA secure, we formalize a reduction for every $q$-query IND-CPAD adversary and prove \[ \Pr[\mathsf{IND\text{-}CPAD}_{\mathsf{NF}}^{\mathcal A}=1] \leq \beta_{\mathsf{CPA}}(\mathcal B_{\mathcal A,q}) + \frac{\sqrt{qn}}{2\gamma}. \] where $n$ is the plaintext dimension and $\gamma$ is the flooding-width multiplier. Our proof constructs a new relational program logic over SSProve semantics. Its Pythagorean judgment composes conditional KL budgets without converting them to statistical distance, and a verified trace compiler lifts a local oracle rule to arbitrary adaptive programs with a single final conversion.

cs.CR↗

arXiv2026

End-to-End Formalization of Quantum Error Correction

Quantum error-correcting codes (QECCs) sit between noisy quantum hardware and reliable computation, so the code parameters used in practice must be trustworthy. The single number that summarizes a code's strength is its distance, yet certifying a distance lower bound is NP-hard in general, placing it beyond the reach of pen-and-paper proofs as well as direct proof-assistant scripting. As a result, distance values in the literature come either from non-scaling hand proofs, or from unverified solvers that leave a trust gap exactly where the code is supposed to provide a guarantee. We present Lean-QEC, the first Lean 4 formalization of stabilizer-code theory that delivers end-to-end, machine-checked distance certificates at industrial code sizes. Lean-QEC formalizes the linear algebra of qubit states, the Pauli group, stabilizer codes, the binary symplectic representation, classical coding theory, and the CSS and Bivariate Bicycle families. To break the combinatorial barrier, Lean-QEC translates the distance condition into a Boolean satisfiability formula through a verified reduction. The pipeline scales through a BitVec-flattened encoding that replaces Lean's Matrix representation, and an error-location encoding that reduces the variable count from $n$ to $k\lceil \log_2 n\rceil$. With these, we obtain automatically-generated Lean-checked distance proofs for a large range of industrially viable qLDPC codes within the Bivariate Bicycle and Generalized Bicycle families, including [[90, 8, 10]] and [[70, 6, 9]] BB codes, with the formulation scaling up to 144 qubits when performed outside the Lean kernel. The resulting library is reusable and is designed to plug into broader Lean-based efforts toward end-to-end verification of fault-tolerant quantum computation.

quant-ph↗

arXiv2026

A Scalable Open-Source QEC System with Sub-Microsecond Decoding-Feedback Latency

Quantum error correction (QEC) is essential for realizing large-scale, fault-tolerant quantum computation, yet its practical implementation remains a major engineering challenge. In particular, QEC demands precise real-time control of a large number of qubits and low-latency, high-throughput and accurate decoding of error syndromes. While most prior work has focused primarily on decoder design, the overall performance of any QEC system depends critically on all its subsystems including control, communication, and decoding, as well as their integration. To address this challenge, we present an open-source, fully integrated QEC system built on RISC-Q, a generator for RISC-V-based quantum control architectures. Implemented on RFSoC FPGAs, our system prototype integrates real-time qubit control, a scalable distributed multi-board architecture, and the state-of-the-art hardware QEC decoder within a low-latency, high-throughput decoding pipeline, forming a complete hardware platform ready for deployment with superconducting qubits. Experimental evaluation on a three-board prototype based on AMD ZCU216 RFSoCs demonstrates an end-to-end QEC decoding-feedback latency of 446 ns for a distance-3 surface code, including syndrome aggregation, network communication, syndrome decoding, and error distribution. Extrapolating from measured subsystem performance and state-of-the-art decoder benchmarks, the architecture can achieve sub-microsecond decoding-feedback latency up to a distance-21 surface code ($\sim$881 physical qubits) when scaled to larger hardware configurations.

quant-ph↗

arXiv2025

RISC-Q: A Generator for Real-Time Quantum Control System-on-Chips Compatible with RISC-V

Quantum computing imposes stringent requirements for the precise control of large-scale qubit systems, including, for example, microsecond-latency feedback and nanosecond-precision timing of gigahertz signals -- demands that far exceed the capabilities of conventional real-time systems. The rapidly evolving and highly diverse nature of quantum control necessitates the development of specialized hardware accelerators. While a few custom real-time systems have been developed to meet the tight timing constraints of specific quantum platforms, they face major challenges in scaling and adapting to increasingly complex control demands -- largely due to fragmented toolchains and limited support for design automation. To address these limitations, we present RISC-Q -- an open-source flexible generator for Quantum Control System-on-Chip (QCSoC) designs, featuring a programming interface compatible with the RISC-V ecosystem. Developed using SpinalHDL, RISC-Q enables efficient automation of highly parameterized and modular QCSoC architectures, supporting agile and iterative development to meet the evolving demands of quantum control. We demonstrate that RISC-Q can replicate the performance of existing QCSoCs with significantly reduced development effort, facilitating efficient exploration of the hardware-software co-design space for rapid prototyping and customization.

cs.AR↗

arXiv2022

Qafny: A Quantum-Program Verifier

Because of the probabilistic/nondeterministic behavior of quantum programs, it is highly advisable to verify them formally to ensure that they correctly implement their specifications. Formal verification, however, also traditionally requires significant effort. To address this challenge, we present Qafny, an automated proof system based on the program verifier Dafny and designed for verifying quantum programs. At its core, Qafny uses a type-guided quantum proof system that translates quantum operations to classical array operations modeled within a classical separation logic framework. We prove the soundness and completeness of our proof system and implement a prototype compiler that transforms Qafny programs and specifications into Dafny for automated verification purposes. We then illustrate the utility of Qafny's automated capabilities in efficiently verifying important quantum algorithms, including quantum-walk algorithms, Grover's algorithm, and Shor's algorithm.

quant-ph↗

arXiv2021

Constant-round Blind Classical Verification of Quantum Sampling

In a recent breakthrough, Mahadev constructed a classical verification of quantum computation (CVQC) protocol for a classical client to delegate decision problems in BQP to an untrusted quantum prover under computational assumptions. In this work, we explore further the feasibility of CVQC with the more general sampling problems in BQP and with the desirable blindness property. We contribute affirmative solutions to both as follows. (1) Motivated by the sampling nature of many quantum applications (e.g., quantum algorithms for machine learning and quantum supremacy tasks), we initiate the study of CVQC for quantum sampling problems (denoted by SampBQP). More precisely, in a CVQC protocol for a SampBQP problem, the prover and the verifier are given an input $x\in \{0,1\}^n$ and a quantum circuit $C$, and the goal of the classical client is to learn a sample from the output $z \leftarrow C(x)$ up to a small error, from its interaction with an untrusted prover. We demonstrate its feasibility by constructing a four-message CVQC protocol for SampBQP based on the quantum Learning With Error assumption. (2) The blindness of CVQC protocols refers to a property of the protocol where the prover learns nothing, and hence is blind, about the client's input. It is a highly desirable property that has been intensively studied for the delegation of quantum computation. We provide a simple yet powerful generic compiler that transforms any CVQC protocol to a blind one while preserving its completeness and soundness errors as well as the number of rounds. Applying our compiler to (a parallel repetition of) Mahadev's CVQC protocol for BQP and our CVQC protocol for SampBQP yields the first constant-round blind CVQC protocol for BQP and SampBQP respectively, with negligible and inverse polynomial soundness errors respectively, and negligible completeness errors.

quant-ph↗

arXiv2021

FOX-NAS: Fast, On-device and Explainable Neural Architecture Search

Neural architecture search can discover neural networks with good performance, and One-Shot approaches are prevalent. One-Shot approaches typically require a supernet with weight sharing and predictors that predict the performance of architecture. However, the previous methods take much time to generate performance predictors thus are inefficient. To this end, we propose FOX-NAS that consists of fast and explainable predictors based on simulated annealing and multivariate regression. Our method is quantization-friendly and can be efficiently deployed to the edge. The experiments on different hardware show that FOX-NAS models outperform some other popular neural network architectures. For example, FOX-NAS matches MobileNetV2 and EfficientNet-Lite0 accuracy with 240% and 40% less latency on the edge CPU. FOX-NAS is the 3rd place winner of the 2020 Low-Power Computer Vision Challenge (LPCVC), DSP classification track. See all evaluation results at https://lpcv.ai/competitions/2020. Search code and pre-trained models are released at https://github.com/great8nctu/FOX-NAS.

cs.LG↗