arXiv subjects

Zack Hassman

Publications and source records attributed to Zack Hassman.

3 recordsLinked to original sources

arXiv2025

ExtraFerm: An Extended Matchgate Simulator

We present and open source Extraferm, a quantum circuit simulator tailored to chemistry applications. More specifically, our simulator can compute the Born-rule probabilities of samples obtained from circuits containing particle number-conserving matchgates and controlled-phase gates. We support both approximate and exact calculation of probabilities, and for approximate probability calculation, our simulator's runtime is exponential only in the magnitudes of the circuit's controlled-phase gate angles. This makes our simulator useful for simulating certain systems that are beyond the reach of conventional state vector methods. We demonstrate our simulator's utility by simulating the local cluster unitary Jastrow (LUCJ) ansatz and integrating it with sample-based quantum diagonalization (SQD) to improve the accuracy of molecular ground-state energy estimates with negligible computational overhead. More generally, we highlight a regime in which our simulator achieves substantially superior latency scaling and exponentially superior memory scaling over a tensor network simulator and a state vector simulator. As an efficient and flexible tool for simulating quantum chemistry circuits, our simulator enables new opportunities for enhancing near-term quantum algorithms in chemistry and related domains.

quant-ph↗

arXiv2023

YFlows: Systematic Dataflow Exploration and Code Generation for Efficient Neural Network Inference using SIMD Architectures on CPUs

We address the challenges associated with deploying neural networks on CPUs, with a particular focus on minimizing inference time while maintaining accuracy. Our novel approach is to use the dataflow (i.e., computation order) of a neural network to explore data reuse opportunities using heuristic-guided analysis and a code generation framework, which enables exploration of various Single Instruction, Multiple Data (SIMD) implementations to achieve optimized neural network execution. Our results demonstrate that the dataflow that keeps outputs in SIMD registers while also maximizing both input and weight reuse consistently yields the best performance for a wide variety of inference workloads, achieving up to 3x speedup for 8-bit neural networks, and up to 4.8x speedup for binary neural networks, respectively, over the optimized implementations of neural networks today.

cs.AR↗

arXiv2023

SONIQ: System-Optimized Noise-Injected Ultra-Low-Precision Quantization with Full-Precision Parity

Ultra-low-precision inference can sharply reduce memory and latency but often degrades accuracy and relies on specialized hardware. We present SONIQ, a system-optimized, noise-injected quantization framework that learns per-channel mixed precision for both weights and activations while training under the same rules used at inference. By injecting hardware-calibrated quantization noise during training, SONIQ steers models toward the discrete arithmetic used at deployment -- without bespoke runtimes. Across CNNs and Transformers, SONIQ achieves up to 16x and 7x compression, respectively, while matching or exceeding full-precision accuracy. Measured end-to-end, SONIQ delivers up to 7.3x CPU speedup over strong INT8 baselines and up to 6.3x (vector units) / 2.8x (tensor cores) GPU speedup relative to FP16. A practical outcome is that two per-channel precision levels -- one in the 1--4-bit range and one in the 4--8-bit range -- suffice in practice; at inference, each channel selects one of the two, keeping kernels simple and fast. To our knowledge, SONIQ is the first framework to reach or surpass full-precision accuracy under ultra-low (1--4 bits per parameter) regimes while remaining deployable on commodity hardware, narrowing the gap between quantization theory and practical, high-throughput inference.

cs.AR↗