arXiv subjects

Shlomo Kashani

Publications and source records attributed to Shlomo Kashani.

7 recordsLinked to original sources

arXiv2026

QuantumNovelty: A Skill-Orchestrating Language Agent for Referee-Style Review and Patentability Screening of Quantum Papers and Patents

Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an auditable, reproducible, and cost-transparent form. We present QuantumNovelty, an open-source skill-orchestrating language agent that both generates quantum-computing artifacts (papers, Pareto-front ansatz candidates, and patent drafts) and reviews them through simulated referee and patent-examiner panels. Its design contribution is an audit-and-falsify layer of deterministic gates -- strict Pareto domination, numerical recomputation from on-disk artifacts, Wilson small-sample intervals, and a cross-vendor consensus guard -- that constrains, rather than generates, the claims allowed to survive; every model call is logged with backend, token count, and cost. We make no accuracy claim against human experts, and validate only what is checkable without human labels: on a planted adversarial corpus the deterministic gates catch every planted overclaim with no false positives, and on a first deployment (six manuscripts and one granted patent, at a measured cost of about twenty-four US dollars) the panels are directionally more conservative than the public acceptance record, on a one-sided sample. The framework is decision support, not a replacement for peer review or patent examination, and we report in full where its mechanisms remain unexercised on real inputs.

physics.soc-ph↗

arXiv2026

Qupertino: Pure MLX Array Kernels versus Hand-Tuned Metal Shaders for Quantum Circuit Simulation on Apple Silicon

We present Qupertino, an open-source quantum circuit simulator for Apple Silicon, and use it to ask how far a simulator written purely in MLX array operations can go and what remains for hand-tuned Metal shaders. The framework ships two measured tiers. The pure tier dispatches structured gates to specialized MLX kernels: diagonal gates run as broadcast phase multiplies, controlled gates as masked half-state updates, and SWAP as an axis permutation; a paired dense-path ablation attributes a 25-33x speedup to this dispatch alone. The opt-in shader tier adds hand-written Metal kernels for every structured layer family in our benchmarks, including phase-LUT diagonals, GF(2) affine permutation gathers, fused tensor-product single-qubit layers, radix-4 QFT and Walsh-Hadamard butterflies, and basis-conjugated XX/YY Trotter layers; runtime fusion detectors route work to them while preserving circuit semantics exactly, confirmed by parity tests. In a four-way interleaved campaign on M1 Max (two warmups, ten measured repeats per cell), the shader tier is fastest by mean runtime in all 18 comparison cells against same-machine Qiskit Aer CPU and PennyLane lightning.qubit. At 25 qubits, gate-stream QFT runs in 0.0591 +/- 0.0029 s (paired 47.2x over Aer, 95.3x over PennyLane) and TFIM Trotter evolution in 0.495 +/- 0.035 s (36.2x and 67.2x). Across a 29-workload suite, the shader tier's paired speedup over pure MLX reaches 25x, with 25 of 29 workloads accelerating above parity. The framework also supports variational ansatz workloads, QAOA, QCBM, Trotter-Suzuki Hamiltonian simulation, and OpenQASM 2.0 import (unitary subset); a preliminary MPS backend reaches 150 qubits on limited-entanglement workloads. Correctness rests on 253 Python tests, independent complex128 checks, and a Trotter-error curve against exact diagonalization. State-vector memory remains exponential in qubit count.

quant-ph↗

arXiv2024

QuantumLLMInstruct: A 500k LLM Instruction-Tuning Dataset with Problem-Solution Pairs for Quantum Computing

We present QuantumLLMInstruct (QLMMI), an innovative dataset featuring over 500,000 meticulously curated instruction-following problem-solution pairs designed specifically for quantum computing - the largest and most comprehensive dataset of its kind. Originating from over 90 primary seed domains and encompassing hundreds of subdomains autonomously generated by LLMs, QLMMI marks a transformative step in the diversity and richness of quantum computing datasets. Designed for instruction fine-tuning, QLMMI seeks to significantly improve LLM performance in addressing complex quantum computing challenges across a wide range of quantum physics topics. While Large Language Models (LLMs) have propelled advancements in computational science with datasets like Omni-MATH and OpenMathInstruct, these primarily target Olympiad-level mathematics, leaving quantum computing largely unexplored. The creation of QLMMI follows a rigorous four-stage methodology. Initially, foundational problems are developed using predefined templates, focusing on critical areas such as synthetic Hamiltonians, QASM code generation, Jordan-Wigner transformations, and Trotter-Suzuki quantum circuit decompositions. Next, detailed and domain-specific solutions are crafted to ensure accuracy and relevance. In the third stage, the dataset is enriched through advanced reasoning techniques, including Chain-of-Thought (CoT) and Task-Oriented Reasoning and Action (ToRA), which enhance problem-solution diversity while adhering to strict mathematical standards. Lastly, a zero-shot Judge LLM performs self-assessments to validate the dataset's quality and reliability, minimizing human oversight requirements.

quant-ph↗

arXiv2023

QonFusion -- Quantum Approaches to Gaussian Random Variables: Applications in Stable Diffusion and Brownian Motion

In the present study, we delineate a strategy focused on non-parametric quantum circuits for the generation of Gaussian random variables (GRVs). This quantum-centric approach serves as a substitute for conventional pseudorandom number generators (PRNGs), such as the \textbf{torch.rand} function in PyTorch. The principal theme of our research is the incorporation of Quantum Random Number Generators (QRNGs) into classical models of diffusion. Notably, our Quantum Gaussian Random Variable Generator fulfills dual roles, facilitating simulations in both Stable Diffusion (SD) and Brownian Motion (BM). This diverges markedly from prevailing methods that utilize parametric quantum circuits (PQCs), often in conjunction with variational quantum eigensolvers (VQEs). Although conventional techniques can accurately approximate ground states in complex systems or model elaborate probability distributions, they require a computationally demanding optimization process to tune parameters. Our non-parametric strategy obviates this necessity. To facilitate assimilating our methodology into existing computational frameworks, we put forward QonFusion, a Python library congruent with both PyTorch and PennyLane, functioning as a bridge between classical and quantum computational paradigms. We validate QonFusion through extensive statistical testing, including tests which confirm the statistical equivalence of the Gaussian samples from our quantum approach to classical counterparts within defined significance limits. QonFusion is available at \url{https://boltzmannentropy.github.io/qonfusion.github.io/} to reproduce all findings here.

quant-ph↗

arXiv2023

Using the Julia framework to teach quantum entanglement

Entanglement, a phenomenon that has puzzled scientists since its discovery, has been extensively studied by many researchers through both theoretical and experimental aspect of both quantum information processing (QIP) and quantum mechanics (QM). But how can entanglement be most effectively taught to computer science students compared to applied physics students?. in this educational pursuit, we propose using Yao.jl, a quantum computing framework written in Julia for teaching entanglement to graduate computer science students attending a quantum computing class at Johns Hopkins University. David Mermin's just enough QM for them to understand and develop algorithms in quantum computation [Mer98, Mer03] idea aligns with the purpose of this work. Additionally, the authors of the study Improving students understanding of QM via the Stern-Gerlach experiment (SGE) argue that this experiment should be a key part of any QM education. Here, we explore the concept of entanglement and it's quantification in various quantum information processing experiments, including one inequality-free form of Bell's theorem: (1) Superposition via the Hadamard, (2) Bell-state generation and (3) GHZ state generation. The utilisation of circuit diagrams and code fragmentsis a central theme in this work's philosophy.

physics.ed-ph↗

arXiv2022

A quantum Fourier transform (QFT) based note detection algorithm

In quantum information processing (QIP), the quantum Fourier transform (QFT) has a plethora of applications [1] [2] [3]: Shor's algorithm and phase estimation are just a few well-known examples. Shor's quantum factorization algorithm, one of the most widely quoted quantum algorithms [4] [5] [6] relies heavily on the QFT and efficiently finds integer prime factors of large numbers on quantum computers [4]. This seminal ground-breaking design for quantum algorithms has triggered a cascade of viable alternatives to previously unsolvable problems on a classical computer that are potentially superior and can run in polynomial time. In this work we examine the QFT's structure and implementation for the creation of a quantum music note detection algorithm both on a simulated and a real quantum computer. Though formal approaches [7] [1] [8] [9] exist for the verification of quantum algorithms, in this study we limit ourselves to a simpler, symbolic representation which we validate using the symbolic SymPy [10] [11] package which symbolically replicates quantum computing processes. The algorithm is then implemented as a quantum circuit, using IBM's qiskit [12] library and finally period detection is exemplified on an actual single musical tone using a varying number of qubits.

quant-ph↗

arXiv2022

Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI

The second edition of Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific topics and provide machine learning MSc / PhD. students, and those awaiting an interview a well-organized overview of the field. The problems it poses are tough enough to cut your teeth on and to dramatically improve your skills-but they're framed within thought-provoking questions and engaging stories. That is what makes the volume so specifically valuable to students and job seekers: it provides them with the ability to speak confidently and quickly on any relevant topic, to answer technical questions clearly and correctly, and to fully understand the purpose and meaning of interview questions and answers. Those are powerful, indispensable advantages to have when walking into the interview room. The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs.

cs.LG↗