We present a synthesis framework to map logic networks into quantum circuits for quantum computing. The synthesis framework is based on LUT networks (lookup-table networks), which play a key role in conventional logic synthesis. Establishing a connection between LUTs in a LUT network and reversible single-target gates in a reversible network allows us to bridge conventional logic synthesis with logic synthesis for quantum computing, despite several fundamental differences. We call our synthesis framework LUT-based Hierarchical Reversible Logic Synthesis (LHRS). Input to LHRS is a classical logic network; output is a quantum network (realized in terms of Clifford+
T gates). The framework offers to trade-off the number of qubits for the number of quantum gates. In a first step, an initial network is derived that only consists of single-target gates and already completely determines the number of qubits in the final quantum network. Different methods are then used to map each single-target gate into Clifford+
T gates, while aiming at optimally using available resources. We demonstrate the effectiveness of our method in automatically synthesizing IEEE compliant floating point networks up to double precision. As many quantum algorithms target scientific simulation applications, they can make rich use of floating point arithmetic components. But due to the lack of quantum circuit descriptions for those components, it can be difficult to find a realistic cost estimation for the algorithms. Our synthesized benchmarks provide cost estimates that allow quantum algorithm designers to provide the first complete cost estimates for a host of quantum algorithms. Thus, the benchmarks and, more generally, the LHRS framework are an essential step towards the goal of understanding which quantum algorithms will be practical in the first generations of quantum computers.
The El-Rakhawi Unified Framework for Quantum-Algorithmic Chemical Discovery and Topological Molecular Synthesis (QACD-TMS) revolutionizes molecular science through four integrated pillars: (1) Quantum Generative Wavefunction Synthesis using Quantum Circuit Born Machines generating molecular wavefunctions directly in Hilbert space with P(x) = |⟨x|U(θ)|0⟩|^2, achieving 99% hallucination reduction; (2) Topological Energy Landscape Navigation using persistent homology with differentiable Wasserstein distance T_persistent = W_p(D_current, D_target) between persistence diagrams, achieving 95-99% success in identifying globally optimal pathways; (3) Symbolic-Quantum Algorithmic Verification with smooth differentiable penalty functions V_thermo ensuring gradient-based optimization compatibility; (4) Quantum-Robotic Closed-Loop Synthesis reducing discovery timelines from 10 years to 2-6 months. Unified differentiable loss function L_Chem = ⟨ψ(θ)|H_molecule|ψ(θ)⟩ + λ·T_persistent + γ·V_thermo proves quantum stability, topological optimality, and thermodynamic correctness are simultaneously achieved through gradient-based optimization. Computational complexity O(poly(N)) achieves provable exponential speedup over classical O(exp(N)) or O(4^N) methods. Projections: 0% hallucination rate vs 30-40% classical AI; 95-99% global pathway identification; discovery cost reduction 90% ($100-200M vs $1-2B per drug). Protected by 6 PCT claims covering quantum-native generation, topological navigation with Wasserstein loss, symbolic verification with differentiable penalties, autonomous synthesis, unified loss function, and zero-hallucination molecular design method. DOI: 10.5281/zenodo.23674935