arXiv subjects

Tamuz Danzig

Publications and source records attributed to Tamuz Danzig.

5 recordsLinked to original sources

arXiv2025

Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading

We present a comprehensive quantum algorithm tailored for pricing autocallable options, offering a full implementation and experimental validation. Our experiments include simulations conducted on high-performance computing (HPC) hardware, along with an empirical analysis of convergence to the classically estimated value. Our key innovation is an improved integration-based exponential amplitude loading technique that reduces circuit depth compared to state-of-the-art approaches. A detailed complexity analysis in a relevant setting shows an approximately 50x reduction in T-depth for the payoff component relative to previous methods. These contributions represent a step toward more efficient quantum approaches to pricing complex financial derivatives.

quant-ph↗

arXiv2025

Quantum Simulation-Based Optimization for Cooling System Design

Engineering design processes involve iterative design evaluations requiring numerous computationally intensive numerical simulations. Quantum algorithms promise substantial speedups for specific tasks relevant to engineering simulations. However, these advantages quickly vanish when considering data input and output on quantum computers. The recently introduced Quantum Simulation-Based Optimization (QuSO) framework circumvents this limitation by treating simulations as subproblems within a larger optimization problem. Here we adapt and implement QuSO for a simplified cooling system design problem, validate correctness in statevector simulations, and present a detailed gate-level complexity analysis for a single QuSO iteration. We express the scaling in terms of problem parameters and QAOA depth and iterations. We show that the cost function can be coherently computed over a superposition of exponentially many configurations using circuits of polynomial complexity. This does not yield a speedup for a single simulation instance, but it enables potential advantages arising from the subsequent QAOA-based search over configurations. The study serves as a proof-of-concept for integrating fault-tolerant quantum subroutines with simulation-based optimization in engineering workflows, clarifying both promise and practical limitations.

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arXiv2024

Design and synthesis of scalable quantum programs

We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together with constraints and objectives on the final program. Advanced synthesis algorithms transform the model into a low-level quantum program that meets the user's specification and is directed at a stipulated hardware. This separation of description from implementation is essential for scale. The technology adapts electronic design automation methods to quantum computing, finding feasible implementations in a virtually unlimited functional space. The results show clear superiority over the compilation and transpilation methods used today. We expect that this technological approach will take over and prevail as quantum software become more demanding, complex, and essential.

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arXiv2024

Hybrid Classical-Quantum Simulation of MaxCut using QAOA-in-QAOA

The Quantum approximate optimization algorithm (QAOA) is a leading hybrid classical-quantum algorithm for solving complex combinatorial optimization problems. QAOA-in-QAOA (QAOA^2) uses a divide-and-conquer heuristic to solve large-scale Maximum Cut (MaxCut) problems, where many subgraph problems can be solved in parallel. In this work, an implementation of the QAOA2 method for the scalable solution of the MaxCut problem is presented, based on the Classiq platform. The framework is executed on an HPE-Cray EX supercomputer by means of the Message Passing Interface (MPI) and the SLURM workload manager. The limits of the Goemans-Williamson (GW) algorithm as a purely classical alternative to QAOA are investigated to understand if QAOA^2 could benefit from solving certain sub-graphs classically. Results from large-scale simulations of up to 33 qubits are presented, showing the advantage of QAOA in certain cases and the efficiency of the implementation, as well as the adequacy of the workflow in the preparation of real quantum devices. For the considered graphs, the best choice for the sub-graphs does not significantly improve results and is still outperformed by GW.

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arXiv2022

Iterative-Free Quantum Approximate Optimization Algorithm Using Neural Networks

The quantum approximate optimization algorithm (QAOA) is a leading iterative variational quantum algorithm for heuristically solving combinatorial optimization problems. A large portion of the computational effort in QAOA is spent by the optimization steps, which require many executions of the quantum circuit. Therefore, there is active research focusing on finding better initial circuit parameters, which would reduce the number of required iterations and hence the overall execution time. While existing methods for parameter initialization have shown great success, they often offer a single set of parameters for all problem instances. We propose a practical method that uses a simple, fully connected neural network that leverages previous executions of QAOA to find better initialization parameters tailored to a new given problem instance. We benchmark state-of-the-art initialization methods for solving the MaxCut problem of Erd\H{o}s-R\'enyi graphs using QAOA and show that our method is consistently the fastest to converge while also yielding the best final result. Furthermore, the parameters predicted by the neural network are shown to match very well with the fully optimized parameters, to the extent that no iterative steps are required, thereby effectively realizing an iterative-free QAOA scheme.

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