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Cyclic ozone additives have the potential to significantly increase the specific impulse of rocket fuel, which would lead to greater efficiency and reduced costs for space launches, allowing up to one third more payload per rocket. Although practical attempts to capture this isomer have not been successful, cyclic ozone might be stabilized within confined geometries. However, the required synthetic methods are challenging to design and need theory-driven inputs that exceed the capabilities of classical methods. Quantum computation could enable these calculations, but the hardware requirements for many practical applications are still unclear. We provide a comprehensive analysis of how quantum methods could aid efforts to isolate cyclic ozone using fullerene encapsulation. Our discussion goes beyond formal complexity analysis, offering both logical and physical overhead estimates for determining ground state energies based on quantum phase estimation (QPE). Together, these data outline a plausible scale for realistic, computationally-assisted molecular design efforts using fault-tolerant quantum computation.

Recent estimates indicate that the U.S. Department of Defense spends over \$20 billion USD annually on corrosion-related maintenance. This expenditure is accompanied by a substantial loss in asset readiness, ranging from 10% to 30%. Moreover, the global costs associated with corrosion damage have been estimated at an astonishing \$2.5 trillion USD per year, or approximately 3.4% of global GDP in 2016. This project aims to describe how quantum computers might be leveraged to fundamentally change the way material-environment interactions are modeled for material discovery, selection, and design. This project also seeks to understand the plausibility and utility of replacing portions of classical computing workflows with algorithms optimized for quantum computing hardware. The utility of quantum computers is explored through the lens of two industrially relevant problems: (1) characterizing magnesium alloy corrosion properties in aqueous environments and (2) identifying stable niobium-rich alloys with corrosion resistance at temperatures above 1500K. This paper presents an end-to-end analysis of the complexity of both classical and quantum algorithms used in application workflows. Resource estimates are produced using a custom software package, pyLIQTR, based on the qubitized Quantum Phase Estimation (QPE) algorithm. Estimates for the two aforementioned applications show that industrially-relevant computational models that have the potential to deliver commercial utility require quantum computers with thousands to hundreds of thousands of logical qubits and the ability to execute $10^{13}$ to $10^{19}$ T-gates. These estimates represent an upper bound and motivate continued research into improved quantum algorithms and resource reduction techniques.

A Quantum Computer is a new type of computer which can solve problems such as factoring and database search very efficiently. The usefulness of a quantum computer is limited by the effect of two different types of errors, decoherence and inaccuracies. In this paper we show the results of simulations of a quantum computer which consider both decoherence and inaccuracies. We simulate circuits which factor the numbers 15, 21, 35, and 57 as well as circuits which use database search to solve the circuit satisfaction problem. Our simulations show that the error rate per gate is on the order of 10^-6 for a trapped ion quantum computer whose noise is kept below pi/4096 per gate and with a decoherence rate of 10^-6. This is an important bound because previous studies have shown that a quantum computer can factor more efficiently than a classical computer if the error rate is of order 10^-6., Comment: 12 pages, Presented at the 1st NASA conference on Quantum Computing and Quantum Communication

A Quantum Computer is a new type of computer which can efficiently solve complex problems such as prime factorization. A quantum computer threatens the security of public key encryption systems because these systems rely on the fact that prime factorization is computationally difficult. Errors limit the effectiveness of quantum computers. Because of the exponential nature of quantum com puters, simulating the effect of errors on them requires a vast amount of processing and memory resources. In this paper we describe a parallel simulator which accesses the feasibility of quantum computers. We also derive and validate an analytical model of execution time for the simulator, which shows that parallel quantum computer simulation is very scalable., Comment: 22 pages, Presented at High Performance Computing 1998

Recently Quantum Computation has generated a lot of interest due to the discovery of a quantum algorithm which can factor large numbers in polynomial time. The usefulness of a quantum com puter is limited by the effect of errors. Simulation is a useful tool for determining the feasibility of quantum computers in the presence of errors. The size of a quantum computer that can be simulat ed is small because faithfully modeling a quantum computer requires an exponential amount of storage and number of operations. In this paper we define simulation models to study the feasibility of quantum computers. The most detailed of these models is based directly on a proposed imple mentation. We also define less detailed models which are exponentially less complex but still pro duce accurate results. Finally we show that the two different types of errors, decoherence and inaccuracies, are uncorrelated. This decreases the number of simulations which must be per formed., Comment: 25 pages