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Paper 1
Universal Quantum Random Access Memory: A Data-Independent Unitary with a Commuting-Projector Hamiltonian
Leonardo Bohac
- Year
- 2025
- Journal
- arXiv preprint
- DOI
- arXiv:2512.12999
- arXiv
- 2512.12999
Quantum random access memory (QRAM) is a central primitive for coherent data access in quantum algorithms, yet it remains controversial in practice because the wall-clock cost of "one lookup" can hide routing depth, control overhead, and geometric constraints. We present a universal QRAM construction (U-QRAM) in which the database is a physical memory register that participates in the lookup unitary as quantum control. This yields a single fixed, data-independent lookup unitary on $A \otimes D \otimes M$ that is correct for all basis-encoded databases. Our first contribution is an explicit, exact Hamiltonian realization: U-QRAM equals a single time-independent evolution $U_{QRAM} = \exp(-iH_{tot})$ where $H_{tot}$ is a sum of mutually commuting projector terms, one per memory cell, and the construction avoids control-dependent phase ambiguities. Our second contribution is an architectural sharpening: under unary (one-hot, mode-addressed) encoding of the address, each cell term becomes uniform and 3-local, delineating the most direct path toward constant-latency interpretations. We keep claims conservative by separating latency from work and stating explicit hardware assumptions required for constant wall-clock queries.
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Quantum Monte Carlo Integration for Simulation-Based Optimisation
Jingjing Cui, Philippe J. S. de Brouwer, Steven Herbert, Philip Intallura, Cahit Kargi, Georgios Korpas, Alexandre Krajenbrink, William Shoosmith, Ifan Williams, Ban Zheng
- Year
- 2024
- Journal
- arXiv preprint
- DOI
- arXiv:2410.03926
- arXiv
- 2410.03926
We investigate the feasibility of integrating quantum algorithms as subroutines of simulation-based optimisation problems with relevance to and potential applications in mathematical finance. To this end, we conduct a thorough analysis of all systematic errors arising in the formulation of quantum Monte Carlo integration in order to better understand the resources required to encode various distributions such as a Gaussian, and to evaluate statistical quantities such as the Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) of an asset. Finally, we study the applicability of quantum Monte Carlo integration for fundamental financial use cases in terms of simulation-based optimisations, notably Mean-Conditional-Value-at-Risk (Mean-CVaR) and (risky) Mean-Variance (Mean-Var) optimisation problems. In particular, we study the Mean-Var optimisation problem in the presence of noise on a quantum device, and benchmark a quantum error mitigation method that applies to quantum amplitude estimation -- a key subroutine of quantum Monte Carlo integration -- showcasing the utility of such an approach.
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