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Abstract Our study evaluates the limitations and potentials of Quantum Random Access Memory (QRAM) within the principles of quantum physics and relativity. QRAM is crucial for advancing quantum algorithms in fields like linear algebra and machine learning, purported to efficiently manage large data sets with$${{{\mathcal{O}}}}(\log N)$$ $O\left(\log N\right)$circuit depth. However, its scalability is questioned when considering the relativistic constraints on qubits interacting locally. Utilizing relativistic quantum field theory and Lieb–Robinson bounds, we delve into the causality-based limits of QRAM. Our investigation introduces a feasible QRAM model in hybrid quantum acoustic systems, capable of supporting a significant number of logical qubits across different dimensions-up to ~10⁷in 1D, ~10¹⁵to ~10²⁰in 2D, and ~10²⁴in 3D, within practical operation parameters. This analysis suggests that relativistic causality principles could universally influence quantum computing hardware, underscoring the need for innovative quantum memory solutions to navigate these foundational barriers, thereby enhancing future quantum computing endeavors in data science. 

We propose a novel deterministic method for preparing arbitrary quantum states. When our protocol is compiled into CNOT and arbitrary single-qubit gates, it prepares an $N$-dimensional state in depth $O(\log (N\left)\right)$and $\text{spacetime allocation}$(a metric that accounts for the fact that oftentimes some ancilla qubits need not be active for the entire circuit) $O\left(N\right)$, which are both optimal. When compiled into the $\left\{\mathrm{H},\mathrm{S},\mathrm{T},\mathrm{C}\mathrm{N}\mathrm{O}\mathrm{T}\right\}$gate set, we show that it requires asymptotically fewer quantum resources than previous methods. Specifically, it prepares an arbitrary state up to error $ϵ$with optimal depth of $O(\log (N)+\log (1/ϵ\left)\right)$and spacetime allocation $O(N\log (\log \left(N\right)/ϵ\left)\right)$, improving over $O(\log (N)\log (\log \left(N\right)/ϵ\left)\right)$and $O(N\log (N/ϵ\left)\right)$, respectively. We illustrate how the reduced spacetime allocation of our protocol enables rapid preparation of many disjoint states with only constant-factor ancilla overhead – $O\left(N\right)$ancilla qubits are reused efficiently to prepare a product state of $w$ $N$-dimensional states in depth $O(w+\log (N\left)\right)$rather than $O(w\log (N\left)\right)$, achieving effectively constant depth per state. We highlight several applications where this ability would be useful, including quantum machine learning, Hamiltonian simulation, and solving linear systems of equations. We provide quantum circuit descriptions of our protocol, detailed pseudocode, and gate-level implementation examples using Braket. 

Contemporary quantum computers encode and process quantum information in binary qubits (d = 2). How- ever, many architectures include higher energy levels that are left as unused computational resources. We demonstrate a superconducting ququart (d = 4) processor and combine quantum optimal control with efficient gate decompositions to implement high-fidelity ququart gates. We distinguish between viewing the ququart as a generalized four-level qubit and an encoded pair of qubits, and characterize the resulting gates in each case. In randomized benchmarking experiments we observe gate fidelities 95% and identify coherence as the primary limiting factor. Our results validate ququarts as a viable tool for quantum information processing.