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Lucas Gretta

Publications and source records attributed to Lucas Gretta.

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Shor's algorithm requires Fanout

Shor's algorithm is a canonical quantum supremacy target whose core operation relies on the Quantum Fourier Transform (QFT). In this note, we resolve an open question of Fang, Fenner, Green, Homer and Zhang from 2006 by showing that approximating QFT in constant depth, for any $n$-qubit modulus, necessarily requires the $n$-qubit Fanout operation. Formally, let $\mathsf{QFT}_q$ be the gate acting on $n = \lceil \log q \rceil$ qubits that computes the QFT under modulus $q$. It is known that any $n$-qubit $\mathsf{QFT}_q$ can be implemented in constant depth using $\mathsf{FANOUT}_n$, i.e. $\mathsf{QFT}_q \in \mathsf{QAC}^0_f$. We prove the converse by using a $\mathsf{QFT}_q$ gate to construct a state of "non-negligible felinity". Consequently, $\mathsf{QFT}_q \in \mathsf{QAC}^0 \iff \mathsf{FANOUT}_n \in \mathsf{QAC^0}$. In the case of $q = 2^n$, such as in Shor's, we approximate $\mathsf{FANOUT}_n$ using a single $\mathsf{QFT}_{2^n}$ gate and $O(1)$ two-qubit local gates, thus tying the feasibility of realizing Shor's algorithm with NISQ circuits to that of Fanout.

quant-ph

Polylogarithmic-Weight Dicke States in QAC$^0$ and Arbitrary Symmetric States in QAC$^0_f$

An $n$-qubit Dicke state of weight $k$, is the uniform superposition over all $n$-bit strings of Hamming weight $k$. Dicke states are central to quantum algorithms exhibiting speedups, such as Decoded Quantum Interferometry (Jordan et al., \emph{Nature}, 2025). In the NISQ era, quantum hardware is constrained by both depth and locality, motivating the question of which global operations suffice to prepare such states. QAC$^0$, the quantum analogue of AC$^0$, minimally extends local $O(1)$-depth quantum circuits by allowing arbitrary-width Toffoli (reversible AND) gates. We show that Dicke states of $\mathrm{polylog}(n)$ weight can be prepared in QAC$^0$. This gives the first QAC$^0$ construction of any super-constant-weight $n$-qubit Dicke state, since previous constructions relied on the much more powerful FANOUT$_n$ gate. In general, we show that any weight-$k$ Dicke state can be constructed using FANOUT$_{\min(k,n-k)}$ gates. Combined with recent hardness results, this yields a tight characterization: for $k \leq n/2$, a $n$-qubit weight-$k$ Dicke state can be prepared in QAC$^0$ if and only if FANOUT$_k$ $\in$ QAC$^0$. We develop a limited-fanout state-synthesis toolkit for QAC$^0$ that yields further constant-depth, poly$(n)$-ancilla constructions: 1. Every $n$-qubit symmetric state supported on Hamming weight $\leq k$ can be prepared using FANOUT$_k$ gates. 2. Every $O(\log n)$-qubit state can be prepared using quantum random-access memory (QRAM$_n$), which refers to a coherent indexing gate. QRAM$_n$ is a potentially weaker resource than FANOUT$_n$ and can be implemented in QAC$^0_f$.

quant-ph

Parity $\notin$ QAC0 $\iff$ QAC0 is Fourier-Concentrated

A major open problem in understanding shallow quantum circuits (QAC$^0$) is whether they can compute Parity. We show that this question is solely about the Fourier spectrum of QAC$^0$: any QAC$^0$ circuit with non-negligible high-level Fourier mass suffices to exactly compute PARITY in QAC$^0$. Thus, proving a quantum analog of the seminal LMN theorem for AC$^0$ is necessary to bound the quantum circuit complexity of PARITY. In the other direction, LMN does not fully capture the limitations of AC$^0$. For example, despite MAJORITY having $99\%$ of its weight on low-degree Fourier coefficients, no AC$^0$ circuit can non-trivially correlate with it. In contrast, we provide a QAC$^0$ circuit that achieves $(1-o(1))$ correlation with MAJORITY, establishing the first average-case decision separation between AC$^0$ and QAC$^0$. This suggests a uniquely quantum phenomenon: unlike in the classical setting, Fourier concentration may largely characterize the power of QAC$^0$. PARITY is also known to be equivalent in QAC$^0$ to inherently quantum tasks such as preparing GHZ states to high fidelity. We extend this equivalence to a broad class of state-synthesis tasks. We demonstrate that existing metrics such as trace distance, fidelity, and mutual information are insufficient to capture these states and introduce a new measure, felinity. We prove that preparing any state with non-negligible felinity, or derived states such as poly(n)-weight Dicke states, implies PARITY $\in$ QAC$^0$.

quant-ph

More Efficient $k$-wise Independent Permutations from Random Reversible Circuits via log-Sobolev Inequalities

We prove that the permutation computed by a reversible circuit with $\tilde{O}(nk\cdot \log(1/\varepsilon))$ random $3$-bit gates is $\varepsilon$-approximately $k$-wise independent. Our bound improves on currently known bounds in the regime when the approximation error $\varepsilon$ is not too small. We obtain our results by analyzing the log-Sobolev constants of appropriate Markov chains rather than their spectral gaps.

cs.CC

Sharp Noisy Binary Search with Monotonic Probabilities

We revisit the noisy binary search model of Karp and Kleinberg, in which we have $n$ coins with unknown probabilities $p_i$ that we can flip. The coins are sorted by increasing $p_i$, and we would like to find where the probability crosses (to within $\varepsilon$) of a target value $\tau$. This generalized the fixed-noise model of Burnashev and Zigangirov , in which $p_i = \frac{1}{2} \pm \varepsilon$, to a setting where coins near the target may be indistinguishable from it. Karp and Kleinberg showed that $\Theta(\frac{1}{\varepsilon^2} \log n)$ samples are necessary and sufficient for this task. We produce a practical algorithm by solving two theoretical challenges: high-probability behavior and sharp constants. We give an algorithm that succeeds with probability $1-\delta$ from \[ \frac{1}{C_{\tau, \varepsilon}} \cdot \left(\lg n + O(\log^{2/3} n \log^{1/3} \frac{1}{\delta} + \log \frac{1}{\delta})\right) \] samples, where $C_{\tau, \varepsilon}$ is the optimal such constant achievable. For $\delta > n^{-o(1)}$ this is within $1 + o(1)$ of optimal, and for $\delta \ll 1$ it is the first bound within constant factors of optimal.

cs.DS