Miklos Santha

We explore the power of the unbounded Fan-Out gate and the Global Tunable gates generated by Ising-type Hamiltonians in constructing constant-depth quantum circuits, with particular attention to quantum memory devices. We propose two types of constant-depth constructions for implementing Uniformly Controlled Gates. These gates include the Fan-In gates defined by x>|b> —> |x>|b+ f(x)> for x in 0,1^n and b in 0,1, where f is a Boolean function. The first of our constructions is based on computing the one-hot encoding of the control register |x>, while the second is based on Boolean analysis and exploits different representations of f such as its Fourier expansion. Via these constructions, we obtain constant-depth circuits for the quantum counterparts of read-only and read-write memory devices — Quantum Random Access Memory (QRAM) and Quantum Random Access Gate (QRAG) — of memory size n. The implementation based on one-hot encoding requires either O(n log(n)łogłog(n)) ancillae and O(n log(n)) Fan-Out gates or O(n log(n)) ancillae and 6 Global Tunable gates. On the other hand, the implementation based on Boolean analysis requires only 2 Global Tunable gates at the expense of O(n^2) ancillae.

Abstract Let $G$ be an $n$-vertex graph with $m$ edges. When asked a subset $S$ of vertices, a cut query on $G$ returns the number of edges of $G$ that have exactly one endpoint in $S$. We show that there is a bounded-error quantum algorithm that determines all connected components of $G$ after making $O(\log(n)^6)$ many cut queries. In contrast, it follows from results in communication complexity that any randomized algorithm even just to decide whether the graph is connected or not must make at least $\Omega(n/\log(n))$ many cut queries. We further show that with $O(\log(n)^8)$ many cut queries a quantum algorithm can with high probability output a spanning forest for $G$. En route to proving these results, we design quantum algorithms for learning a graph using cut queries. We show that a quantum algorithm can learn a graph with maximum degree $d$ after $O(d \log(n)^2)$ many cut queries, and can learn a general graph with $O(\sqrt{m} \log(n)^{3/2})$ many cut queries. These two upper bounds are tight up to the poly-logarithmic factors, and compare to $\Omega(dn)$ and $\Omega(m/\log(n))$ lower bounds on the number of cut queries needed by a randomized algorithm for the same problems, respectively. The key ingredients in our results are the Bernstein-Vazirani algorithm, approximate counting with ``OR queries'', and learning sparse vectors from inner products as in compressed sensing.