Aleksandrs Belovs

Quantum query complexity has several nice properties with respect to composition. First, bounded-error quantum query algorithms can be composed without incurring log factors through error reduction (emphexactness). Second, through careful accounting (emphthriftiness), the total query complexity is smaller if subroutines are mostly run on cheaper inputs – a property that is much less obvious in quantum algorithms than in their classical counterparts. While these properties were previously seen through the model of span programs (alternatively, the dual adversary bound), a recent work by two of the authors (Belovs, Yolcu 2023) showed how to achieve these benefits without converting to span programs, by defining emphquantum Las Vegas query complexity. Independently, recent works, including by one of the authors (Jeffery 2022), have worked towards bringing thriftiness to the more practically significant setting of quantum emphtime complexity. In this work, we show how to achieve both exactness and thriftiness in the setting of time complexity. We generalize the quantum subroutine composition results of Jeffery 2022 so that, in particular, no error reduction is needed. We give a time complexity version of the well-known result in quantum query complexity, Q(fcirc g)=ØO(Q(f)cdot Q(g)), without log factors. We achieve this by employing a novel approach to the design of quantum algorithms based on what we call emphtransducers, and which we think is of large independent interest. While a span program is a completely different computational model, a transducer is a direct generalisation of a quantum algorithm, which allows for much greater transparency and control. Transducers naturally characterize general state conversion, rather than only decision problems; provide a very simple treatment of other quantum primitives such as quantum walks; and lend themselves well to time complexity analysis.

We prove tight lower bounds for the following variant of the counting problem considered by Aaronson \etal. The task is to distinguish whether an input set $x\subseteq [n]$ has size either $k$ or $k’=(1+\epsilon)k$. We assume the algorithm has access to

We study how efficiently a k-element set S subseteq [n] can be learned from a uniform superposition ket{S} of its elements. One can think of ket{S}=sum_{i in S} ket{i}/sqrt{|S|} as the quantum version of a uniformly random sample over S, as in the classical analysis of the “coupon collector problem.” We show that if k is close to n, then we can learn S using asymptotically fewer quantum samples than random samples. In particular, if there are n-k=O(1) missing elements then O(k) copies of ket{S} suffice, in contrast to the Theta(k log k) random samples needed by a classical coupon collector. On the other hand, if n-k=Omega(k), then Omega(k log k) quantum samples are necessary. More generally, we give tight bounds on the number of quantum samples needed for every k and n, and we give efficient quantum learning algorithms. We also give tight bounds in the model where we can additionally reflect through ket{S}. Finally, we relate coupon collection to a known example separating proper and improper PAC learning that turns out to show no separation in the quantum case.