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In 1998, Brassard, Hoyer, Mosca, and Tapp (BHMT) gave a quantum algorithm for approximate counting. Given a list of $N$ items, $K$ of them marked, their algorithm estimates $K$ to within relative error $varepsilon$ by making only $Oleft( frac{1}{varepsilon}sqrt{frac{N}{K}}right) $ queries. Although this speedup is of Grover type, the BHMT algorithm has the curious feature of relying on the Quantum Fourier Transform (QFT), more commonly associated with Shors algorithm. Is this necessary? This paper presents a simplified algorithm, which we prove achieves the same query complexity using Grover iterations only. We also generalize this to a QFT-free algorithm for amplitude estimation. Related approaches to approximate counting were sketched previously by Grover, Abrams and Williams, Suzuki et al., and Wie (the latter two as we were writing this paper), but in all cases without rigorous analysis.
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Approximate Counting refers to the problem where we are given query access to a function $f : [N] to {0,1}$, and we wish to estimate $K = #{x : f(x) = 1}$ to within a factor of $1+epsilon$ (with high probability), while minimizing the number of queries. In the quantum setting, Approximate Counting can be done with $Oleft(minleft(sqrt{N/epsilon}, sqrt{N/K}/epsilonright)right)$ queries. It has recently been shown that this can be achieved by a simple algorithm that only uses Grover iterations; however the algorithm performs these iterations adaptively. Motivated by concerns of computational simplicity, we consider algorithms that use Grover iterations with limited adaptivity. We show that algorithms using only nonadaptive Grover iterations can achieve $Oleft(sqrt{N/epsilon}right)$ query complexity, which is tight.
We prove tight lower bounds for the following variant of the counting problem considered by Aaronson et al. The task is to distinguish whether an input set $xsubseteq [n]$ has size either $k$ or $k=(1+epsilon)k$. We assume the algorithm has access to * the membership oracle, which, for each $iin [n]$, can answer whether $iin x$, or not; and * the uniform superposition $|psi_xrangle = sum_{iin x} |irangle/sqrt{|x|}$ over the elements of $x$. Moreover, we consider three different ways how the algorithm can access this state: ** the algorithm can have copies of the state $|psi_xrangle$; ** the algorithm can execute the reflecting oracle which reflects about the state $|psi_xrangle$; ** the algorithm can execute the state-generating oracle (or its inverse) which performs the transformation $|0ranglemapsto |psi_xrangle$. Without the second type of resources (related to $|psi_xrangle$), the problem is well-understood, see Brassard et al. The study of the problem with the second type of resources was recently initiated by Aaronson et al. We completely resolve the problem for all values of $1/k le epsilonle 1$, giving tight trade-offs between all types of resources available to the algorithm. Thus, we close the main open problems from Aaronson et al. The lower bounds are proven using variants of the adversary bound by Belovs and employing analysis closely related to the Johnson association scheme.
We study the fundamental design automation problem of equivalence checking in the NISQ (Noisy Intermediate-Scale Quantum) computing realm where quantum noise is present inevitably. The notion of approximate equivalence of (possibly noisy) quantum circuits is defined based on the Jamiolkowski fidelity which measures the average distance between output states of two super-operators when the input is chosen at random. By employing tensor network contraction, we present two algorithms, aiming at different situations where the number of noises varies, for computing the fidelity between an ideal quantum circuit and its noisy implementation. The effectiveness of our algorithms is demonstrated by experimenting on benchmarks of real NISQ circuits. When compared with the state-of-the-art implementation incorporated in Qiskit, experimental results show that the proposed algorithms outperform in both efficiency and scalability.
We study quantum algorithms that are given access to trusted and untrusted quantum witnesses. We establish strong limitations of such algorithms, via new techniques based on Laurent polynomials (i.e., polynomials with positive and negative integer exponents). Specifically, we resolve the complexity of approximate counting, the problem of multiplicatively estimating the size of a nonempty set $S subseteq [N]$, in two natural generalizations of quantum query complexity. Our first result holds in the standard Quantum Merlin--Arthur ($mathsf{QMA}$) setting, in which a quantum algorithm receives an untrusted quantum witness. We show that, if the algorithm makes $T$ quantum queries to $S$, and also receives an (untrusted) $m$-qubit quantum witness, then either $m = Omega(|S|)$ or $T=Omega bigl(sqrt{N/left| Sright| } bigr)$. This is optimal, matching the straightforward protocols where the witness is either empty, or specifies all the elements of $S$. As a corollary, this resolves the open problem of giving an oracle separation between $mathsf{SBP}$, the complexity class that captures approximate counting, and $mathsf{QMA}$. In our second result, we ask what if, in addition to a membership oracle for $S$, a quantum algorithm is also given QSamples -- i.e., copies of the state $left| Srightrangle = frac{1}{sqrt{left| Sright| }} sum_{iin S}|irangle$ -- or even access to a unitary transformation that enables QSampling? We show that, even then, the algorithm needs either $Theta bigl(sqrt{N/left| Sright| }bigr)$ queries or else $Theta bigl(min bigl{left| Sright| ^{1/3}, sqrt{N/left| Sright| }bigr}bigr)$ QSamples or accesses to the unitary. Our lower bounds in both settings make essential use of Laurent polynomials, but in different ways.
With the advent of gravitational wave detectors employing squeezed light, quantum waveform estimation---estimating a time-dependent signal by means of a quantum-mechanical probe---is of increasing importance. As is well known, backaction of quantum measurement limits the precision with which the waveform can be estimated, though these limits can in principle be overcome by quantum nondemolition (QND) measurement setups found in the literature. Strictly speaking, however, their implementation would require infinite energy, as their mathematical description involves Hamiltonians unbounded from below. This raises the question of how well one may approximate nondemolition setups with finite energy or finite-dimensional realizations. Here we consider a finite-dimensional waveform estimation setup based on the quasi-ideal clock and show that the estimation errors due to approximating the QND condition decrease slowly, as a power law, with increasing dimension. As a result, we find that good QND approximations require large energy or dimensionality. We argue that this result can be expected to also hold for setups based on truncated oscillators or spin systems.
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