Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Classical Complexity of Boson Sampling
529
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We study the classical complexity of the exact Boson Sampling problem where the objective is to produce provably correct random samples from a particular quantum mechanical distribution. The computational framework was proposed by Aaronson and Arkhipov in 2011 as an attainable demonstration of `quantum supremacy, that is a practical quantum computing experiment able to produce output at a speed beyond the reach of classical (that is non-quantum) computer hardware. Since its introduction Boson Sampling has been the subject of intense international research in the world of quantum computing. On the face of it, the problem is challenging for classical computation. Aaronson and Arkhipov show that exact Boson Sampling is not efficiently solvable by a classical computer unless $P^{#P} = BPP^{NP}$ and the polynomial hierarchy collapses to the third level. The fastest known exact classical algorithm for the standard Boson Sampling problem takes $O({m + n -1 choose n} n 2^n )$ time to produce samples for a system with input size $n$ and $m$ output modes, making it infeasible for anything but the smallest values of $n$ and $m$. We give an algorithm that is much faster, running in $O(n 2^n + operatorname{poly}(m,n))$ time and $O(m)$ additional space. The algorithm is simple to implement and has low constant factor overheads. As a consequence our classical algorithm is able to solve the exact Boson Sampling problem for system sizes far beyond current photonic quantum computing experimentation, thereby significantly reducing the likelihood of achieving near-term quantum supremacy in the context of Boson Sampling.
rate research
Read More
A new algorithm which is called Store-zechin, and utilizes stored data repetitively for calculating the permanent of an n * n matrix is proposed. The analysis manifests that the numbers of multiplications and additions taken by the new algorithm are respectively far smaller than those taken by the famous Ryser algorithm. Especially, for a 5-boson sampling task, the running time of the Store-zechin algorithm computing the correspondent permanent on ENIAC as well as TRADIC is lower than that of the sampling operation on a multiphoton boson sampling machine (shortly MPBSM), and thus MPBSM does not beat the early classical computers (despite of this, it is possible that when n gets large enough, a quantum boson sampling machine will beat a classical computer). On a computer, people can design an algorithm that exchanges space for time while on MPBSM, people can not do so, which is the greatest difference between a universal computer and MPBSM. This difference is right the reason why MPBSM may not be called a (photonic) quantum computer.
Since its introduction Boson Sampling has been the subject of intense study in the world of quantum computing. The task is to sample independently from the set of all $n times n$ submatrices built from possibly repeated rows of a larger $m times n$ complex matrix according to a probability distribution related to the permanents of the submatrices. Experimental systems exploiting quantum photonic effects can in principle perform the task at great speed. In the framework of classical computing, Aaronson and Arkhipov (2011) showed that exact Boson Sampling problem cannot be solved in polynomial time unless the polynomial hierarchy collapses to the third level. Indeed for a number of years the fastest known exact classical algorithm ran in $O({m+n-1 choose n} n 2^n )$ time per sample, emphasising the potential speed advantage of quantum computation. The advantage was reduced by Clifford and Clifford (2018) who gave a significantly faster classical solution taking $O(n 2^n + operatorname{poly}(m,n))$ time and linear space, matching the complexity of computing the permanent of a single matrix when $m$ is polynomial in $n$. We continue by presenting an algorithm for Boson Sampling whose average-case time complexity is much faster when $m$ is proportional to $n$. In particular, when $m = n$ our algorithm runs in approximately $O(ncdot1.69^n)$ time on average. This result further increases the problem size needed to establish quantum computational supremacy via Boson Sampling.
Quantum mechanics promises computational powers beyond the reach of classical computers. Current technology is on the brink of an experimental demonstration of the superior power of quantum computation compared to classical devices. For such a demonstration to be meaningful, experimental noise must not affect the computational power of the device; this occurs when a classical algorithm can use the noise to simulate the quantum system. In this work, we demonstrate an algorithm which simulates boson sampling, a quantum advantage demonstration based on many-body quantum interference of indistinguishable bosons, in the presence of optical loss. Finding the level of noise where this approximation becomes efficient lets us map out the maximum level of imperfections at which it is still possible to demonstrate a quantum advantage. We show that current photonic technology falls short of this benchmark. These results call into question the suitability of boson sampling as a quantum advantage demonstration.
We study the query complexity of determining if a graph is connected with global queries. The first model we look at is matrix-vector multiplication queries to the adjacency matrix. Here, for an $n$-vertex graph with adjacency matrix $A$, one can query a vector $x in {0,1}^n$ and receive the answer $Ax$. We give a randomized algorithm that can output a spanning forest of a weighted graph with constant probability after $O(log^4(n))$ matrix-vector multiplication queries to the adjacency matrix. This complements a result of Sun et al. (ICALP 2019) that gives a randomized algorithm that can output a spanning forest of a graph after $O(log^4(n))$ matrix-vector multiplication queries to the signed vertex-edge incidence matrix of the graph. As an application, we show that a quantum algorithm can output a spanning forest of an unweighted graph after $O(log^5(n))$ cut queries, improving and simplifying a result of Lee, Santha, and Zhang (SODA 2021), which gave the bound $O(log^8(n))$. In the second part of the paper, we turn to showing lower bounds on the linear query complexity of determining if a graph is connected. If $w$ is the weight vector of a graph (viewed as an $binom{n}{2}$ dimensional vector), in a linear query one can query any vector $z in mathbb{R}^{n choose 2}$ and receive the answer $langle z, wrangle$. We show that a zero-error randomized algorithm must make $Omega(n)$ linear queries in expectation to solve connectivity. As far as we are aware, this is the first lower bound of any kind on the unrestricted linear query complexity of connectivity. We show this lower bound by looking at the linear query emph{certificate complexity} of connectivity, and characterize this certificate complexity in a linear algebraic fashion.
Boson sampling is expected to be one of an important milestones that will demonstrate quantum supremacy. The present work establishes the benchmarking of Gaussian boson sampling (GBS) with threshold detection based on the Sunway TaihuLight supercomputer. To achieve the best performance and provide a competitive scenario for future quantum computing studies, the selected simulation algorithm is fully optimized based on a set of innovative approaches, including a parallel scheme and instruction-level optimizing method. Furthermore, data precision and instruction scheduling are handled in a sophisticated manner by an adaptive precision optimization scheme and a DAG-based heuristic search algorithm, respectively. Based on these methods, a highly efficient and parallel quantum sampling algorithm is designed. The largest run enables us to obtain one Torontonian function of a 100 x 100 submatrix from 50-photon GBS within 20 hours in 128-bit precision and 2 days in 256-bit precision.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
