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arXiv:2210.11568

Polynomial computational complexity of matrix elements of finite-rank-generated single-particle operators in products of finite bosonic states

Dmitri A. Ivanov

Published Oct 20, 2022• 0 citations
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Abstract

It is known that computing the permanent of the matrix $1+A$, where $A$ is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: an expectation value in a product of a large number of identical bosonic states with a bounded number of bosons. This result complements earlier studies on the computational complexity in boson sampling and related setups. The proposed technique based on the Gaussian averaging is equally applicable to bosonic and fermionic systems. This also allows us to improve an earlier polynomial complexity estimate for the fermionic version of the same problem.

DOI
10.3929/ethz-b-000672351

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Polynomial computational complexity of matrix elements of finite-rank-generated single-particle operators in products of finite bosonic states

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It is known that computing the permanent of the matrix 1+A, where A is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: An expectation value in a product of a large number of identical bosonic states with a bounded number of bosons. This result complements earlier studies on the computational complexity in boson sampling and related setups. The proposed technique based on the Gaussian averaging is equally applicable to bosonic and fermionic systems. This also allows us to improve an earlier polynomial complexity estimate for the fermionic version of the same problem.

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Abstract Since its introduction boson sampling has been the subject of intense study in the world of quantum computing. In the context of Fock-state boson sampling, the task is to sample independently from the set of all n × n submatrices built from possibly repeated rows of a larger m × 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. For 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 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 + 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 Fock 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(n · 1.69 n ) time on average. This result further increases the problem size needed to establish quantum computational advantage via the Fock scheme of boson sampling.

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The permanent is #P-hard to compute exactly on average for natural random matrices including matrices over finite fields or Gaussian ensembles. Should we expect that it remains #P-hard to compute on average if we only care about approximation instead of exact computation? In this work we take a first step towards resolving this question: We present a quasi-polynomial time deterministic algorithm for approximating the permanent of a typical n × n random matrix with unit variance and vanishing mean μ = O(ln ln n)-1/8to within inverse polynomial multiplicative error. (alternatively, one can achieve permanent approximation for matrices with mean μ = 1/polylog(n) in time 2n(ε), for arbitrarily small ε>0). The proposed algorithm significantly extends the regime of matrices for which efficient approximation of the permanent is known. This is because unlike previous algorithms which require a stringent correlation between the signs of the entries of the matrix [1], [2] it can tolerate random ensembles in which this correlation is negligible (albeit non-zero). Among important special cases we note: 1) Biased Gaussian: each entry is a complex Gaussian with unit variance 1 and mean μ. 2) Biased Bernoulli: each entry is -1 + μ with probability 1/2, and 1 with probability 1/2. These results counter the common intuition that the difficulty of computing the permanent, even approximately, stems merely from our inability to treat matrices with many opposing signs. The Gaussian ensemble approaches the threshold of a conjectured hardness [3] of computing the permanent of a zero mean Gaussian matrix. This conjecture is one of the baseline assumptions of the BosonSampling paradigm that has received vast attention in recent years in the context of quantum supremacy experiments. We furthermore show that the permanent of the biased Gaussian ensemble is #P-hard to compute exactly on average. To our knowledge, this is the first natural example of a counting problem that becomes easy only when average case analysis and approximation are combined. On a technical level, our approach stems from a recent approach taken by Barvinok [1], [4], [5], [6] who used Taylor series approximation of the logarithm of a certain univariate polynomial related to the permanent. Our main contribution is to introduce an average-case analysis of such related polynomials. We complement our approach with a new technique for iteratively computing a Taylor series approximation of a function that is analytical in the vicinity of a curve in the complex plane. This method can be viewed as a computational version of analytic continuation in complex analysis.

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Quantum estimation bound of the Gaussian matrix permanent

Joonsuk Huh

Exact calculation and even multiplicative error estimation of matrix permanent are challenging for both classical and quantum computers. Regarding the permanents of random Gaussian matrices, the additive error estimation is closely linked to boson sampling and achieving multiplicative error estimation requires exponentially many samplings. Our formula for matrix permanents and its corresponding quantum expression have enabled better estimation of the average additive error for random Gaussian matrices compared to Gurvits' classical sampling algorithm. The well-known Ryser formula has been converted into a quantum permanent estimator. When dealing with real random Gaussian square matrices of size $N$, the quantum estimator can approximate the matrix permanent with an additive error smaller than $\ensuremath{\epsilon}{(\sqrt{\mathrm{e}N})}^{N}$, where $\ensuremath{\epsilon}$ is the estimation precision. In contrast, Gurvits' classical sampling algorithm has an estimation error of $\ensuremath{\epsilon}{(2\sqrt{N})}^{N}$, which is exponentially larger ($1.{2}^{N}$) than the quantum method. As expected, the quantum additive error bound fails to reach the multiplicative error bound of ${(2\ensuremath{\pi}N)}^{1/4}\ensuremath{\epsilon}{(\sqrt{N/\mathrm{e}})}^{N}$. Additionally, the quantum permanent estimator can be up to quadratically faster than the classical estimator when using quantum phase estimation-based amplitude estimation.

arXiv:2205.01328•Physical Review A•Jan 13, 2025•6 citations
It is known that computing the permanent of the matrix 1+A1+A1+A, where AAA is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: an expectation value in a product of a large number of identical bosonic states with a bounded number of bosons. This result complements earlier studies on the computational complexity in boson sampling and related setups. The proposed technique based on the Gaussian averaging is equally applicable to bosonic and fermionic systems. This also allows us to improve an earlier polynomial complexity estimate for the fermionic version of the same problem.
It is known that computing the permanent of the matrix 1+A, where A is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: An expectation value in a product of a large number of identical bosonic states with a bounded number of bosons. This result complements earlier studies on the computational complexity in boson sampling and related setups. The proposed technique based on the Gaussian averaging is equally applicable to bosonic and fermionic systems. This also allows us to improve an earlier polynomial complexity estimate for the fermionic version of the same problem.
We study the computational complexity of quantum-mechanical expectation values of single-particle operators in bosonic and fermionic multiparticle product states. Such expectation values appear, in particular, in full-counting-statistics problems. Depending on the initial multiparticle product state, the expectation values may be either easy to compute (the required number of operations scales polynomially with the particle number) or hard to compute (at least as hard as a permanent of a matrix). However, if we only consider full counting statistics in a finite number of final single-particle states, then the full-counting-statistics generating function becomes easy to compute in all the analyzed cases. We prove the latter statement for the general case of the fermionic product state and for the single-boson product state (the same as used in the boson-sampling proposal). This result may be relevant for using multiparticle product states as a resource for quantum computing.
The approximate coherent state rank is the minimal number of (classical) coherent states required to approximate a continuous-variable bosonic quantum state and directly relates to the classical complexity of simulating bosonic computations. Despite its importance, little is known about lower bounds on this quantity, even for basic families of states. In this work, we initiate a systematic study of lower bounds on the approximate coherent state rank. Our contributions are as follows. (i) We introduce a technique based on low-rank approximation theory yielding generic lower bounds on the approximate coherent state rank of arbitrary single-mode states. (ii) Using this technique, we find a complete characterization of all single-mode states of finite approximate coherent state rank, and we obtain in particular analytical expressions for the approximate coherent state rank of squeezed states and of finite superpositions of Fock states. (iii) We further show that our single-mode lower bounds can be lifted to multimode lower bounds for finite superpositions of multimode Fock states. (iv) Finally, we prove a super-polynomial lower bound on the approximate coherent state rank of the nnn-mode Fock state ∣1⟩⊗n|1\rangle^{\otimes n}∣1⟩⊗n, by exploiting a connection to the permanent. To do so, we show that the algebraic complexity of approximate multi-linear formulas for the permanent is super-polynomial, building upon the proof of a lower bound for exact formulas due to [Raz, JACM 2009]. Our results establish an unconditional barrier to efficient classical simulation of Boson Sampling via coherent state decompositions and connect non-classicality of bosonic quantum systems to central questions in algebraic complexity.
Abstract Since its introduction boson sampling has been the subject of intense study in the world of quantum computing. In the context of Fock-state boson sampling, the task is to sample independently from the set of all n × n submatrices built from possibly repeated rows of a larger m × 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. For 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 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 + 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 Fock 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(n · 1.69 n ) time on average. This result further increases the problem size needed to establish quantum computational advantage via the Fock scheme of boson sampling.
The permanent is #P-hard to compute exactly on average for natural random matrices including matrices over finite fields or Gaussian ensembles. Should we expect that it remains #P-hard to compute on average if we only care about approximation instead of exact computation? In this work we take a first step towards resolving this question: We present a quasi-polynomial time deterministic algorithm for approximating the permanent of a typical n × n random matrix with unit variance and vanishing mean μ = O(ln ln n)-1/8to within inverse polynomial multiplicative error. (alternatively, one can achieve permanent approximation for matrices with mean μ = 1/polylog(n) in time 2n(ε), for arbitrarily small ε>0). The proposed algorithm significantly extends the regime of matrices for which efficient approximation of the permanent is known. This is because unlike previous algorithms which require a stringent correlation between the signs of the entries of the matrix [1], [2] it can tolerate random ensembles in which this correlation is negligible (albeit non-zero). Among important special cases we note: 1) Biased Gaussian: each entry is a complex Gaussian with unit variance 1 and mean μ. 2) Biased Bernoulli: each entry is -1 + μ with probability 1/2, and 1 with probability 1/2. These results counter the common intuition that the difficulty of computing the permanent, even approximately, stems merely from our inability to treat matrices with many opposing signs. The Gaussian ensemble approaches the threshold of a conjectured hardness [3] of computing the permanent of a zero mean Gaussian matrix. This conjecture is one of the baseline assumptions of the BosonSampling paradigm that has received vast attention in recent years in the context of quantum supremacy experiments. We furthermore show that the permanent of the biased Gaussian ensemble is #P-hard to compute exactly on average. To our knowledge, this is the first natural example of a counting problem that becomes easy only when average case analysis and approximation are combined. On a technical level, our approach stems from a recent approach taken by Barvinok [1], [4], [5], [6] who used Taylor series approximation of the logarithm of a certain univariate polynomial related to the permanent. Our main contribution is to introduce an average-case analysis of such related polynomials. We complement our approach with a new technique for iteratively computing a Taylor series approximation of a function that is analytical in the vicinity of a curve in the complex plane. This method can be viewed as a computational version of analytic continuation in complex analysis.
Exact calculation and even multiplicative error estimation of matrix permanent are challenging for both classical and quantum computers. Regarding the permanents of random Gaussian matrices, the additive error estimation is closely linked to boson sampling and achieving multiplicative error estimation requires exponentially many samplings. Our formula for matrix permanents and its corresponding quantum expression have enabled better estimation of the average additive error for random Gaussian matrices compared to Gurvits' classical sampling algorithm. The well-known Ryser formula has been converted into a quantum permanent estimator. When dealing with real random Gaussian square matrices of size NNN, the quantum estimator can approximate the matrix permanent with an additive error smaller than $\ensuremath{\epsilon}{(\sqrt{\mathrm{e}N})}^{N}$, where $\ensuremath{\epsilon}$ is the estimation precision. In contrast, Gurvits' classical sampling algorithm has an estimation error of $\ensuremath{\epsilon}{(2\sqrt{N})}^{N}$, which is exponentially larger (1.2N1.{2}^{N}1.2N) than the quantum method. As expected, the quantum additive error bound fails to reach the multiplicative error bound of ${(2\ensuremath{\pi}N)}^{1/4}\ensuremath{\epsilon}{(\sqrt{N/\mathrm{e}})}^{N}$. Additionally, the quantum permanent estimator can be up to quadratically faster than the classical estimator when using quantum phase estimation-based amplitude estimation.