is the number of gates in the quantum computation. More precisely, we show that the above problem is #P-hard under BPPNPreduction. In the recent experiments, the quantum circuit has n-qubits and the architecture is a two-dimensional grid of size

\sqrt{n}\times\sqrt{n}

[1]. Indeed for constant depth circuits approximating the output probabilities to within

2^{-\Omega(n\log n)}

is hard. For circuits of depth

\log n

\sqrt{n}

for which the anti-concentration property holds, approximating the output probabilities to within

2^{-\Omega(n\log^{2}n)}

and

2^{-\Omega(n^{3/2}\log n)}

is hard respectively. We then show that the hardness results extend to any open neighborhood of an arbitrary (fixed) circuit including the trivial circuit with identity gates. We made an effort to find the best proofs and proved these results from first principles, which do not use the standard techniques such as the Berlekamp–Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.

DOI: 10.1109/focs52979.2021.00126

Related papers

Improved robustness of quantum supremacy for random circuit sampling

Y. Kondo, Ryuhei Mori, Ramis Movassagh

Motivated by the recent experimental demonstrations of quantum supremacy, proving the hardness of the output of random quantum circuits is an imperative near term goal. We prove under the complexity theoretical assumption of the non-collapse of the polynomial hierarchy that approximating the output probabilities of random quantum circuits to within $\exp(-\Omega(m\log m))$ additive error is hard for any classical computer, where $m$ is the number of gates in the quantum computation. More precisely, we show that the above problem is $\#\mathsf{P}$ -hard under $\mathsf{BPP}^{\mathsf{NP}}$ reduction. In the recent experiments, the quantum circuit has $n$ -qubits and the architecture is a two-dimensional grid of size $\sqrt{n}\times\sqrt{n}$ . Indeed for constant depth circuits approximating the output probabilities to within $2^{-\Omega(n\log{n})}$ is hard. For circuits of depth $\log{n}$ or $\sqrt{n}$ for which the anti-concentration property holds, approximating the output probabilities to within $2^{-\Omega(n\log^2{n})}$ and $2^{-\Omega(n^{3/2}\log n)}$ is hard respectively. We made an effort to find the best proofs and proved these results from first principles, which do not use the standard techniques such as the Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.Motivated by the recent experimental demonstrations of quantum supremacy, proving the hardness of the output of random quantum circuits is an imperative near term goal. We prove under the complexity theoretical assumption of the non-collapse of the polynomial hierarchy that approximating the output probabilities of random quantum circuits to within $\exp(-\Omega(m\log m))$ additive error is hard for any classical computer, where $m$ is the number of gates in the quantum computation. More precisely, we show that the above problem is $\#\mathsf{P}$-hard under $\mathsf{BPP}^{\mathsf{NP}}$ reduction. In the recent experiments, the quantum circuit has $n$-qubits and the architecture is a two-dimensional grid of size $\sqrt{n}\times\sqrt{n}$. Indeed for constant depth circuits approximating the output probabilities to within $2^{-\Omega(n\log{n})}$ is hard. For circuits of depth $\log{n}$ or $\sqrt{n}$ for which the anti-concentration property holds, approximating the output probabilities to within $2^{-\Omega(n\log^2{n})}$ and $2^{-\Omega(n^{3/2}\log n)}$ is hard respectively. We made an effort to find the best proofs and proved these results from first principles, which do not use the standard techniques such as the Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.

Feb 3, 20215 citations

Fine-grained analysis and improved robustness of quantum supremacy for random circuit sampling

Y. Kondo, Ryuhei Mori, Ramis Movassagh

We prove under the complexity theoretical assumption of the non-collapse of the polynomial hierarchy that estimating the output probabilities of random quantum circuits to within $\exp(-\Omega(m\log m))$ additive error is hard for any classical computer, where $m$ is the number of gates in the quantum computation. More precisely, we show that the above problem is $\#\mathsf{P}$ -hard under $\mathsf{BPP}^{\mathsf{NP}}$ reduction. In the recent experiments, the quantum circuit has $n-$ qubits and the architecture is a two-dimensional grid of size $\sqrt{n}\times\sqrt{n}$ . Indeed for constant depth circuits estimating the output probabilities to within $2^{-\Omega(n\log{n})}$ is hard, and for circuits of depth $\sqrt{n}$ , for which the anti-concentration property holds, estimating the output probabilities to within $2^{-\Omega(n^{3/2}\log n)}$ is hard. We prove these results from first principles and do not use the standard techniques such as the Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.We prove under the complexity theoretical assumption of the non-collapse of the polynomial hierarchy that estimating the output probabilities of random quantum circuits to within $\exp(-\Omega(m\log m))$ additive error is hard for any classical computer, where $m$ is the number of gates in the quantum computation. More precisely, we show that the above problem is $\#\mathsf{P}$-hard under $\mathsf{BPP}^{\mathsf{NP}}$ reduction. In the recent experiments, the quantum circuit has $n-$qubits and the architecture is a two-dimensional grid of size $\sqrt{n}\times\sqrt{n}$. Indeed for constant depth circuits estimating the output probabilities to within $2^{-\Omega(n\log{n})}$ is hard, and for circuits of depth $\sqrt{n}$, for which the anti-concentration property holds, estimating the output probabilities to within $2^{-\Omega(n^{3/2}\log n)}$ is hard. We prove these results from first principles and do not use the standard techniques such as the Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.

Feb 3, 20215 citations

Quantum supremacy and random circuits

Ramis Movassagh

As Moore's law reaches its limits, quantum computers are emerging with the promise of dramatically outperforming classical computers. We have witnessed the advent of quantum processors with over $50$ quantum bits (qubits), which are expected to be beyond the reach of classical simulation. Quantum supremacy is the event at which the old Extended Church-Turing Thesis is overturned: A quantum computer performs a task that is practically impossible for any classical (super)computer. The demonstration requires both a solid theoretical guarantee and an experimental realization. The lead candidate is Random Circuit Sampling (RCS), which is the task of sampling from the output distribution of random quantum circuits. Google recently announced a $53-$ qubit experimental demonstration of RCS. Soon after, classical algorithms appeared that challenge the supremacy of random circuits by estimating their outputs. How hard is it to classically simulate the output of random quantum circuits? We prove that estimating the output probabilities of random quantum circuits is formidably hard ( $\#P$ -Hard) for any classical computer. This makes RCS the strongest candidate for demonstrating quantum supremacy relative to all other proposals. The robustness to the estimation error that we prove may serve as a new hardness criterion for the performance of classical algorithms. To achieve this, we introduce the Cayley path interpolation between any two gates of a quantum computation and convolve recent advances in quantum complexity and information with probability and random matrices. Furthermore, we apply algebraic geometry to generalize the well-known Berlekamp-Welch algorithm that is widely used in coding theory and cryptography. Our results imply that there is an exponential hardness barrier for the classical simulation of most quantum circuits.As Moore's law reaches its limits, quantum computers are emerging with the promise of dramatically outperforming classical computers. We have witnessed the advent of quantum processors with over $50$ quantum bits (qubits), which are expected to be beyond the reach of classical simulation. Quantum supremacy is the event at which the old Extended Church-Turing Thesis is overturned: A quantum computer performs a task that is practically impossible for any classical (super)computer. The demonstration requires both a solid theoretical guarantee and an experimental realization. The lead candidate is Random Circuit Sampling (RCS), which is the task of sampling from the output distribution of random quantum circuits. Google recently announced a $53-$qubit experimental demonstration of RCS. Soon after, classical algorithms appeared that challenge the supremacy of random circuits by estimating their outputs. How hard is it to classically simulate the output of random quantum circuits? We prove that estimating the output probabilities of random quantum circuits is formidably hard ($\#P$-Hard) for any classical computer. This makes RCS the strongest candidate for demonstrating quantum supremacy relative to all other proposals. The robustness to the estimation error that we prove may serve as a new hardness criterion for the performance of classical algorithms. To achieve this, we introduce the Cayley path interpolation between any two gates of a quantum computation and convolve recent advances in quantum complexity and information with probability and random matrices. Furthermore, we apply algebraic geometry to generalize the well-known Berlekamp-Welch algorithm that is widely used in coding theory and cryptography. Our results imply that there is an exponential hardness barrier for the classical simulation of most quantum circuits.

arXiv:1909.06210Sep 11, 201923 citations

Average-case hardness of estimating probabilities of random quantum circuits with a linear scaling in the error exponent

Hari Krovi

We consider the hardness of computing additive approximations to output probabilities of random quantum circuits. We consider three random circuit families, namely, Haar random, $p=1$ QAOA, and random IQP circuits. Our results are as follows. For Haar random circuits with $m$ gates, we improve on prior results by showing $\mathsf{coC_=P}$ hardness of average-case additive approximations to an imprecision of $2^{-O(m)}$ . Efficient classical simulation of such problems would imply the collapse of the polynomial hierarchy. For constant depth circuits i.e., when $m=O(n)$ , this linear scaling in the exponent is within a constant of the scaling required to show hardness of sampling. Prior to our work, such a result was shown only for Boson Sampling in Bouland et al (2021). We also use recent results in polynomial interpolation to show $\mathsf{coC_=P}$ hardness under $\mathsf{BPP}$ reductions rather than $\mathsf{BPP}^{\mathsf{NP}}$ reductions. This improves the results of prior work for Haar random circuits both in terms of the error scaling and the power of reductions. Next, we consider random $p=1$ QAOA and IQP circuits and show that in the average-case, it is $\mathsf{coC_=P}$ hard to approximate the output probability to within an additive error of $2^{-O(n)}$ . For $p=1$ QAOA circuits, this work constitutes the first average-case hardness result for the problem of approximating output probabilities for random QAOA circuits, which include Sherrington-Kirkpatrick and Erdös-Renyi graphs. For IQP circuits, a consequence of our results is that approximating the Ising partition function with imaginary couplings to an additive error of $2^{-O(n)}$ is hard even in the average-case, which extends prior work on worst-case hardness of multiplicative approximation to Ising partition functions.We consider the hardness of computing additive approximations to output probabilities of random quantum circuits. We consider three random circuit families, namely, Haar random, $p=1$ QAOA, and random IQP circuits. Our results are as follows. For Haar random circuits with $m$ gates, we improve on prior results by showing $\mathsf{coC_=P}$ hardness of average-case additive approximations to an imprecision of $2^{-O(m)}$. Efficient classical simulation of such problems would imply the collapse of the polynomial hierarchy. For constant depth circuits i.e., when $m=O(n)$, this linear scaling in the exponent is within a constant of the scaling required to show hardness of sampling. Prior to our work, such a result was shown only for Boson Sampling in Bouland et al (2021). We also use recent results in polynomial interpolation to show $\mathsf{coC_=P}$ hardness under $\mathsf{BPP}$ reductions rather than $\mathsf{BPP}^{\mathsf{NP}}$ reductions. This improves the results of prior work for Haar random circuits both in terms of the error scaling and the power of reductions. Next, we consider random $p=1$ QAOA and IQP circuits and show that in the average-case, it is $\mathsf{coC_=P}$ hard to approximate the output probability to within an additive error of $2^{-O(n)}$. For $p=1$ QAOA circuits, this work constitutes the first average-case hardness result for the problem of approximating output probabilities for random QAOA circuits, which include Sherrington-Kirkpatrick and Erdös-Renyi graphs. For IQP circuits, a consequence of our results is that approximating the Ising partition function with imaginary couplings to an additive error of $2^{-O(n)}$ is hard even in the average-case, which extends prior work on worst-case hardness of multiplicative approximation to Ising partition functions.

arXiv:2206.05642Jun 12, 20222 citations

Efficient unitary paths and quantum computational supremacy: A proof of average-case hardness of Random Circuit Sampling

Ramis Movassagh

One-parameter interpolations between any two unitary matrices (e.g., quantum gates) $U_1$ and $U_2$ along efficient paths contained in the unitary group are constructed. Motivated by applications, we propose the continuous unitary path $U(θ)$ obtained from the QR-factorization $U(θ)R(θ)=(1-θ)A+θB,$ where $U_1 R_1=A$ and $U_2 R_2=B$ are the QR-factorizations of $A$ and $B$ , and $U(θ)$ is a unitary for all $θ$ with $U(0)=U_1$ and $U(1)=U_2$ . The QR-algorithm is modified to, instead of $U(θ)$ , output a matrix whose columns are proportional to the corresponding columns of $U(θ)$ and whose entries are polynomial or rational functions of $θ$ . By an extension of the Berlekamp-Welch algorithm we show that rational functions can be efficiently and exactly interpolated with respect to $θ$ . We then construct probability distributions over unitaries that are arbitrarily close to the Haar measure. Demonstration of computational advantages of NISQ over classical computers is an imperative near-term goal, especially with the exuberant experimental frontier in academia and industry (e.g., IBM and Google). A candidate for quantum computational supremacy is Random Circuit Sampling (RCS), which is the task of sampling from the output distribution of a random circuit. The aforementioned mathematical results provide a new way of scrambling quantum circuits and are applied to prove that exact RCS is $\#P$ -Hard on average, which is a simpler alternative to Bouland et al's. (Dis)Proving the quantum supremacy conjecture requires "approximate" average case hardness; this remains an open problem for all quantum supremacy proposals.One-parameter interpolations between any two unitary matrices (e.g., quantum gates) $U_1$ and $U_2$ along efficient paths contained in the unitary group are constructed. Motivated by applications, we propose the continuous unitary path $U(θ)$ obtained from the QR-factorization \[ U(θ)R(θ)=(1-θ)A+θB, \] where $U_1 R_1=A$ and $U_2 R_2=B$ are the QR-factorizations of $A$ and $B$, and $U(θ)$ is a unitary for all $θ$ with $U(0)=U_1$ and $U(1)=U_2$. The QR-algorithm is modified to, instead of $U(θ)$, output a matrix whose columns are proportional to the corresponding columns of $U(θ)$ and whose entries are polynomial or rational functions of $θ$. By an extension of the Berlekamp-Welch algorithm we show that rational functions can be efficiently and exactly interpolated with respect to $θ$. We then construct probability distributions over unitaries that are arbitrarily close to the Haar measure. Demonstration of computational advantages of NISQ over classical computers is an imperative near-term goal, especially with the exuberant experimental frontier in academia and industry (e.g., IBM and Google). A candidate for quantum computational supremacy is Random Circuit Sampling (RCS), which is the task of sampling from the output distribution of a random circuit. The aforementioned mathematical results provide a new way of scrambling quantum circuits and are applied to prove that exact RCS is $\#P$-Hard on average, which is a simpler alternative to Bouland et al's. (Dis)Proving the quantum supremacy conjecture requires "approximate" average case hardness; this remains an open problem for all quantum supremacy proposals.

arXiv:1810.04681Oct 10, 201813 citations

Exponential improvements to the average-case hardness of BosonSampling

Adam Bouland, Ishaun Datta, Bill Fefferman +1 more

BosonSampling and Random Circuit Sampling are important both as a theoretical tool for separating quantum and classical computation, and as an experimental means of demonstrating quantum speedups. Prior works have shown that average-case hardness of sampling follows from certain unproven conjectures about the hardness of computing output probabilities, such as the Permanent-of-Gaussians Conjecture (PGC), which states that $e^{-n \log n-n-O(\log n)}$ additive-error estimates to the output probability of most random BosonSampling experiments are #P-hard. Prior works have only shown weaker average-case hardness results that do not imply sampling hardness. Proving these conjectures has become a central question in quantum complexity. In this work, we show that $e^{-n \log n-n-O\left(n^{\delta}\right)}$ additive-error estimates to output probabilities of most random BosonSampling experiments are #P-hard for any $\delta\gt 0$ , exponentially improving on prior work. In the process, we circumvent all known barrier results for proving PGC. The remaining hurdle to prove PGC is now “merely” to show that the $O\left(n^{\delta}\right)$ in the exponent can be improved to $O(\log n)$ . We also obtain an analogous result for Random Circuit Sampling. We then show, for the first time, a hardness of average-case classical sampling result for BosonSampling, under an anticoncentration conjecture. Specifically, we prove the impossibility of multiplicative-error sampling from random BosonSampling experiments with probability $1-2^{-\tilde{O}\left(N^{1 / 3}\right)}$ for input size N, unless the Polynomial Hierarchy collapses. This exponentially improves upon the state-of-the-art. To do this, we introduce new proof techniques which tolerate exponential loss in the worst-to-average-case reduction. This opens the possibility to show the hardness of average-case sampling without ever proving PGC.BosonSampling and Random Circuit Sampling are important both as a theoretical tool for separating quantum and classical computation, and as an experimental means of demonstrating quantum speedups. Prior works have shown that average-case hardness of sampling follows from certain unproven conjectures about the hardness of computing output probabilities, such as the Permanent-of-Gaussians Conjecture (PGC), which states that $e^{-n \log n-n-O(\log n)}$ additive-error estimates to the output probability of most random BosonSampling experiments are #P-hard. Prior works have only shown weaker average-case hardness results that do not imply sampling hardness. Proving these conjectures has become a central question in quantum complexity. In this work, we show that $e^{-n \log n-n-O\left(n^{\delta}\right)}$ additive-error estimates to output probabilities of most random BosonSampling experiments are #P-hard for any $\delta\gt 0$, exponentially improving on prior work. In the process, we circumvent all known barrier results for proving PGC. The remaining hurdle to prove PGC is now “merely” to show that the $O\left(n^{\delta}\right)$ in the exponent can be improved to $O(\log n)$. We also obtain an analogous result for Random Circuit Sampling. We then show, for the first time, a hardness of average-case classical sampling result for BosonSampling, under an anticoncentration conjecture. Specifically, we prove the impossibility of multiplicative-error sampling from random BosonSampling experiments with probability $1-2^{-\tilde{O}\left(N^{1 / 3}\right)}$ for input size N, unless the Polynomial Hierarchy collapses. This exponentially improves upon the state-of-the-art. To do this, we introduce new proof techniques which tolerate exponential loss in the worst-to-average-case reduction. This opens the possibility to show the hardness of average-case sampling without ever proving PGC.

Annual Symposium on Foundations of Computer ScienceDec 14, 20251 citation

\exp(-\Omega(m\log m))

additive error is hard for any classical computer, where

m

\sqrt{n}\times\sqrt{n}

[1]. Indeed for constant depth circuits approximating the output probabilities to within

2^{-\Omega(n\log n)}

is hard. For circuits of depth

\log n

\sqrt{n}

for which the anti-concentration property holds, approximating the output probabilities to within

2^{-\Omega(n\log^{2}n)}

and

2^{-\Omega(n^{3/2}\log n)}

\exp(-\Omega(m\log m))

additive error is hard for any classical computer, where

m

is the number of gates in the quantum computation. More precisely, we show that the above problem is

\#\mathsf{P}

-hard under

\mathsf{BPP}^{\mathsf{NP}}

reduction. In the recent experiments, the quantum circuit has

n

-qubits and the architecture is a two-dimensional grid of size

\sqrt{n}\times\sqrt{n}

. Indeed for constant depth circuits approximating the output probabilities to within

2^{-\Omega(n\log{n})}

is hard. For circuits of depth

\log{n}

\sqrt{n}

for which the anti-concentration property holds, approximating the output probabilities to within

2^{-\Omega(n\log^2{n})}

and

2^{-\Omega(n^{3/2}\log n)}

is hard respectively. We made an effort to find the best proofs and proved these results from first principles, which do not use the standard techniques such as the Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.

We prove under the complexity theoretical assumption of the non-collapse of the polynomial hierarchy that estimating the output probabilities of random quantum circuits to within

\exp(-\Omega(m\log m))

additive error is hard for any classical computer, where

m

is the number of gates in the quantum computation. More precisely, we show that the above problem is

\#\mathsf{P}

-hard under

\mathsf{BPP}^{\mathsf{NP}}

reduction. In the recent experiments, the quantum circuit has

n-

qubits and the architecture is a two-dimensional grid of size

\sqrt{n}\times\sqrt{n}

. Indeed for constant depth circuits estimating the output probabilities to within

2^{-\Omega(n\log{n})}

is hard, and for circuits of depth

\sqrt{n}

, for which the anti-concentration property holds, estimating the output probabilities to within

2^{-\Omega(n^{3/2}\log n)}

is hard. We prove these results from first principles and do not use the standard techniques such as the Berlekamp--Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.

As Moore's law reaches its limits, quantum computers are emerging with the promise of dramatically outperforming classical computers. We have witnessed the advent of quantum processors with over

50

quantum bits (qubits), which are expected to be beyond the reach of classical simulation. Quantum supremacy is the event at which the old Extended Church-Turing Thesis is overturned: A quantum computer performs a task that is practically impossible for any classical (super)computer. The demonstration requires both a solid theoretical guarantee and an experimental realization. The lead candidate is Random Circuit Sampling (RCS), which is the task of sampling from the output distribution of random quantum circuits. Google recently announced a

53-

qubit experimental demonstration of RCS. Soon after, classical algorithms appeared that challenge the supremacy of random circuits by estimating their outputs. How hard is it to classically simulate the output of random quantum circuits? We prove that estimating the output probabilities of random quantum circuits is formidably hard (

\#P

-Hard) for any classical computer. This makes RCS the strongest candidate for demonstrating quantum supremacy relative to all other proposals. The robustness to the estimation error that we prove may serve as a new hardness criterion for the performance of classical algorithms. To achieve this, we introduce the Cayley path interpolation between any two gates of a quantum computation and convolve recent advances in quantum complexity and information with probability and random matrices. Furthermore, we apply algebraic geometry to generalize the well-known Berlekamp-Welch algorithm that is widely used in coding theory and cryptography. Our results imply that there is an exponential hardness barrier for the classical simulation of most quantum circuits.

We consider the hardness of computing additive approximations to output probabilities of random quantum circuits. We consider three random circuit families, namely, Haar random,

p=1

QAOA, and random IQP circuits. Our results are as follows. For Haar random circuits with

m

gates, we improve on prior results by showing

\mathsf{coC_=P}

hardness of average-case additive approximations to an imprecision of

2^{-O(m)}

. Efficient classical simulation of such problems would imply the collapse of the polynomial hierarchy. For constant depth circuits i.e., when

m=O(n)

, this linear scaling in the exponent is within a constant of the scaling required to show hardness of sampling. Prior to our work, such a result was shown only for Boson Sampling in Bouland et al (2021). We also use recent results in polynomial interpolation to show

\mathsf{coC_=P}

hardness under

\mathsf{BPP}

reductions rather than

\mathsf{BPP}^{\mathsf{NP}}

reductions. This improves the results of prior work for Haar random circuits both in terms of the error scaling and the power of reductions. Next, we consider random

p=1

QAOA and IQP circuits and show that in the average-case, it is

\mathsf{coC_=P}

hard to approximate the output probability to within an additive error of

2^{-O(n)}

. For

p=1

QAOA circuits, this work constitutes the first average-case hardness result for the problem of approximating output probabilities for random QAOA circuits, which include Sherrington-Kirkpatrick and Erdös-Renyi graphs. For IQP circuits, a consequence of our results is that approximating the Ising partition function with imaginary couplings to an additive error of

2^{-O(n)}

is hard even in the average-case, which extends prior work on worst-case hardness of multiplicative approximation to Ising partition functions.

One-parameter interpolations between any two unitary matrices (e.g., quantum gates)

U_1

and

U_2

along efficient paths contained in the unitary group are constructed. Motivated by applications, we propose the continuous unitary path

U(θ)

obtained from the QR-factorization

U(θ)R(θ)=(1-θ)A+θB,

where

U_1 R_1=A

and

U_2 R_2=B

are the QR-factorizations of

A

and

B

, and

U(θ)

is a unitary for all

θ

with

U(0)=U_1

and

U(1)=U_2

. The QR-algorithm is modified to, instead of

U(θ)

, output a matrix whose columns are proportional to the corresponding columns of

U(θ)

and whose entries are polynomial or rational functions of

θ

. By an extension of the Berlekamp-Welch algorithm we show that rational functions can be efficiently and exactly interpolated with respect to

θ

. We then construct probability distributions over unitaries that are arbitrarily close to the Haar measure. Demonstration of computational advantages of NISQ over classical computers is an imperative near-term goal, especially with the exuberant experimental frontier in academia and industry (e.g., IBM and Google). A candidate for quantum computational supremacy is Random Circuit Sampling (RCS), which is the task of sampling from the output distribution of a random circuit. The aforementioned mathematical results provide a new way of scrambling quantum circuits and are applied to prove that exact RCS is

\#P

-Hard on average, which is a simpler alternative to Bouland et al's. (Dis)Proving the quantum supremacy conjecture requires "approximate" average case hardness; this remains an open problem for all quantum supremacy proposals.

e^{-n \log n-n-O(\log n)}

additive-error estimates to the output probability of most random BosonSampling experiments are #P-hard. Prior works have only shown weaker average-case hardness results that do not imply sampling hardness. Proving these conjectures has become a central question in quantum complexity. In this work, we show that

e^{-n \log n-n-O\left(n^{\delta}\right)}

additive-error estimates to output probabilities of most random BosonSampling experiments are #P-hard for any

\delta\gt 0

, exponentially improving on prior work. In the process, we circumvent all known barrier results for proving PGC. The remaining hurdle to prove PGC is now “merely” to show that the

O\left(n^{\delta}\right)

in the exponent can be improved to

O(\log n)

. We also obtain an analogous result for Random Circuit Sampling. We then show, for the first time, a hardness of average-case classical sampling result for BosonSampling, under an anticoncentration conjecture. Specifically, we prove the impossibility of multiplicative-error sampling from random BosonSampling experiments with probability

1-2^{-\tilde{O}\left(N^{1 / 3}\right)}

for input size N, unless the Polynomial Hierarchy collapses. This exponentially improves upon the state-of-the-art. To do this, we introduce new proof techniques which tolerate exponential loss in the worst-to-average-case reduction. This opens the possibility to show the hardness of average-case sampling without ever proving PGC.