Abstract

Quantum computational supremacy arguments, which describe a way for a quantum computer to perform a task that cannot also be done by a classical computer, typically require some sort of computational assumption related to the limitations of classical computation. One common assumption is that the polynomial hierarchy (PH) does not collapse, a stronger version of the statement thatP≠NP, which leads to the conclusion that any classical simulation of certain families of quantum circuits requires time scaling worse than any polynomial in the size of the circuits. However, the asymptotic nature of this conclusion prevents us from calculating exactly how many qubits these quantum circuits must have for their classical simulation to be intractable on modern classical supercomputers. We refine these quantum computational supremacy arguments and perform such a calculation by imposing fine-grained versions of the non-collapse conjecture. Our first two conjectures poly3-NSETH(a) and per-int-NSETH(b) take specific classical counting problems related to the number of zeros of a degree-3 polynomial innvariables overF2or the permanent of ann×ninteger-valued matrix, and assert that any non-deterministic algorithm that solves them requires2cntime steps, wherec∈{a,b}. A third conjecture poly3-ave-SBSETH(a′) asserts a similar statement about average-case algorithms living in the exponential-time version of the complexity classSBP. We analyze evidence for these conjectures and argue that they are plausible whena=1/2,b=0.999anda′=1/2.Imposing poly3-NSETH(1/2) and per-int-NSETH(0.999), and assuming that the runtime of a hypothetical quantum circuit simulation algorithm would scale linearly with the number of gates/constraints/optical elements, we conclude that Instantaneous Quantum Polynomial-Time (IQP) circuits with 208 qubits and 500 gates, Quantum Approximate Optimization Algorithm (QAOA) circuits with 420 qubits and 500 constraints and boson sampling circuits (i.e. linear optical networks) with 98 photons and 500 optical elements are large enough for the task of producing samples from their output distributions up to constant multiplicative error to be intractable on current technology. Imposing poly3-ave-SBSETH(1/2), we additionally rule out simulations with constant additive error for IQP and QAOA circuits of the same size. Without the assumption of linearly increasing simulation time, we can make analogous statements for circuits with slightly fewer qubits but requiring104to107gates.

Highlights

  • Quantum computational supremacy (QCS) is the goal of carrying out a computational task on a quantum computer that cannot be performed by any classical computer [51]

  • The three families we focus on in this work are Instantaneous Quantum Polynomial-time (IQP) circuits [16, 56], Quantum Approximate Optimization Algorithm (QAOA) circuits [24, 25], and boson sampling circuits [3], all of which are among those whose simulation is hard for the polynomial hierarchy (PH)

  • Like the Instantaneous Quantum Polynomial-Time (IQP) and QAOA models, the linear optical model is not believed to be as powerful as the general quantum circuit model, but under the assumption that the PH does not collapse, it has been shown that classical simulation up to constant multiplicative error requires more than polynomial time [3]

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Summary

Introduction

Quantum computational supremacy (QCS) is the goal of carrying out a computational task on a quantum computer that cannot be performed by any classical computer [51]. A weaker conjecture is the statement that the polynomial hierarchy (PH) does not collapse, which is closely related to the assertion that P = NP Under this assumption, it has been shown that there cannot exist an efficient classical algorithm to produce samples from the output distribution of certain families of quantum circuits [3, 5, 11, 13, 14, 16, 24, 28, 31, 38, 39, 46, 47, 58], up to constant multiplicative error. Assuming poly3-NSETH(a), in Section 3 we derive a fine-grained lower bound on the runtime for any multiplicative-error classical simulation algorithm for QAOA and IQP circuits with n qubits. By comparison, factoring a 1024-bit integer, which is sufficiently beyond the capabilities of today’s classical computers running best known algorithms, has been estimated to require more than 2000 qubits and on the order of 1011 gates using Shor’s algorithm [54]

Counting complexity and quantum computational supremacy
IQP Circuits
Boson sampling circuits
Degree-3 polynomials and the problem poly3-NONBALANCED
The permanent and the problem per-int-NONZERO
For IQP Circuits
For QAOA circuits
For boson sampling circuits
Evidence for conjectures
Overview
Quantum computational supremacy with the complexity class SBP
The problem poly3-SGAP
For IQP circuits
Evidence for average-case conjecture
Our estimate
Relationship to Google’s quantum computational supremacy experiment
Conclusion
B Better-than-brute-force solution to poly3-NONBALANCED
Full Text
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