We find a scaling reduction in the stabilizer rank of the twelve-qubit tensored
T gate magic state. This lowers its asymptotic bound to
2∼0.463t for multi-Pauli measurements on
t magic states, improving over the best previously found bound of
2∼0.468t. We numerically demonstrate this reduction. This constructively produces the most efficient strong simulation algorithm of the Clifford+
T gateset to relative or multiplicative error. We then examine the cost of Pauli measurement in terms of its Gauss sum rank, which is a slight generalization of the stabilizer rank and is a lower bound on its asymptotic scaling. We demonstrate that this lower bound appears to be tight at low
t-counts, which suggests that the stabilizer rank found at the twelve-qubit state can be lowered further to
2∼0.449t and we prove and numerically show that this is the case for single-Pauli measurements. Our construction directly shows how the reduction at
12 qubits is iteratively based on the reduction obtained at
6,
3,
2, and
1 qubits. This explains why novel reductions are found at tensor factors for these number of qubit primitives, an explanation lacking previously in the literature. Furthermore, in the process we observe an interesting relationship between the T gate magic state's stabilizer rank and decompositions that are Clifford-isomorphic to a computational sub-basis tensored with single-qubit states that produce minimal unique stabilizer state inner products -- the same relationship that allowed for finding minimal numbers of unique Gauss sums in the odd-dimensional qudit Wigner formulation of Pauli measurements.
One of the state of the art techniques for classically simulating quantum circuits relies on approximating the output state of the circuit by a superposition of stabilizer states. \nIf the number of non-Clifford gates in the circuit is small, such simulations can be very effective. \nThis thesis provides various improvements in this framework. First, we describe an improved method of computing approximate stabilizer decompositions, which reduces the time cost of computing a single term in the decomposition from
O(elln2) to
O(mn2), where
ell is the total number of gates in the circuit, and
m is the number of non-Clifford gates. \nSince this subroutine has to be repeated exponentially many times, this improvement can be significant in practice whenever
ellggm. \nOur method uses a certain re-writing of the circuit, which in some cases allows for a significant amelioration of the exponential scaling of the required classical resources. \n \nFurthermore, we describe a method of constructing exact, low-rank stabilizer decompositions of
ketpsiotimesm, where
ketpsi is either a magic state or an equatorial state. \nFor any single qubit magic state
ketpsi, we find stabilizer decompositions of
ketpsiotimesm with
2mlog2(3)/4 terms. This improves on the best known bound of
2mlog2(7)/6. \nSimilarly, for any single qubit equatorial state
ketpsi, we give a stabilizer decomposition of
ketpsiotimesm with
2m/2 terms. \nTo our knowledge no such decompositions were previously known. \nThese results translate to milder exponential scaling of the classical resources required for estimating probabilities of quantum circuits up to a polynomially small multiplicative error, as well as allowing more types of circuits to be simulated in this way. \n \nWe also consider certain obstructions to classical simulations. \nIt has been argued in various contexts that contextuality and non-locality hamper classical simulations of quantum circuits. \nLinear constraint systems (LCSs) are a generalization of the well-known Peres-Mermin magic square, which has been recently used to prove a separation between the power of constant depth classical and quantum circuits. \n \nWhile binary LCSs have been studied in detail,
d-ary LCSs are less well-understood. In this thesis we consider linear constraint systems modulo
d>2. We give a simple proof, of the previously known fact, that any linear constraint system which admits a quantum solution consisting of generalized Pauli observables in odd dimension must be classically satisfiable. We further prove that, for odd
d, if a Pauli-like commutation relation between two variables in the LCS arises, then it has no quantum solutions in any dimensions, in stark contrast to the even
d case. We apply this result to various examples, for instance showing that many generalizations of the Peres-Mermin magic square do not give rise to a quantum vs. classical satisfiability gap.
The approximate stabilizer rank of a quantum state is the minimum number of terms in any approximate decomposition of that state into stabilizer states. Bravyi and Gosset showed that the approximate stabilizer rank of a so-called “magic” state like |T⟩⊗ n, up to polynomial factors, is an upper bound on the number of classical operations required to simulate an arbitrary quantum circuit with Clifford gates and n number of T gates. As a result, an exponential lower bound on this quantity seems inevitable. Despite this intuition, several attempts using various techniques could not lead to a better than a linear lower bound on the “exact” rank of |T⟩⊗ n, meaning the minimal size of a decomposition that exactly produces the state. For the “approximate” rank, which is more realistically related to the cost of simulating quantum circuits, no lower bound better than Ω(√n) has been known. In this paper, we improve the lower bound on the approximate rank to Ω(n2) for a wide range of the approximation parameters. An immediate corollary of our result is the existence of polynomial time computable functions which require a super-linear number of terms in any decomposition into exponentials of quadratic forms over F2, resolving a question by Williams. Our approach is based on a strong lower bound on the approximate rank of a quantum state sampled from the Haar measure, a step-by-step analysis of the approximate rank of a magic-state teleportation protocol to sample from the Haar measure, and a result about trading Clifford operations with T gates.
In this work we improve the runtime of recent classical algorithms for strong simulation of quantum circuits composed of Clifford and T gates. The improvement is obtained by establishing a new upper bound on the stabilizer rank of m copies of the magic state | T ⟩ = 2 − 1 ( | 0 ⟩ + e i π / 4 | 1 ⟩ ) in the limit of large m . In particular, we show that | T ⟩ ⊗ m can be exactly expressed as a superposition of at most O ( 2 α m ) stabilizer states, where α ≤ 0.3963 , improving on the best previously known bound α ≤ 0.463 . This furnishes, via known techniques, a classical algorithm which approximates output probabilities of an n -qubit Clifford + T circuit U with m uses of the T gate to within a given inverse polynomial relative error using a runtime p o l y ( n , m ) 2 α m . We also provide improved upper bounds on the stabilizer rank of symmetric product states | ψ ⟩ ⊗ m more generally; as a consequence we obtain a strong simulation algorithm for circuits consisting of Clifford gates and m instances of any (fixed) single-qubit Z -rotation gate with runtime poly ( n , m ) 2 m / 2 . We suggest a method to further improve the upper bounds by constructing linear codes with certain properties.
Recent work has explored using the stabilizer formalism to classically simulate quantum circuits containing a few non-Clifford gates. The computational cost of such methods is directly related to the notion of s t a b i l i z e r rank , which for a pure state ψ is defined to be the smallest integer χ such that ψ is a superposition of χ stabilizer states. Here we develop a comprehensive mathematical theory of the stabilizer rank and the related approximate stabilizer rank. We also present a suite of classical simulation algorithms with broader applicability and significantly improved performance over the previous state-of-the-art. A new feature is the capability to simulate circuits composed of Clifford gates and arbitrary diagonal gates, extending the reach of a previous algorithm specialized to the Clifford+T gate set. We implemented the new simulation methods and used them to simulate quantum algorithms with 40-50 qubits and over 60 non-Clifford gates, without resorting to high-performance computers. We report a simulation of the Quantum Approximate Optimization Algorithm in which we process superpositions of χ ∼ 10 6 stabilizer states and sample from the full n -bit output distribution, improving on previous simulations which used ∼ 10 3 stabilizer states and sampled only from single-qubit marginals. We also simulated instances of the Hidden Shift algorithm with circuits including up to 64 T gates or 16 CCZ gates; these simulations showcase the performance gains available by optimizing the decomposition of a circuit's non-Clifford components.
Recent work has explored using the stabilizer formalism to classically simulate quantum circuits containing a few non-Clifford gates. The computational cost of such methods is directly related to the notion of \ns\nt\na\nb\ni\nl\ni\nz\ne\nr\n \nrank\n, which for a pure state \nψ\n is defined to be the smallest integer \nχ\n such that \nψ\n is a superposition of \nχ\n stabilizer states. Here we develop a comprehensive mathematical theory of the stabilizer rank and the related approximate stabilizer rank. We also present a suite of classical simulation algorithms with broader applicability and significantly improved performance over the previous state-of-the-art. A new feature is the capability to simulate circuits composed of Clifford gates and arbitrary diagonal gates, extending the reach of a previous algorithm specialized to the Clifford+T gate set. We implemented the new simulation methods and used them to simulate quantum algorithms with 40-50 qubits and over 60 non-Clifford gates, without resorting to high-performance computers. We report a simulation of the Quantum Approximate Optimization Algorithm in which we process superpositions of \nχ\n∼\n10\n6\n stabilizer states and sample from the full \nn\n-bit output distribution, improving on previous simulations which used \n∼\n10\n3\n stabilizer states and sampled only from single-qubit marginals. We also simulated instances of the Hidden Shift algorithm with circuits including up to 64 \nT\n gates or 16 CCZ gates; these simulations showcase the performance gains available by optimizing the decomposition of a circuit's non-Clifford components.