A polynomial time and space heuristic algorithm for T-count

TL;DR

This paper introduces a polynomial time and space heuristic algorithm for minimizing T gates in quantum circuits, significantly improving resource efficiency for fault-tolerant quantum computing.

Contribution

It presents a new classical algorithm for the COUNT-T problem with polynomial complexity in T-count and dimension, and introduces a heuristic method for T-count optimization.

Findings

Polynomial time and space complexity for COUNT-T decision problem

A faster multiplication method reduces overall complexity

Heuristic algorithm achieves polynomial scaling with T-count

Abstract

This work focuses on reducing the physical cost of implementing quantum algorithms when using the state-of-the-art fault-tolerant quantum error correcting codes, in particular, those for which implementing the T gate consumes vastly more resources than the other gates in the gate set. More specifically, we consider the group of unitaries that can be exactly implemented by a quantum circuit consisting of the Clifford+T gate set, a universal gate set. Our primary interest is to compute a circuit for a given $n$-qubit unitary $U$, using the minimum possible number of T gates (called the T-count of unitary $U$). We consider the problem COUNT-T, the optimization version of which aims to find the T-count of $U$. In its decision version the goal is to decide if the T-count is at most some positive integer $m$. Given an oracle for COUNT-T, we can compute a T-count-optimal circuit in time polynomial in the T-count and dimension of $U$. We give a provable classical algorithm that solves COUNT-T (decision) in time $O\left(N^{2(c-1)\lceil\frac{m}{c}\rceil}\text{poly}(m,N)\right)$ and space $O\left(N^{2\lceil\frac{m}{c}\rceil}\text{poly}(m,N)\right)$, where $N=2^n$ and $c\geq 2$. This gives a space-time trade-off for solving this problem with variants of meet-in-the-middle techniques. We also introduce an asymptotically faster multiplication method that shaves a factor of $N^{0.7457}$ off of the overall complexity. Lastly, beyond our improvements to the rigorous algorithm, we give a heuristic algorithm that outputs a T-count-optimal circuit and has space and time complexity $\text{poly}(m,N)$, under some assumptions. While our heuristic method still scales exponentially with the number of qubits (though with a lower exponent, there is a large improvement by going from exponential to polynomial scaling with $m$.