A polynomial time and space heuristic algorithm for T-count

• URL: http://arxiv.org/abs/2006.12440v3
• Date: Wed, 6 Oct 2021 18:07:04 GMT
• Title: A polynomial time and space heuristic algorithm for T-count
• Authors: Michele Mosca and Priyanka Mukhopadhyay
• Abstract summary: 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. 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.
• Score: 2.28438857884398
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 $m$.

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