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arXiv:1703.00874

Optimized Aaronson-Gottesman stabilizer circuit simulation through quantum circuit transformations

Maslov, Dmitri

Published Mar 2, 2017• 0 citations
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Abstract

In this paper we improve the layered implementation of arbitrary stabilizer circuits introduced by Aaronson and Gottesman in {\it Phys. Rev. A 70(052328)}, 2004. In particular, we reduce their 11-stage computation -H-C-P-C-P-C-H-P-C-P-C- into an 8-stage computation of the form -H-C-CZ-P-H-P-CZ-C-. We show arguments in support of using -CZ- stages over the -C- stages: not only the use of -CZ- stages allows a shorter layered expression, but -CZ- stages are simpler and appear to be easier to implement compared to the -C- stages. Relying on the 8-stage decomposition we develop a two-qubit depth-$(14n-4)$ implementation of stabilizer circuits over the gate library {P,H,CNOT}, executable in the LNN architecture, improving best previously known depth-$25n$ circuit, also executable in the LNN architecture. Our constructions rely on folding arbitrarily long sequences $($-P-C-$)^m$ into a 3-stage computation -P-CZ-C-, as well as efficient implementation of the -CZ- stage circuits.

DOI
10.48550/arxiv.1703.00874

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In this paper we improve the layered implementation of arbitrary stabilizer circuits introduced by Aaronson and Gottesman in {\it Phys. Rev. A 70(052328)}, 2004. In particular, we reduce their 11-stage computation -H-C-P-C-P-C-H-P-C-P-C- into an 8-stage computation of the form -H-C-CZ-P-H-P-CZ-C-. We show arguments in support of using -CZ- stages over the -C- stages: not only the use of -CZ- stages allows a shorter layered expression, but -CZ- stages are simpler and appear to be easier to implement compared to the -C- stages. Relying on the 8-stage decomposition we develop a two-qubit depth-(14n−4)(14n-4)(14n−4) implementation of stabilizer circuits over the gate library {P,H,CNOT}, executable in the LNN architecture, improving best previously known depth-25n25n25n circuit, also executable in the LNN architecture. Our constructions rely on folding arbitrarily long sequences (((-P-C-)m)^m)m into a 3-stage computation -P-CZ-C-, as well as efficient implementation of the -CZ- stage circuits.
In this paper, we improve the layered implementation of arbitrary stabilizer circuits introduced by Aaronson and Gottesman in Phys. Rev. A 70 (052328), 2004: to implement a general stabilizer circuit, we reduce their 11-stage computation -H-C-P-C-P-C-H-P-C-P-Cover the gate set consisting of Hadamard, controlled-NOT, and phase gates, into a 7-stage computation of the form -C-CZ-P-H-P-CZ-C-. We show arguments in support of using -CZ- stages over the -C- stages: not only the use of -CZ- stages allows a shorter layered expression, but -CZ- stages are simpler and appear to be easier to implement compared to the -C- stages. Based on this decomposition, we develop a two-qubit gate depth(14n-4) implementation of stabilizer circuits over the gate library {H, P, CNOT}, executable in the Linear Nearest Neighbor (LNN) architecture, improving best previously known depth25n circuit, also executable in the LNN architecture. Our constructions rely on Bruhat decomposition of the symplectic group and on folding arbitrarily long sequences of the form (-P-C-)minto a three-stage computation -P-CZ-C-. Our results include the reduction of the 11-stage decomposition -H-C-P-C-PC-H-P-C-P-Cinto a 9-stage decomposition of the form -C-P-C-PH-C-P-C-P-. This reduction is based on the Bruhat decomposition of the symplectic group. This result also implies a new normal form for stabilizer circuits. We show that a circuit in this normal form is optimal in the number of Hadamard gates used. We also show that the normal form has an asymptotically optimal number of parameters.
We start by studying the subgroup structures underlying stabilizer circuits. Then we apply our results to provide two normal forms for stabilizer circuits. These forms are computed by induction using simple conjugation rules in the Clifford group and our algorithms do not rely on a special decomposition in the symplectic group. The first normal form has shape CX-CZ-P-Z-X-H-CZ-P-H, where CX (resp. CZ) denotes a layer of CNOT (resp. controlled-Z) gates, P a layer of phase gates, X (resp. Z) a layer of Pauli-X (resp. Pauli-Z) gates. Then we replace most of the controlled-Z gates by CNOT gates to obtain a second normal form of type P-CX-CZ-CX-Z-X-H-CZ-CX-P-H. In this second form, both controlled-Z layers have depth 1 and together contain therefore at most n controlled-Z gates. We also consider normal forms for stabilizer states and graph states. Finally we carry out a few tests on classical and quantum computers in order to show experimentally the utility of these normal forms to reduce the gate count of a stabilizer circuit.
We start by studying the subgroup structures underlying stabilizer circuits and we use our results to propose a new normal form for stabilizer circuits. This normal form is computed by induction using simple conjugation rules in the Clifford group. It has shape CX-CZ-P-H-CZ-P-H, where CX (resp. CZ) denotes a layer of $\cnot$ (resp. $\cz$) gates, P a layer of phase gates and H a layer of Hadamard gates. Then we consider a normal form for stabilizer states and we show how to reduce the two-qubit gate count in circuits implementing graph states. Finally we carry out a few numerical tests on classical and quantum computers in order to show the practical utility of our methods. All the algorithms described in the paper are implemented in the C language as a Linux command available on GitHub.
Simulation of quantum information processing remains a major challenge with important applications in quantum computer science and engineering. Generic quantum-circuit simulation appears intractable for conventional computers and may be unnecessary because useful quantum circuits exhibit significant structure that can be exploited during simulation. For example, Gottesman and Knill identified an important subclass, called stabilizer circuits, which can be simulated efficiently using the Heisenberg representation for quantum computers. Stabilizer circuits are exclusively composed of stabilizer gates -- Hadamard, Phase and CNOT -- followed by one-qubit measurements in the computational basis. Such circuits are applied to a computational-basis state and produce so-called stabilizer states. Aaronson and Gottesman generalized stabilizer-circuit simulation to additionally handle a small number of non-stabilizer gates. We design new, more efficient data structures and algorithms for such beyond-stabilizer simulation using superpositions of stabilizer states. One such data structure, a stabilizer frame, offers more compact storage than previous approaches but require additional algorithms to maintain the global phases of each state in the superposition. To explore the advantages and limitations of our technique, we analyze the geometric structure of stabilizer states and their embedding in Hilbert space. Our analysis includes results on the computational geometry of stabilizer states such as efficient algorithms for computing distances, angles and volumes between them. The main advantages of using stabilizer-state superpositions to simulate quantum circuits are: (i) stabilizer subcircuits are simulated with high efficiency, (ii) superpositions can be restructured and compressed on the fly during simulation to reduce resource requirements, and (iii) operations performed on such superpositions lend themselves to distributed or asynchronous processing. Our software implementation, called Quipu, simulates certain quantum arithmetic circuits (e.g., reversible ripple-carry adders) and quantum Fourier transform circuits in polynomial time and space for specific input states. On such instances, known linear-algebraic simulation techniques, such as the (state-of-the-art) BDD-based simulator QuIDDPro, take exponential time. We simulate quantum fault-tolerant circuits using Quipu, and the results indicate that our stabilizer-based technique empirically outperforms QuIDDPro in all cases. While previous structure-aware simulations of quantum circuits were difficult to parallellize, we demonstrate a parallel version of Quipu that achieves a nontrivial speedup.
The Gottesman-Knill theorem says that a stabilizer circuit---that is, a quantum circuit consisting solely of controlled-NOT (CNOT), Hadamard, and phase gates---can be simulated efficiently on a classical computer. This paper improves that theorem in several directions. First, by removing the need for Gaussian elimination, we make the simulation algorithm much faster at the cost of a factor of 2 increase in the number of bits needed to represent a state. We have implemented the improved algorithm in a freely available program called CHP (CNOT-Hadamard-phase), which can handle thousands of qubits easily. Second, we show that the problem of simulating stabilizer circuits is complete for the classical complexity class $\ensuremath{\bigoplus}\mathsf{L}$, which means that stabilizer circuits are probably not even universal for classical computation. Third, we give efficient algorithms for computing the inner product between two stabilizer states, putting any nnn-qubit stabilizer circuit into a ``canonical form'' that requires at most O(n2∕logn)O({n}^{2}∕\mathrm{log}\phantom{\rule{0.2em}{0ex}}n)O(n2∕logn) gates, and other useful tasks. Fourth, we extend our simulation algorithm to circuits acting on mixed states, circuits containing a limited number of nonstabilizer gates, and circuits acting on general tensor-product initial states but containing only a limited number of measurements.