Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers

Pozzi, Matteo G.; Herbert, Steven; Sengupta, Akash; Mullins, Robert

doi:10.48550/arxiv.2007.15957

Cited by 8 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DRL has recently been deployed for a variety of quantum control problems using numerical simulation [46], including both theoretical gate optimization [37,47,48] and other tasks [49][50][51][52][53][54][55][56][57]. In these simulation based studies it was possible to make at least one of the following strong assumptions: the system suffers zero noise or has a deterministic error model; controls are perfect and instantaneous; and quantum states are completely observable across time.…”

Section: Optimized Quantum Logic Design With Deep Reinforcement Learningmentioning

confidence: 99%

Experimental Deep Reinforcement Learning for Error-Robust Gate-Set Design on a Superconducting Quantum Computer

et al. 2021

View full text Add to dashboard Cite

Quantum computers promise tremendous impact across applications -and have shown great strides in hardware engineering -but remain notoriously error prone. Careful design of low-level controls has been shown to compensate for the processes which induce hardware errors, leveraging techniques from optimal and robust control. However, these techniques rely heavily on the availability of highly accurate and detailed physical models which generally only achieve sufficient representative fidelity for the most simple operations and generic noise modes. In this work, we use deep reinforcement learning to design a universal set of error-robust quantum logic gates on a superconducting quantum computer, without requiring knowledge of a specific Hamiltonian model of the system, its controls, or its underlying error processes. We experimentally demonstrate that a fully autonomous deep reinforcement learning agent can design single qubit gates up to 3× faster than default DRAG operations without additional leakage error, and exhibiting robustness against calibration drifts over weeks. We then show that ZX(−π/2) operations implemented using the cross-resonance interaction can outperform hardware default gates by over 2× and equivalently exhibit superior calibration-free performance up to 25 days post optimization using various metrics. We benchmark the performance of deep reinforcement learning derived gates against other black box optimization techniques, showing that deep reinforcement learning can achieve comparable or marginally superior performance, even with limited hardware access.

show abstract

Section: Optimized Quantum Logic Design With Deep Reinforcement Learningmentioning

confidence: 99%

Experimental Deep Reinforcement Learning for Error-Robust Gate-Set Design on a Superconducting Quantum Computer

et al. 2021

View full text Add to dashboard Cite

show abstract

“…This process of circuit transformation by a compiler routine for the target hardware is known as qubit routing (Cowtan et al 2019). The output instructions in the transformed quantum circuit should follow the connectivity constraints and essentially result in the same overall unitary evolution as the original circuit (Pozzi et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…In the context of NISQ hardware, this procedure is of extreme importance as the transformed circuit will, in general, have higher depth due to the insertion of extra SWAP gates. This overhead in the circuit depth becomes more prominent due to the high decoherence rates of the qubits and it becomes essential to find the most optimal and efficient strategy to minimize it (Cowtan et al 2019;Herbert and Sengupta 2018;Pozzi et al 2020). In this article, we present a procedure that we refer to as QRoute.…”

Section: Introductionmentioning

confidence: 99%

Qubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search

Sinha

Azad

Singh

2022

AAAI

View full text Add to dashboard Cite

Near-term quantum hardware can support two-qubit operations only on the qubits that can interact with each other. Therefore, to execute an arbitrary quantum circuit on the hardware, compilers have to first perform the task of qubit routing, i.e., to transform the quantum circuit either by inserting additional SWAP gates or by reversing existing CNOT gates to satisfy the connectivity constraints of the target topology. The depth of the transformed quantum circuits is minimized by utilizing the Monte Carlo tree search (MCTS) to perform qubit routing by making it both construct each action and search over the space of all actions. It is aided in performing these tasks by a Graph neural network that evaluates the value function and action probabilities for each state. Along with this, we propose a new method of adding mutex-lock like variables in our state representation which helps factor in the parallelization of the scheduled operations, thereby pruning the depth of the output circuit. Overall, our procedure (referred to as QRoute) performs qubit routing in a hardware agnostic manner, and it outperforms other available qubit routing implementations on various circuit benchmarks.

show abstract

“…In [23], machine learning is used to optimize the hyper-parameters of QCT algorithms, not being directly involved in the transformation process. Reinforcement learning is utilized in [24] to reduce the depth of the transformed quantum circuit. Different from these works, the proposed policy ANN can be embedded in many existing search-based QCT algorithms to enhance their performance, and the experimental results in Sec.…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning Enhanced Quantum Circuit Transformation

Zhou,

Feng,

2021

Preprint

View full text Add to dashboard Cite

Quantum circuit transformation (QCT) is required when executing a quantum program in a real quantum processing unit (QPU). Through inserting auxiliary SWAP gates, a QCT algorithm transforms a quantum circuit to one that satisfies the connectivity constraint imposed by the QPU. Due to the nonnegligible gate error and the limited qubit coherence time of the QPU, QCT algorithms which minimize gate number or circuit depth or maximize the fidelity of output circuits are in urgent need. Unfortunately, finding optimized transformations often involves deep and exhaustive search, which is extremely timeconsuming and not practical for circuits with medium to large depth. In this paper, we propose a framework which uses a policy artificial neural network (ANN) trained by supervised learning on shallow circuits to help existing QCT algorithms select the most promising SWAP. Very attractively, ANNs can be trained in a distributed way off-line. Meanwhile, the trained ANN can be easily incorporated into many QCT algorithms without bringing too much overhead in time complexity. Exemplary embeddings of the trained ANNs into target QCT algorithms demonstrate that the transformation performance can be consistently improved on QPUs with various connectivity structures and random quantum circuits.

show abstract

Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers

Cited by 8 publications

References 0 publications

Experimental Deep Reinforcement Learning for Error-Robust Gate-Set Design on a Superconducting Quantum Computer

Experimental Deep Reinforcement Learning for Error-Robust Gate-Set Design on a Superconducting Quantum Computer

Qubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search

Supervised Learning Enhanced Quantum Circuit Transformation

Contact Info

Product

Resources

About