Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning
- URL: http://arxiv.org/abs/2404.14865v3
- Date: Fri, 17 May 2024 11:23:02 GMT
- Title: Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning
- Authors: Sebastian Rietsch, Abhishek Y. Dubey, Christian Ufrecht, Maniraman Periyasamy, Axel Plinge, Christopher Mutschler, Daniel D. Scherer,
- Abstract summary: Unitary synthesis aims to identify a quantum circuit that represents a given unitary.
We apply the tree-search method Gumbel AlphaZero to solve the problem for a subset of exactly synthesizable Clifford+T unitaries.
Our inference times are around 30 seconds on a single GPU on average, surpassing state-of-the-art algorithms QuantumCircuitOpt and MIN-T-SYNTH for higher qubit numbers.
- Score: 2.4646794072984477
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a given unitary while minimizing circuit depth, total gate count, a specific gate count, or a combination of these factors. While past research has focused predominantly on continuous gate sets, synthesizing unitaries from the parameter-free Clifford+T gate set remains a challenge. Although the time complexity of this task will inevitably remain exponential in the number of qubits for general unitaries, reducing the runtime for simple problem instances still poses a significant challenge. In this study, we apply the tree-search method Gumbel AlphaZero to solve the problem for a subset of exactly synthesizable Clifford+T unitaries. Our approach can synthesize unitaries for up to five qubits generated from the set of randomized quantum circuits with up to 60 gates. Furthermore, our inference times are around 30 seconds on a single GPU on average, surpassing state-of-the-art algorithms QuantumCircuitOpt and MIN-T-SYNTH for higher qubit numbers. Our work provides a competitive baseline for synthesis algorithms to be developed in the upcoming years.
Related papers
- Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
We develop a reinforcement learning-based quantum compiler for a superconducting processor.
We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths.
Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
arXiv Detail & Related papers (2024-06-18T01:49:48Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Improving Quantum Circuit Synthesis with Machine Learning [0.7894596908025954]
We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms.
This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries.
arXiv Detail & Related papers (2023-06-09T01:53:56Z) - Partitioning Quantum Chemistry Simulations with Clifford Circuits [1.0286890995028481]
Current quantum computing hardware is restricted by the availability of only few, noisy qubits.
We investigate the limits of their classical and near-classical treatment while staying within the framework of quantum circuits.
arXiv Detail & Related papers (2023-03-02T13:05:19Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - A single $T$-gate makes distribution learning hard [56.045224655472865]
This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits.
We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
arXiv Detail & Related papers (2022-07-07T08:04:15Z) - Algebraic Compression of Quantum Circuits for Hamiltonian Evolution [52.77024349608834]
Unitary evolution under a time dependent Hamiltonian is a key component of simulation on quantum hardware.
We present an algorithm that compresses the Trotter steps into a single block of quantum gates.
This results in a fixed depth time evolution for certain classes of Hamiltonians.
arXiv Detail & Related papers (2021-08-06T19:38:01Z) - Clifford Circuit Optimization with Templates and Symbolic Pauli Gates [11.978356827088595]
The Clifford group is a finite subgroup of the unitary group generated by the Hadamard, the CNOT, and the Phase gates.
Here we consider the problem of finding a short quantum circuit implementing a given Clifford group element.
Our methods aim to minimize the entangling gate count assuming all-to-all qubit connectivity.
arXiv Detail & Related papers (2021-05-05T19:18:35Z) - Efficient CNOT Synthesis for NISQ Devices [1.0152838128195467]
In the era of noisy intermediate-scale quantum (NISQ), executing quantum algorithms on actual quantum devices faces unique challenges.
We propose a CNOT synthesis method called the token reduction method to solve this problem.
Our algorithm consistently outperforms the best publicly accessible algorithm for all of the tested quantum architectures.
arXiv Detail & Related papers (2020-11-12T15:13:32Z) - A Generic Compilation Strategy for the Unitary Coupled Cluster Ansatz [68.8204255655161]
We describe a compilation strategy for Variational Quantum Eigensolver (VQE) algorithms.
We use the Unitary Coupled Cluster (UCC) ansatz to reduce circuit depth and gate count.
arXiv Detail & Related papers (2020-07-20T22:26:16Z) - Topological Quantum Compiling with Reinforcement Learning [7.741584909637626]
We introduce an efficient algorithm that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set.
Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.
arXiv Detail & Related papers (2020-04-09T18:00:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.