QUEST: systematically approximating Quantum circuits for higher output fidelity

Patel, Tirthak; Younis, Ed; Iancu, Costin; Jong, Wibe A. de; Tiwari, Devesh

doi:10.1145/3503222.3507739

Cited by 27 publications

(6 citation statements)

References 31 publications

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“…For larger problems where an (impractically deep) circuit implementation is already known, the number of gates can often be reduced by partitioning the structure into smaller sub-problems. These sub-circuits can then be compiled individually, either with high precision [28] or only approximately [29].…”

Section: Introductionmentioning

confidence: 99%

Exploring ab initio machine synthesis of quantum circuits

2023

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Gate-level quantum circuits are often derived manually from higher level algorithms. While this suffices for small implementations and demonstrations, ultimately automatic circuit design will be required to realise complex algorithms using hardware-specific operations and connectivity. Therefore, ab initio creation of circuits within a machine, either a classical computer or a hybrid quantum-classical device, is of key importance. We explore a range of established and novel techniques for the synthesis of new circuit structures, the optimisation of parameterised circuits, and the efficient removal of low-value gates via the quantum geometric tensor. Using these techniques we tackle the tasks of automatic encoding of unitary processes and translation (recompilation) of a circuit from one form to another. Using emulated quantum computers with various noise-free gate sets we provide simple examples involving up to 10 qubits, corresponding to 20 qubits in the augmented space we use. Further applications of specific relevance to chemistry modelling are considered in a sister paper, 'Exploiting subspace constraints and ab initio variational methods for quantum chemistry'.

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Section: Introductionmentioning

confidence: 99%

Exploring ab initio machine synthesis of quantum circuits

2023

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“…Finally, it is worth mentioning that both here and in Ref. [35], as well as numerous other papers on quantum process learning [4,7,13,[45][46][47], it is tacitly assumed that we are interested in learning the output of a quantum process over randomly chosen input states (or equivalently we are interested in maximizing the Hilbert-Schmidt inner product). However, in practise one may well be interested in learning to predict the output of a quantum process for a given set of physically interesting input states [15].…”

Section: Discussionmentioning

confidence: 99%

The power and limitations of learning quantum dynamics incoherently

Jerbi¹,

Gibbs²,

Rudolph³

et al. 2023

Preprint

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Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share quantum information, less attention has been paid to whether the dynamics of quantum systems can be learned without the system and target directly interacting. Such incoherent frameworks are practically appealing since they open up methods of transpiling quantum processes between the different physical platforms without the need for technically challenging hybrid entanglement schemes. Here we provide bounds on the sample complexity of learning unitary processes incoherently by analyzing the number of measurements that are required to emulate well-established coherent learning strategies. We prove that if arbitrary measurements are allowed, then any efficiently representable unitary can be efficiently learned within the incoherent framework; however, when restricted to shallow-depth measurements only low-entangling unitaries can be learned. We demonstrate our incoherent learning algorithm for low entangling unitaries by successfully learning a 16-qubit unitary on ibmq kolkata, and further demonstrate the scalabilty of our proposed algorithm through extensive numerical experiments.

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“…In fact, the effect of noise is often reduced by CNOT gate count reduction (see, e.g., [20]). In addition, the effect of crosstalk could also be mitigated through commutativitybased instruction reordering after qubit mapping [29].…”

Section: Hardware Noises and Fidelitymentioning

confidence: 99%

On constructing benchmark quantum circuits with known near-optimal transformation cost

Li¹,

Zhou²,

Feng³

2023

Preprint

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Current quantum devices impose strict connectivity constraints on quantum circuits, making circuit transformation necessary before running logical circuits on real quantum devices. Many quantum circuit transformation (QCT) algorithms have been proposed in the past several years. This paper proposes a novel method for constructing benchmark circuits and uses these benchmark circuits to evaluate state-of-the-art QCT algorithms, including t|ket from Cambridge Quantum Computing, Qiskit from IBM, and three academic algorithms SABRE, SAHS, and MCTS. These benchmarks have known near-optimal transformation costs and thus are called QUEKNO (for quantum examples with known near-optimality). Compared with QUEKO benchmarks designed by Tan and Cong ( 2021), which all have zero optimal transformation costs, QUEKNO benchmarks are more general and can provide a more faithful evaluation for QCT algorithms (like t|ket ) which use subgraph isomorphism to find the initial mapping. Our evaluation results show that SABRE can generate transformations with conspicuously low average costs on the 53-qubit IBM Q Rochester and Google's Sycamore in both gate size and depth objectives.

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QUEST: systematically approximating Quantum circuits for higher output fidelity

Cited by 27 publications

References 31 publications

Exploring ab initio machine synthesis of quantum circuits

Exploring ab initio machine synthesis of quantum circuits

The power and limitations of learning quantum dynamics incoherently

On constructing benchmark quantum circuits with known near-optimal transformation cost

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